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ftopal
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huggingface-models-embeddings

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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	aidonuts/catacombs-001-ep3	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T01:26:21+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8399 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.053 \| 1.0 \| 19000 \| 1.8399 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "results", "results": []}]}	null	washimneupane/results	[ "peft", "safetensors", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]	2024-02-12T01:43:15+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-gpt2 #license-mit #region-us	results ======= This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.8399 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 2 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.37.1 * Pytorch 2.1.2 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.1\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-gpt2 #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.1\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 34, 97, 4, 36 ]	[ "passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-gpt2 #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.1\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.1029907613992691, -0.02696954272687435, -0.0006874270620755851, 0.11813642084598541, 0.20102664828300476, 0.02728985622525215, 0.1392863392829895, 0.04972473904490471, -0.1256207525730133, 0.03234406188130379, 0.11188475042581558, 0.11312916874885559, -0.0009857002878561616, 0.143269345164299, -0.04320298880338669, -0.2518545985221863, 0.0013295753160491586, 0.007121999282389879, -0.05535177141427994, 0.13218271732330322, 0.09077142179012299, -0.14241307973861694, 0.06825952231884003, -0.029894284904003143, -0.2819095849990845, 0.047381818294525146, 0.04963931441307068, -0.03959254175424576, 0.14461497962474823, 0.0008024769485928118, 0.18570397794246674, 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null	null	transformers	# Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 6.5507588386535645 f1_macro: 0.007553613837545024 f1_micro: 0.04596774193548386 f1_weighted: 0.02342410713485415 precision_macro: 0.0064405619945614515 precision_micro: 0.04596774193548387 precision_weighted: 0.020352886732684962 recall_macro: 0.017759772448305164 recall_micro: 0.04596774193548387 recall_weighted: 0.04596774193548387 accuracy: 0.04596774193548387	{"tags": ["autotrain", "text-classification"], "datasets": ["poetry-author/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]}	text-classification	dvs/poetry-author	[ "transformers", "safetensors", "bert", "text-classification", "autotrain", "dataset:poetry-author/autotrain-data", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2024-02-12T01:58:02+00:00	[]	[]	TAGS #transformers #safetensors #bert #text-classification #autotrain #dataset-poetry-author/autotrain-data #autotrain_compatible #endpoints_compatible #has_space #region-us	# Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 6.5507588386535645 f1_macro: 0.007553613837545024 f1_micro: 0.04596774193548386 f1_weighted: 0.02342410713485415 precision_macro: 0.0064405619945614515 precision_micro: 0.04596774193548387 precision_weighted: 0.020352886732684962 recall_macro: 0.017759772448305164 recall_micro: 0.04596774193548387 recall_weighted: 0.04596774193548387 accuracy: 0.04596774193548387	[ "# Model Trained Using AutoTrain\n\n- Problem type: Text Classification", "## Validation Metrics\nloss: 6.5507588386535645\n\nf1_macro: 0.007553613837545024\n\nf1_micro: 0.04596774193548386\n\nf1_weighted: 0.02342410713485415\n\nprecision_macro: 0.0064405619945614515\n\nprecision_micro: 0.04596774193548387\n\nprecision_weighted: 0.020352886732684962\n\nrecall_macro: 0.017759772448305164\n\nrecall_micro: 0.04596774193548387\n\nrecall_weighted: 0.04596774193548387\n\naccuracy: 0.04596774193548387" ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #autotrain #dataset-poetry-author/autotrain-data #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# Model Trained Using AutoTrain\n\n- Problem type: Text Classification", "## Validation Metrics\nloss: 6.5507588386535645\n\nf1_macro: 0.007553613837545024\n\nf1_micro: 0.04596774193548386\n\nf1_weighted: 0.02342410713485415\n\nprecision_macro: 0.0064405619945614515\n\nprecision_micro: 0.04596774193548387\n\nprecision_weighted: 0.020352886732684962\n\nrecall_macro: 0.017759772448305164\n\nrecall_micro: 0.04596774193548387\n\nrecall_weighted: 0.04596774193548387\n\naccuracy: 0.04596774193548387" ]	[ 62, 16, 142 ]	[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #autotrain #dataset-poetry-author/autotrain-data #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Text Classification## Validation Metrics\nloss: 6.5507588386535645\n\nf1_macro: 0.007553613837545024\n\nf1_micro: 0.04596774193548386\n\nf1_weighted: 0.02342410713485415\n\nprecision_macro: 0.0064405619945614515\n\nprecision_micro: 0.04596774193548387\n\nprecision_weighted: 0.020352886732684962\n\nrecall_macro: 0.017759772448305164\n\nrecall_micro: 0.04596774193548387\n\nrecall_weighted: 0.04596774193548387\n\naccuracy: 0.04596774193548387" ]	[ -0.09750010818243027, 0.15423764288425446, -0.0019412196706980467, 0.1211213544011116, 0.11120200157165527, 0.04449460282921791, 0.11225597560405731, 0.13443079590797424, -0.040827106684446335, 0.13898852467536926, 0.15826453268527985, 0.08619018644094467, 0.048167433589696884, 0.12406855076551437, -0.13029268383979797, -0.1629021167755127, 0.012996481731534004, 0.0037906221114099026, 0.10601774603128433, 0.08864189684391022, 0.07684288173913956, -0.10356313735246658, 0.09476912766695023, -0.05263033136725426, -0.15665827691555023, 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null	null	transformers	Prompt format: ``` @@ Domanda: ... @@ Risposta: ... ```	{"library_name": "transformers", "tags": []}	text-generation	cassanof/maestrale-gazzetta-si1000	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T02:01:55+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Prompt format:	[]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 47 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.024438748136162758, -0.004013474564999342, -0.006808666978031397, -0.022386319935321808, 0.15806317329406738, -0.006393054965883493, 0.1504720002412796, 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null	null	transformers	# binarized-ingotrix-slerp-7b binarized-ingotrix-slerp-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) * [liminerity/Ingot-7b-slerp-7-forged](https://huggingface.co/liminerity/Ingot-7b-slerp-7-forged) ## 🧩 Configuration ```yaml slices: - sources: - model: eren23/dpo-binarized-NeuralTrix-7B layer_range: [0, 32] - model: liminerity/Ingot-7b-slerp-7-forged layer_range: [0, 32] merge_method: slerp base_model: eren23/dpo-binarized-NeuralTrix-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "liminerity/binarized-ingotrix-slerp-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "eren23/dpo-binarized-NeuralTrix-7B", "liminerity/Ingot-7b-slerp-7-forged"], "base_model": ["eren23/dpo-binarized-NeuralTrix-7B", "liminerity/Ingot-7b-slerp-7-forged"]}	text-generation	liminerity/binarized-ingotrix-slerp-7b	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "eren23/dpo-binarized-NeuralTrix-7B", "liminerity/Ingot-7b-slerp-7-forged", "base_model:eren23/dpo-binarized-NeuralTrix-7B", "base_model:liminerity/Ingot-7b-slerp-7-forged", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T02:02:55+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #eren23/dpo-binarized-NeuralTrix-7B #liminerity/Ingot-7b-slerp-7-forged #base_model-eren23/dpo-binarized-NeuralTrix-7B #base_model-liminerity/Ingot-7b-slerp-7-forged #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# binarized-ingotrix-slerp-7b binarized-ingotrix-slerp-7b is a merge of the following models using LazyMergekit: * eren23/dpo-binarized-NeuralTrix-7B * liminerity/Ingot-7b-slerp-7-forged ## Configuration ## Usage	[ "# binarized-ingotrix-slerp-7b\n\nbinarized-ingotrix-slerp-7b is a merge of the following models using LazyMergekit:\n* eren23/dpo-binarized-NeuralTrix-7B\n* liminerity/Ingot-7b-slerp-7-forged", "## Configuration", "## Usage" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #eren23/dpo-binarized-NeuralTrix-7B #liminerity/Ingot-7b-slerp-7-forged #base_model-eren23/dpo-binarized-NeuralTrix-7B #base_model-liminerity/Ingot-7b-slerp-7-forged #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# binarized-ingotrix-slerp-7b\n\nbinarized-ingotrix-slerp-7b is a merge of the following models using LazyMergekit:\n* eren23/dpo-binarized-NeuralTrix-7B\n* liminerity/Ingot-7b-slerp-7-forged", "## Configuration", "## Usage" ]	[ 142, 71, 4, 3 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #eren23/dpo-binarized-NeuralTrix-7B #liminerity/Ingot-7b-slerp-7-forged #base_model-eren23/dpo-binarized-NeuralTrix-7B #base_model-liminerity/Ingot-7b-slerp-7-forged #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# binarized-ingotrix-slerp-7b\n\nbinarized-ingotrix-slerp-7b is a merge of the following models using LazyMergekit:\n* eren23/dpo-binarized-NeuralTrix-7B\n* liminerity/Ingot-7b-slerp-7-forged## Configuration## Usage" ]	[ -0.029289884492754936, 0.017704714089632034, -0.006355880293995142, 0.001019426272250712, 0.04529014229774475, 0.05009351670742035, 0.14123094081878662, 0.11746729165315628, 0.016280993819236755, 0.05764629691839218, 0.052033353596925735, 0.13502375781536102, 0.023678366094827652, 0.12113724648952484, -0.05068659409880638, -0.20363882184028625, 0.08965353667736053, 0.035818759351968765, -0.026039663702249527, 0.06962629407644272, 0.09341102093458176, -0.04867697134613991, 0.06774527579545975, -0.005025386344641447, -0.13300099968910217, 0.0023214726243168116, -0.024475302547216415, -0.013414213433861732, 0.1031351238489151, 0.0810847356915474, 0.09705404937267303, 0.02793559990823269, -0.013443520292639732, 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null	null	transformers	# Malaysian TinyLlama + siglip-large-patch16-384 WanDB https://wandb.ai/huseinzol05/vision-tinyllama?workspace=user-huseinzol05 ## how-to ```python from modeling_vision import MM_LLMs, MM_LLMs_Config from transformers import AutoTokenizer, AutoProcessor from PIL import Image import requests model = MM_LLMs.from_pretrained( 'mesolitica/malaysian-tinyllama-1.1b-siglip-large-384-vision', flash_attention = True, dtype = torch.bfloat16, torch_dtype = torch.bfloat16 ) _ = model.cuda() image_processor = AutoProcessor.from_pretrained('google/siglip-large-patch16-384') tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-tinyllama-1.1b-siglip-large-384-vision') def prepare_dataset(messages, images: List[str] = None): if images is not None: images = [Image.open(f).convert('RGB') for f in images] image_output = image_processor(images=images, return_tensors='pt')['pixel_values'] else: image_output = None prompt = tokenizer.apply_chat_template(messages, tokenize = False) outputs = tokenizer( prompt, return_tensors='pt', return_overflowing_tokens=False, return_length=False) outputs['images'] = image_output outputs['image_index'] = torch.tensor([0] * len(outputs['images'])) outputs['image_starts'] = torch.tensor([tokenizer.convert_tokens_to_ids('<image>')] * len(outputs['images'])) return outputs with open('Persian-cat-breed.jpg', 'wb') as fopen: fopen.write(requests.get('https://cdn.beautifulnara.net/wp-content/uploads/2017/12/10201620/Persian-cat-breed.jpg').content) with open('nasi-goreng-1-23.jpg', 'wb') as fopen: fopen.write(requests.get('https://www.jocooks.com/wp-content/uploads/2023/09/nasi-goreng-1-23.jpg').content) messages = [ {'role': 'user', 'content': '<image> </image> ini gambar apa'}, ] outputs = prepare_dataset(messages, images = ['Persian-cat-breed.jpg']) outputs['images'] = outputs['images'].type(model.dtype) for k in outputs.keys(): if outputs[k] is not None: outputs[k] = outputs[k].cuda() with torch.no_grad(): model_inputs = model.prepare_inputs_for_generation(outputs) r = model_inputs.pop('input_ids', None) generate_kwargs = dict( model_inputs, max_new_tokens=300, top_p=0.95, top_k=50, temperature=0.1, do_sample=True, num_beams=1, ) r = model.llm.generate(generate_kwargs) print(tokenizer.decode(r[0])) ``` ``` <s>Imej itu menunjukkan seekor kucing putih yang comel duduk di atas sofa hitam.</s> ``` ```python messages = [ {'role': 'user', 'content': '<image> </image> <image> </image> apa kaitan 2 gambar ni'}, ] outputs = prepare_dataset(messages, images = ['Persian-cat-breed.jpg', 'nasi-goreng-1-23.jpg']) outputs['images'] = outputs['images'].type(model.dtype) for k in outputs.keys(): if outputs[k] is not None: outputs[k] = outputs[k].cuda() with torch.no_grad(): model_inputs = model.prepare_inputs_for_generation(outputs) r = model_inputs.pop('input_ids', None) generate_kwargs = dict( model_inputs, max_new_tokens=300, top_p=0.95, top_k=50, temperature=0.1, do_sample=True, num_beams=1, ) r = model.llm.generate(generate_kwargs) print(tokenizer.decode(r[0])) ``` ``` <s>Tiada hubungan yang jelas antara gambar 1 (anak kucing putih duduk di atas sofa) dan gambar 2 (foto penutup mangkuk mi telur dengan nasi dan cili). Gambar pertama ialah imej haiwan, manakala gambar kedua ialah imej makanan. Mereka tergolong dalam kategori yang berbeza dan tidak mempunyai hubungan antara satu sama lain.</s> ```	{"library_name": "transformers", "tags": []}	feature-extraction	mesolitica/malaysian-tinyllama-1.1b-siglip-large-384-vision	[ "transformers", "safetensors", "mm_llms", "feature-extraction", "custom_code", "region:us" ]	2024-02-12T02:11:22+00:00	[]	[]	TAGS #transformers #safetensors #mm_llms #feature-extraction #custom_code #region-us	# Malaysian TinyLlama + siglip-large-patch16-384 WanDB URL ## how-to	[ "# Malaysian TinyLlama + siglip-large-patch16-384\n\nWanDB URL", "## how-to" ]	[ "TAGS\n#transformers #safetensors #mm_llms #feature-extraction #custom_code #region-us \n", "# Malaysian TinyLlama + siglip-large-patch16-384\n\nWanDB URL", "## how-to" ]	[ 30, 22, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mm_llms #feature-extraction #custom_code #region-us \n# Malaysian TinyLlama + siglip-large-patch16-384\n\nWanDB URL## how-to" ]	[ -0.061664558947086334, -0.10801823437213898, 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null	null	mlx	# mlx-community/Qwen1.5-1.8B-Chat-4bit This model was converted to MLX format from [`Qwen/Qwen1.5-1.8B-Chat`](). Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen1.5-1.8B-Chat-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```	{"language": ["en"], "license": "other", "tags": ["chat", "mlx"], "license_name": "tongyi-qianwen-research", "license_link": "https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/blob/main/LICENSE", "pipeline_tag": "text-generation"}	text-generation	mlx-community/Qwen1.5-1.8B-Chat-4bit	[ "mlx", "safetensors", "qwen2", "chat", "text-generation", "conversational", "en", "license:other", "region:us" ]	2024-02-12T02:14:53+00:00	[]	[ "en" ]	TAGS #mlx #safetensors #qwen2 #chat #text-generation #conversational #en #license-other #region-us	# mlx-community/Qwen1.5-1.8B-Chat-4bit This model was converted to MLX format from ['Qwen/Qwen1.5-1.8B-Chat'](). Refer to the original model card for more details on the model. ## Use with mlx	[ "# mlx-community/Qwen1.5-1.8B-Chat-4bit\nThis model was converted to MLX format from ['Qwen/Qwen1.5-1.8B-Chat']().\nRefer to the original model card for more details on the model.", "## Use with mlx" ]	[ "TAGS\n#mlx #safetensors #qwen2 #chat #text-generation #conversational #en #license-other #region-us \n", "# mlx-community/Qwen1.5-1.8B-Chat-4bit\nThis model was converted to MLX format from ['Qwen/Qwen1.5-1.8B-Chat']().\nRefer to the original model card for more details on the model.", "## Use with mlx" ]	[ 36, 58, 5 ]	[ "passage: TAGS\n#mlx #safetensors #qwen2 #chat #text-generation #conversational #en #license-other #region-us \n# mlx-community/Qwen1.5-1.8B-Chat-4bit\nThis model was converted to MLX format from ['Qwen/Qwen1.5-1.8B-Chat']().\nRefer to the original model card for more details on the model.## Use with mlx" ]	[ -0.06970985978841782, -0.05209539085626602, -0.000389641645597294, 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null	null	transformers	# StrangeMerges_21-7B-slerp StrangeMerges_21-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Gille/StrangeMerges_20-7B-slerp](https://huggingface.co/Gille/StrangeMerges_20-7B-slerp) * [Kukedlc/NeuTrixOmniBe-7B-model-remix](https://huggingface.co/Kukedlc/NeuTrixOmniBe-7B-model-remix) ## 🧩 Configuration ```yaml slices: - sources: - model: Gille/StrangeMerges_20-7B-slerp layer_range: [0, 32] - model: Kukedlc/NeuTrixOmniBe-7B-model-remix layer_range: [0, 32] merge_method: slerp base_model: Gille/StrangeMerges_20-7B-slerp parameters: t: - filter: self_attn value: [0.1, 0.3, 0.5, 0.7, 0.9] - filter: mlp value: [0.9, 0.7, 0.5, 0.3, 0.1] - value: 0.45 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Gille/StrangeMerges_21-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "Gille/StrangeMerges_20-7B-slerp", "Kukedlc/NeuTrixOmniBe-7B-model-remix"], "base_model": ["Gille/StrangeMerges_20-7B-slerp", "Kukedlc/NeuTrixOmniBe-7B-model-remix"]}	text-generation	Gille/StrangeMerges_21-7B-slerp	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Gille/StrangeMerges_20-7B-slerp", "Kukedlc/NeuTrixOmniBe-7B-model-remix", "base_model:Gille/StrangeMerges_20-7B-slerp", "base_model:Kukedlc/NeuTrixOmniBe-7B-model-remix", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T02:23:47+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #Gille/StrangeMerges_20-7B-slerp #Kukedlc/NeuTrixOmniBe-7B-model-remix #base_model-Gille/StrangeMerges_20-7B-slerp #base_model-Kukedlc/NeuTrixOmniBe-7B-model-remix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# StrangeMerges_21-7B-slerp StrangeMerges_21-7B-slerp is a merge of the following models using LazyMergekit: * Gille/StrangeMerges_20-7B-slerp * Kukedlc/NeuTrixOmniBe-7B-model-remix ## Configuration ## Usage	[ "# StrangeMerges_21-7B-slerp\n\nStrangeMerges_21-7B-slerp is a merge of the following models using LazyMergekit:\n* Gille/StrangeMerges_20-7B-slerp\n* Kukedlc/NeuTrixOmniBe-7B-model-remix", "## Configuration", "## Usage" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #Gille/StrangeMerges_20-7B-slerp #Kukedlc/NeuTrixOmniBe-7B-model-remix #base_model-Gille/StrangeMerges_20-7B-slerp #base_model-Kukedlc/NeuTrixOmniBe-7B-model-remix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# StrangeMerges_21-7B-slerp\n\nStrangeMerges_21-7B-slerp is a merge of the following models using LazyMergekit:\n* Gille/StrangeMerges_20-7B-slerp\n* Kukedlc/NeuTrixOmniBe-7B-model-remix", "## Configuration", "## Usage" ]	[ 144, 69, 4, 3 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #Gille/StrangeMerges_20-7B-slerp #Kukedlc/NeuTrixOmniBe-7B-model-remix #base_model-Gille/StrangeMerges_20-7B-slerp #base_model-Kukedlc/NeuTrixOmniBe-7B-model-remix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# StrangeMerges_21-7B-slerp\n\nStrangeMerges_21-7B-slerp is a merge of the following models using LazyMergekit:\n* Gille/StrangeMerges_20-7B-slerp\n* Kukedlc/NeuTrixOmniBe-7B-model-remix## Configuration## Usage" ]	[ -0.02329511195421219, -0.023322753608226776, -0.0057039447128772736, -0.004028772469609976, 0.05611785873770714, 0.05016573518514633, 0.14752647280693054, 0.09726645052433014, 0.06474711745977402, 0.07169953733682632, 0.04508993774652481, 0.13899151980876923, 0.0455438606441021, 0.11649376153945923, -0.047981955111026764, -0.20176950097084045, 0.0953531265258789, -0.010236851871013641, -0.025562936440110207, 0.10372339934110641, 0.10213194787502289, -0.0505632646381855, 0.11495768278837204, 0.010429141111671925, -0.08706065267324448, -0.004599154926836491, 0.02956310845911503, -0.02989981509745121, 0.09675775468349457, 0.08686058968305588, 0.058731455355882645, 0.0525791198015213, -0.04463787004351616, -0.16732312738895416, 0.03308786079287529, 0.009892350994050503, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	JJhooww/MistralReloadInstructionBR	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T02:27:19+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04571164771914482, 0.1637648642063141, -0.005522117950022221, 0.017756497487425804, 0.09821303188800812, 0.01318030059337616, 0.06541220843791962, 0.1127115860581398, -0.017605241388082504, 0.1127321794629097, 0.030432263389229774, 0.09820804744958878, 0.1134178638458252, 0.14702944457530975, -0.003594378475099802, -0.22472713887691498, 0.052083637565374374, -0.12124937027692795, -0.03241228312253952, 0.1181139275431633, 0.14941681921482086, -0.09871039539575577, 0.07234785705804825, -0.030714161694049835, -0.01334790326654911, -0.03167412802577019, -0.05947697162628174, -0.045681875199079514, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	tommymarto/LernnaviBERT_mcqbert1_students_answers_4096_mistral_seq_len_30	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T02:28:46+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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(2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 33, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05835729464888573, 0.21513818204402924, -0.0027643628418445587, 0.027697166427969933, 0.12558044493198395, -0.00036080856807529926, 0.038943830877542496, 0.12901438772678375, -0.01060954574495554, 0.1100858673453331, 0.03811120614409447, 0.09515609592199326, 0.09883695095777512, 0.1663336604833603, 0.04276633635163307, -0.21661408245563507, 0.003279293654486537, -0.08966897428035736, 0.019332116469740868, 0.10749275237321854, 0.13046206533908844, -0.10735081136226654, 0.07876921445131302, -0.03911958634853363, -0.01563864015042782, -0.002511978382244706, -0.09296175837516785, -0.07015316188335419, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lyfi-continue-classification This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.3.0.dev20240212 - Datasets 2.16.1 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-multilingual-cased", "model-index": [{"name": "lyfi-continue-classification", "results": []}]}	text-classification	MrHungry/lyfi-continue-classification	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T02:32:17+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-multilingual-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# lyfi-continue-classification This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.3.0.dev20240212 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "# lyfi-continue-classification\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.3.0.dev20240212\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-multilingual-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# lyfi-continue-classification\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.3.0.dev20240212\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]	[ 71, 37, 6, 12, 8, 3, 90, 4, 36 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-multilingual-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# lyfi-continue-classification\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.3.0.dev20240212\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]	[ 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ko-openhermes-open-llama-2-ko-7b This model is a fine-tuned version of [beomi/open-llama-2-ko-7b](https://huggingface.co/beomi/open-llama-2-ko-7b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00015 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 256 - total_train_batch_size: 1024 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.3.0.dev20240127+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "beomi/open-llama-2-ko-7b", "model-index": [{"name": "ko-openhermes-open-llama-2-ko-7b", "results": []}]}	text-generation	Unggi/ko-openhermes-open-llama-2-ko-7b	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:beomi/open-llama-2-ko-7b", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T02:47:52+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-beomi/open-llama-2-ko-7b #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# ko-openhermes-open-llama-2-ko-7b This model is a fine-tuned version of beomi/open-llama-2-ko-7b on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00015 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 256 - total_train_batch_size: 1024 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.3.0.dev20240127+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "# ko-openhermes-open-llama-2-ko-7b\n\nThis model is a fine-tuned version of beomi/open-llama-2-ko-7b on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0", "### Training results", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-beomi/open-llama-2-ko-7b #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ko-openhermes-open-llama-2-ko-7b\n\nThis model is a fine-tuned version of beomi/open-llama-2-ko-7b on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0", "### Training results", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ 76, 46, 6, 12, 8, 3, 157, 4, 43 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-beomi/open-llama-2-ko-7b #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ko-openhermes-open-llama-2-ko-7b\n\nThis model is a fine-tuned version of beomi/open-llama-2-ko-7b on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ -0.09749746322631836, 0.2040618509054184, -0.002782898722216487, 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null	null	null	# MoEv4Config-TestWeightedTIES-7b MoEv4Config-TestWeightedTIES-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/NeuTrixOmniBe-7B-model-remix](https://huggingface.co/Kukedlc/NeuTrixOmniBe-7B-model-remix) * [PetroGPT/WestSeverus-7B-DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO) * [vanillaOVO/supermario_v4](https://huggingface.co/vanillaOVO/supermario_v4) ## 🧩 Configuration ```yaml models: - model: Kukedlc/NeuTrixOmniBe-7B-model-remix # No parameters necessary for base model - model: Kukedlc/NeuTrixOmniBe-7B-model-remix parameters: density: [1, 0.7, 0.1] weight: [0, 0.3, 0.7, 1] - model: PetroGPT/WestSeverus-7B-DPO parameters: density: [1, 0.7, 0.3] weight: [0, 0.25, 0.5, 1] - model: vanillaOVO/supermario_v4 parameters: density: 0.33 weight: - filter: mlp value: 0.5 - value: 0 merge_method: ties base_model: Kukedlc/NeuTrixOmniBe-7B-model-remix parameters: int8_mask: true normalize: true sparsify: - filter: mlp value: 0.5 - filter: self_attn value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "jsfs11/MoEv4Config-TestWeightedTIES-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"tags": ["merge", "mergekit", "lazymergekit", "Kukedlc/NeuTrixOmniBe-7B-model-remix", "PetroGPT/WestSeverus-7B-DPO", "vanillaOVO/supermario_v4"], "base_model": ["Kukedlc/NeuTrixOmniBe-7B-model-remix", "PetroGPT/WestSeverus-7B-DPO", "vanillaOVO/supermario_v4"]}	null	jsfs11/MoEv4Config-TestWeightedTIES-7b-GGUF	[ "gguf", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuTrixOmniBe-7B-model-remix", "PetroGPT/WestSeverus-7B-DPO", "vanillaOVO/supermario_v4", "base_model:Kukedlc/NeuTrixOmniBe-7B-model-remix", "base_model:PetroGPT/WestSeverus-7B-DPO", "base_model:vanillaOVO/supermario_v4", "region:us" ]	2024-02-12T02:51:47+00:00	[]	[]	TAGS #gguf #merge #mergekit #lazymergekit #Kukedlc/NeuTrixOmniBe-7B-model-remix #PetroGPT/WestSeverus-7B-DPO #vanillaOVO/supermario_v4 #base_model-Kukedlc/NeuTrixOmniBe-7B-model-remix #base_model-PetroGPT/WestSeverus-7B-DPO #base_model-vanillaOVO/supermario_v4 #region-us	# MoEv4Config-TestWeightedTIES-7b MoEv4Config-TestWeightedTIES-7b is a merge of the following models using LazyMergekit: * Kukedlc/NeuTrixOmniBe-7B-model-remix * PetroGPT/WestSeverus-7B-DPO * vanillaOVO/supermario_v4 ## Configuration ## Usage	[ "# MoEv4Config-TestWeightedTIES-7b\n\nMoEv4Config-TestWeightedTIES-7b is a merge of the following models using LazyMergekit:\n* Kukedlc/NeuTrixOmniBe-7B-model-remix\n* PetroGPT/WestSeverus-7B-DPO\n* vanillaOVO/supermario_v4", "## Configuration", "## Usage" ]	[ "TAGS\n#gguf #merge #mergekit #lazymergekit #Kukedlc/NeuTrixOmniBe-7B-model-remix #PetroGPT/WestSeverus-7B-DPO #vanillaOVO/supermario_v4 #base_model-Kukedlc/NeuTrixOmniBe-7B-model-remix #base_model-PetroGPT/WestSeverus-7B-DPO #base_model-vanillaOVO/supermario_v4 #region-us \n", "# MoEv4Config-TestWeightedTIES-7b\n\nMoEv4Config-TestWeightedTIES-7b is a merge of the following models using LazyMergekit:\n* Kukedlc/NeuTrixOmniBe-7B-model-remix\n* PetroGPT/WestSeverus-7B-DPO\n* vanillaOVO/supermario_v4", "## Configuration", "## Usage" ]	[ 124, 85, 4, 3 ]	[ "passage: TAGS\n#gguf #merge #mergekit #lazymergekit #Kukedlc/NeuTrixOmniBe-7B-model-remix #PetroGPT/WestSeverus-7B-DPO #vanillaOVO/supermario_v4 #base_model-Kukedlc/NeuTrixOmniBe-7B-model-remix #base_model-PetroGPT/WestSeverus-7B-DPO #base_model-vanillaOVO/supermario_v4 #region-us \n# MoEv4Config-TestWeightedTIES-7b\n\nMoEv4Config-TestWeightedTIES-7b is a merge of the following models using LazyMergekit:\n* Kukedlc/NeuTrixOmniBe-7B-model-remix\n* PetroGPT/WestSeverus-7B-DPO\n* vanillaOVO/supermario_v4## Configuration## Usage" ]	[ -0.022633787244558334, -0.02283533848822117, -0.004476943984627724, -0.002329615643247962, 0.06642653793096542, 0.09252624958753586, 0.13918721675872803, 0.11643870174884796, 0.09605833888053894, 0.05138247832655907, 0.049824535846710205, 0.11030951142311096, 0.05317755788564682, 0.12802007794380188, 0.008414841257035732, -0.20449724793434143, 0.0680486410856247, 0.01947566494345665, -0.02341950498521328, 0.07103697210550308, 0.1165279969573021, -0.06568749248981476, 0.10246822983026505, 0.043948736041784286, -0.1734044998884201, 0.019361019134521484, -0.022106502205133438, 0.01141137070953846, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "google/flan-t5-xxl"}	null	MattBoraske/flan-t5-xxl-samsum-peft-adapter	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/flan-t5-xxl", "region:us" ]	2024-02-12T02:54:36+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-xxl #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-xxl #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 36, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-xxl #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.10498052090406418, 0.1956775039434433, -0.003209230024367571, 0.03674392029643059, 0.09446008503437042, 0.01811073161661625, 0.05514674261212349, 0.12348994612693787, -0.031557511538267136, 0.1103045791387558, 0.06901126354932785, 0.10099126398563385, 0.10352281481027603, 0.21338306367397308, 0.00816099438816309, -0.2037719190120697, 0.02561279758810997, -0.09573599696159363, -0.003965914715081453, 0.12485166639089584, 0.14845964312553406, -0.09812065958976746, 0.08149949461221695, -0.015534100122749805, -0.011056782677769661, -0.03537489101290703, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	automatic-speech-recognition	spsither/wav2vec2_run9.455	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T02:57:42+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 47, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06877388060092926, 0.1546701192855835, -0.0037609888240695, 0.013798683881759644, 0.11170210689306259, 0.0049477447755634785, 0.07622946053743362, 0.1076156347990036, -0.024175573140382767, 0.12644733488559723, 0.04164152219891548, 0.09870775043964386, 0.11074616760015488, 0.18980292975902557, 0.0015578214079141617, -0.20271944999694824, 0.06667982041835785, -0.11557482928037643, 0.02210802026093006, 0.12125445902347565, 0.14131462574005127, -0.10717527568340302, 0.06805222481489182, -0.03453851491212845, -0.022604284808039665, -0.03256304934620857, -0.06200181692838669, -0.0628168061375618, 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null	null	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # TaengooTV/my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0631 - Validation Loss: 0.2327 - Train Accuracy: 0.9338 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.2552 \| 0.1976 \| 0.9236 \| 0 \| \| 0.1324 \| 0.1892 \| 0.9307 \| 1 \| \| 0.0631 \| 0.2327 \| 0.9338 \| 2 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "TaengooTV/my_awesome_model", "results": []}]}	text-classification	TaengooTV/my_awesome_model	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T03:02:03+00:00	[]	[]	TAGS #transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	TaengooTV/my\_awesome\_model ============================ This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.0631 * Validation Loss: 0.2327 * Train Accuracy: 0.9338 * Epoch: 2 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 7810, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} * training\_precision: float32 ### Training results ### Framework versions * Transformers 4.35.2 * TensorFlow 2.15.0 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7810, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7810, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 70, 304, 4, 31 ]	[ "passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7810, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.07356255501508713, 0.07796312868595123, -0.008143098093569279, 0.07474439591169357, 0.13115376234054565, 0.05202529579401016, 0.08794788271188736, 0.1215585470199585, -0.05440857261419296, 0.151730477809906, 0.10217637568712234, 0.15660026669502258, 0.03900034725666046, 0.11834672093391418, -0.06877114623785019, -0.1427891105413437, 0.05389244481921196, -0.04477478191256523, -0.05187319964170456, 0.07480733841657639, 0.0667436346411705, -0.05339566245675087, 0.08141550421714783, -0.02915327623486519, -0.06218792870640755, -0.003492767224088311, 0.026791488751769066, -0.04138680920004845, 0.07272615283727646, 0.06714216619729996, 0.0395873598754406, 0.014313186518847942, -0.0008077847887761891, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	akkky02/llama2-fine-tuned-alpaca-1000	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T03:05:44+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 56, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06061961501836777, 0.15481999516487122, -0.004844071343541145, 0.02074851468205452, 0.0983177199959755, 0.007407687604427338, 0.07119518518447876, 0.11185134947299957, -0.023851769044995308, 0.1167980208992958, 0.031993988901376724, 0.09781743586063385, 0.11217817664146423, 0.16186554729938507, 0.0015333457849919796, -0.22897611558437347, 0.049678247421979904, -0.125278040766716, -0.0294334813952446, 0.11977242678403854, 0.1422213912010193, -0.10954539477825165, 0.0752737894654274, -0.038042325526475906, -0.005828251596540213, -0.0323176346719265, -0.06205610930919647, -0.05266609415411949, 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null	null	transformers	# CrystalMistral-24B CrystalMistral-24B is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) * [macadeliccc/WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo) * [Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp) * [cognitivecomputations/WestLake-7B-v2-laser](https://huggingface.co/cognitivecomputations/WestLake-7B-v2-laser) ## 🧩 Configuration ```yaml base_model: eren23/dpo-binarized-NeuralTrix-7B gate_mode: hidden dtype: bfloat16 experts: - source_model: eren23/dpo-binarized-NeuralTrix-7B positive_prompts: - "Generate a response to a given situation" - "Explain the concept of climate change" - source_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo positive_prompts: - "What is the capital of France?" - "Who wrote the novel 'Pride and Prejudice'?" - source_model: Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp positive_prompts: - "Write a short poem about spring" - "Design a logo for a tech startup called 'GreenLeaf'" - source_model: cognitivecomputations/WestLake-7B-v2-laser positive_prompts: - "Solve the equation x^2 + 3x - 10 = 0" - "Calculate the area of a circle with radius 5 units" ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "Crystalcareai/CrystalMistral-24B" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"license": "apache-2.0", "tags": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "eren23/dpo-binarized-NeuralTrix-7B", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo", "Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp", "cognitivecomputations/WestLake-7B-v2-laser"], "base_model": ["eren23/dpo-binarized-NeuralTrix-7B", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo", "Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp", "cognitivecomputations/WestLake-7B-v2-laser"]}	text-generation	Crystalcareai/CrystalMistral-24B	[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "eren23/dpo-binarized-NeuralTrix-7B", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo", "Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp", "cognitivecomputations/WestLake-7B-v2-laser", "base_model:eren23/dpo-binarized-NeuralTrix-7B", "base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo", "base_model:Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp", "base_model:cognitivecomputations/WestLake-7B-v2-laser", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T03:06:55+00:00	[]	[]	TAGS #transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #eren23/dpo-binarized-NeuralTrix-7B #macadeliccc/WestLake-7B-v2-laser-truthy-dpo #Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp #cognitivecomputations/WestLake-7B-v2-laser #base_model-eren23/dpo-binarized-NeuralTrix-7B #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #base_model-Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp #base_model-cognitivecomputations/WestLake-7B-v2-laser #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# CrystalMistral-24B CrystalMistral-24B is a Mixure of Experts (MoE) made with the following models using LazyMergekit: * eren23/dpo-binarized-NeuralTrix-7B * macadeliccc/WestLake-7B-v2-laser-truthy-dpo * Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp * cognitivecomputations/WestLake-7B-v2-laser ## Configuration ## Usage	[ "# CrystalMistral-24B\n\nCrystalMistral-24B is a Mixure of Experts (MoE) made with the following models using LazyMergekit:\n* eren23/dpo-binarized-NeuralTrix-7B\n* macadeliccc/WestLake-7B-v2-laser-truthy-dpo\n* Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp\n* cognitivecomputations/WestLake-7B-v2-laser", "## Configuration", "## Usage" ]	[ "TAGS\n#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #eren23/dpo-binarized-NeuralTrix-7B #macadeliccc/WestLake-7B-v2-laser-truthy-dpo #Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp #cognitivecomputations/WestLake-7B-v2-laser #base_model-eren23/dpo-binarized-NeuralTrix-7B #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #base_model-Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp #base_model-cognitivecomputations/WestLake-7B-v2-laser #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CrystalMistral-24B\n\nCrystalMistral-24B is a Mixure of Experts (MoE) made with the following models using LazyMergekit:\n* eren23/dpo-binarized-NeuralTrix-7B\n* macadeliccc/WestLake-7B-v2-laser-truthy-dpo\n* Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp\n* cognitivecomputations/WestLake-7B-v2-laser", "## Configuration", "## Usage" ]	[ 248, 112, 4, 3 ]	[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #eren23/dpo-binarized-NeuralTrix-7B #macadeliccc/WestLake-7B-v2-laser-truthy-dpo #Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp 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null	null	transformers	# OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy) Website and Demo: [https://openbuddy.ai](https://openbuddy.ai) Evaluation result of this model: [Evaluation.txt](Evaluation.txt) ![Demo](https://raw.githubusercontent.com/OpenBuddy/OpenBuddy/main/media/demo.png) # Copyright Notice Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 License: Apache 2.0 ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。	{"language": ["zh", "en", "fr", "de", "ja", "ko", "it", "ru"], "license": "apache-2.0", "library_name": "transformers", "pipeline_tag": "text-generation", "inference": false}	text-generation	OpenBuddy/openbuddy-mixtral-7bx8-v18.1-32k	[ "transformers", "safetensors", "mixtral", "text-generation", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	2024-02-12T03:07:34+00:00	[]	[ "zh", "en", "fr", "de", "ja", "ko", "it", "ru" ]	TAGS #transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us	# OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: URL Website and Demo: URL Evaluation result of this model: URL !Demo # Copyright Notice Base model: URL License: Apache 2.0 ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。	[ "# OpenBuddy - Open Multilingual Chatbot\n\nGitHub and Usage Guide: URL\n\nWebsite and Demo: URL\n\nEvaluation result of this model: URL\n\n!Demo", "# Copyright Notice\n\nBase model: URL\n\nLicense: Apache 2.0", "## Disclaimer\n\nAll OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.\n\nOpenBuddy is provided \"as-is\" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.\n\nBy using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.", "## 免责声明\n\n所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。\n\nOpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。\n\n使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。" ]	[ "TAGS\n#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n", "# OpenBuddy - Open Multilingual Chatbot\n\nGitHub and Usage Guide: URL\n\nWebsite and Demo: URL\n\nEvaluation result of this model: URL\n\n!Demo", "# Copyright Notice\n\nBase model: URL\n\nLicense: Apache 2.0", "## Disclaimer\n\nAll OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.\n\nOpenBuddy is provided \"as-is\" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.\n\nBy using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.", "## 免责声明\n\n所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。\n\nOpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。\n\n使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。" ]	[ 63, 35, 13, 298, 234 ]	[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n# OpenBuddy - Open Multilingual Chatbot\n\nGitHub and Usage Guide: URL\n\nWebsite and Demo: URL\n\nEvaluation result of this model: URL\n\n!Demo# Copyright Notice\n\nBase model: URL\n\nLicense: Apache 2.0## Disclaimer\n\nAll OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.\n\nOpenBuddy is provided \"as-is\" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.\n\nBy using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy." ]	[ -0.08544217795133591, -0.027712270617485046, -0.004752747248858213, 0.044435832649469376, 0.0470123253762722, -0.06760399788618088, 0.12859797477722168, 0.03612658753991127, 0.12981750071048737, 0.0047390395775437355, -0.014042103663086891, -0.0015371168265119195, -0.0004705979663413018, -0.030020128935575485, 0.035077054053545, -0.09343081712722778, 0.004317799117416143, -0.07658527791500092, 0.09031379967927933, -0.01956583745777607, 0.03498178720474243, 0.016850275918841362, 0.021398911252617836, 0.030199173837900162, 0.0253347996622324, -0.0565081425011158, 0.047160811722278595, 0.016137314960360527, 0.05486157163977623, 0.12993663549423218, -0.01899045705795288, -0.0066807153634727, -0.0712936520576477, -0.1733139008283615, 0.015914248302578926, 0.017978262156248093, -0.06352951377630234, 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null	null	null	# ESM-S ESM-S (https://arxiv.org/abs/2402.05856) is a series of structure-informed protein language models, which are trained on remote homology detection tasks for distilling structural information. The corresponding datasets can be downloaded at https://huggingface.co/datasets/Oxer11/Protein-Function-Annotation. The codebase can be found at https://github.com/DeepGraphLearning/esm-s. ![Training](./asset/training.png) # Evaluation Performance Freezing model weights and train a 2-layer MLP on downstream function prediction tasks. ![Predictor](./asset/predictor.png) Using ESM-S representations to retrieve similar proteins for function annotation. ![Retriever](./asset/retriever.png) # BibTeX ``` @article{zhang2024structureplm, title={Structure-Informed Protein Language Model}, author={Zhang, Zuobai and Lu, Jiarui and Chenthamarakshan, Vijil and Lozano, Aurelie and Das, Payel and Tang, Jian}, journal={arXiv preprint arXiv:2402.05856}, year={2024} } ```	{"language": ["en"], "license": "apache-2.0", "tags": ["Protein Langauge Model", "AI for Drug Discovery", "AI for Science"], "datasets": ["Oxer11/Protein-Function-Annotation"]}	null	Oxer11/ESM-S	[ "Protein Langauge Model", "AI for Drug Discovery", "AI for Science", "en", "dataset:Oxer11/Protein-Function-Annotation", "arxiv:2402.05856", "license:apache-2.0", "region:us" ]	2024-02-12T03:07:40+00:00	[ "2402.05856" ]	[ "en" ]	TAGS #Protein Langauge Model #AI for Drug Discovery #AI for Science #en #dataset-Oxer11/Protein-Function-Annotation #arxiv-2402.05856 #license-apache-2.0 #region-us	# ESM-S ESM-S (URL is a series of structure-informed protein language models, which are trained on remote homology detection tasks for distilling structural information. The corresponding datasets can be downloaded at URL The codebase can be found at URL !Training # Evaluation Performance Freezing model weights and train a 2-layer MLP on downstream function prediction tasks. !Predictor Using ESM-S representations to retrieve similar proteins for function annotation. !Retriever # BibTeX	[ "# ESM-S\n\nESM-S (URL is a series of structure-informed protein language models, which are trained on remote homology detection tasks for distilling structural information.\nThe corresponding datasets can be downloaded at URL\nThe codebase can be found at URL\n\n!Training", "# Evaluation Performance\n\nFreezing model weights and train a 2-layer MLP on downstream function prediction tasks.\n!Predictor\n\nUsing ESM-S representations to retrieve similar proteins for function annotation.\n!Retriever", "# BibTeX" ]	[ "TAGS\n#Protein Langauge Model #AI for Drug Discovery #AI for Science #en #dataset-Oxer11/Protein-Function-Annotation #arxiv-2402.05856 #license-apache-2.0 #region-us \n", "# ESM-S\n\nESM-S (URL is a series of structure-informed protein language models, which are trained on remote homology detection tasks for distilling structural information.\nThe corresponding datasets can be downloaded at URL\nThe codebase can be found at URL\n\n!Training", "# Evaluation Performance\n\nFreezing model weights and train a 2-layer MLP on downstream function prediction tasks.\n!Predictor\n\nUsing ESM-S representations to retrieve similar proteins for function annotation.\n!Retriever", "# BibTeX" ]	[ 60, 65, 54, 5 ]	[ "passage: TAGS\n#Protein Langauge Model #AI for Drug Discovery #AI for Science #en #dataset-Oxer11/Protein-Function-Annotation #arxiv-2402.05856 #license-apache-2.0 #region-us \n# ESM-S\n\nESM-S (URL is a series of structure-informed protein language models, which are trained on remote homology detection tasks for distilling structural information.\nThe corresponding datasets can be downloaded at URL\nThe codebase can be found at URL\n\n!Training# Evaluation Performance\n\nFreezing model weights and train a 2-layer MLP on downstream function prediction tasks.\n!Predictor\n\nUsing ESM-S representations to retrieve similar proteins for function annotation.\n!Retriever# BibTeX" ]	[ -0.02793359011411667, 0.018198519945144653, -0.0018791727488860488, 0.04358071833848953, 0.08673163503408432, -0.010862107388675213, 0.09112538397312164, 0.15750150382518768, 0.20775103569030762, 0.0010561656672507524, 0.05543556436896324, 0.062337324023246765, -0.007945692166686058, 0.19288752973079681, 0.04800751432776451, -0.0834711343050003, -0.0331789068877697, 0.016909247264266014, 0.07240499556064606, 0.029657529667019844, 0.06525196135044098, 0.008080694824457169, 0.17575593292713165, -0.007790352217853069, 0.006014103069901466, 0.0715014711022377, -0.029696697369217873, -0.029058020561933517, 0.10543359816074371, -0.015693424269557, -0.021504253149032593, -0.028415001928806305, 0.09036126732826233, -0.16764602065086365, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2412 - Accuracy: 0.55 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 20 \| 1.2182 \| 0.5625 \| \| No log \| 2.0 \| 40 \| 1.2392 \| 0.5312 \| \| No log \| 3.0 \| 60 \| 1.1474 \| 0.6 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "image_classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.55, "name": "Accuracy"}]}]}]}	image-classification	dewifaj/image_classification	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T03:10:32+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	image\_classification ===================== This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.2412 * Accuracy: 0.55 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 86, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.11736467480659485, 0.1389250010251999, -0.0026365139055997133, 0.12090291827917099, 0.14029932022094727, 0.00531614338979125, 0.1431695818901062, 0.13917332887649536, -0.07218696177005768, 0.07970607280731201, 0.14738447964191437, 0.1310456395149231, 0.031224915757775307, 0.18546482920646667, -0.04954014718532562, -0.22446352243423462, 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null	null	ml-agents	# poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Facepalm0/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀	{"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]}	reinforcement-learning	Facepalm0/poca-SoccerTwos	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	2024-02-12T03:11:57+00:00	[]	[]	TAGS #ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us	# poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A longer tutorial to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: Facepalm0/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play	[ "# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Facepalm0/poca-SoccerTwos\n 3. Step 2: Select your .nn /.onnx file\n 4. Click on Watch the agent play" ]	[ "TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n", "# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Facepalm0/poca-SoccerTwos\n 3. Step 2: Select your .nn /.onnx file\n 4. Click on Watch the agent play" ]	[ 52, 206 ]	[ "passage: TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Facepalm0/poca-SoccerTwos\n 3. Step 2: Select your .nn /.onnx file\n 4. 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null	null	transformers	this is [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b), dequantised from q5 to f16 && transposed to pytorch. shapes have been rotated less wrongly than in [alpindale/miqu-1-70b-pytorch](https://huggingface.co/alpindale/miqu-1-70b-pytorch/tree/main) usage ```python from transformers import LlamaForCausalLM as LLM, LlamaTokenizer as LT lt = LT.from_pretrained("NousResearch/Llama-2-7b-hf") t = lt("[INST] eloquent high camp prose about a cute catgirl [/INST]", return_tensors='pt').input_ids.cuda() llm = LLM.from_pretrained("152334H/miqu-1-70b-sf", device_map='auto') # note: you may need many gpus for this out = llm.generate(t, use_cache=False, max_new_tokens=200) print(lt.decode(out[0])) ``` result: ``` <s> [INST] eloquent high camp prose about a cute catgirl [/INST] In the resplendent realm of high camp, where irony and extravagance dance in a dazzling pas de deux, there exists a creature of such enchanting allure that she captivates the hearts and minds of all who behold her. This beguiling figure, a vision of feline grace and innocence, is none other than the inimitable catgirl. With her delicate features and winsome smile, she is the embodiment of a dream, a living testament to the power of imagination and the boundless possibilities of the human spirit. Her eyes, those twin orbs of sapphire fire, sparkle with a mischievous intelligence that belies her diminutive stature. They are windows into a soul that is at once ancient and eternally young, a soul that has traversed the vast expanse of time and space to find solace in the warm embrace of human companion ``` this roughly (but not entirely) matches the llama.cpp q5 result: ```bash $ ./main -ngl 99 -m ./miqu-q5 --color --temp 0.0 -n -1 -p '[INST] eloquent high camp prose about a cute catgirl [/INST]' ... [INST] eloquent high camp prose about a cute catgirl [/INST] In the resplendent realm of high camp, where irony and extravagance dance in a dazzling pas de deux, there exists a creature so enchantingly adorable that she captures the hearts of all who behold her. This is no ordinary feline, but rather a vision of elegance and whimsy combined: the cute catgirl. With her delicate features framed by an ethereal halo of pastel tresses, this darling diva prowls through life with the grace of a prima ballerina and the playfulness of a kitten. Her eyes, twin pools of sapphire or emerald, sparkle with mischief and intelligence as they survey their surroundings, ever alert for the next grand adventure or delightful prank. Her ensemble is a symphony of ruffles, bows, and lace, each detail painstakingly chosen to accentuate her lithe form and play up her feline charms. A frilly apron adorned with paw prints sways gently as she moves, while dainty ears perched atop her head twitch in response to every sound. Her gloved hands, so petite and perfect, seem made for holding teacups or sketching delicate portraits of her many admirers. But do not be fooled by her diminutive stature and sweet demeanor; beneath that fluffy exterior lies the heart of a lioness. Fiercely loyal and protective, she will stop at nothing to defend those she loves from harm. And when the situation calls for it, she can unleash a ferocious roar that belies her cute exterior. Indeed, the cute catgirl is a paradox wrapped in ruffles and ribbons, a living embodiment of the high camp aesthetic. She revels in the absurdity of her existence, finding joy in every outrageous situation and turning even the most mundane tasks into opportunities for sartorial expression. In her world, there is no such thing as too much glitter or too many bows; more is always more, and excess is a virtue to be celebrated. So let us raise our teacups in honor of this fabulous feline, this queen of camp who reminds us that life is too short for dull clothing and boring hairstyles. May we all strive to embody her spirit, embracing the absurdity of existence with open arms and a generous helping of glitter. Long live the cute catgirl! [end of text] ``` exl2 3.0bpw coming soon ![](https://thicc-af.mywaifulist.moe/waifus/miku-nakano-the-quintessential-quintuplets/phUEiEhPOL75GTDLncGy2dUbkDVMfYExZ2A1RBeQ.png?class=thumbnail)	{"license": "mit"}	text-generation	LoneStriker/miqu-1-70b-sf-5.5bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T03:14:33+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	this is miqu-1-70b, dequantised from q5 to f16 && transposed to pytorch. shapes have been rotated less wrongly than in alpindale/miqu-1-70b-pytorch usage result: this roughly (but not entirely) matches the URL q5 result: exl2 3.0bpw coming soon ![](URL	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 56 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0005691677215509117, 0.03026840090751648, -0.005779425613582134, -0.007680800743401051, 0.12393421679735184, -0.016670791432261467, 0.20057982206344604, 0.10256171226501465, 0.0027368133887648582, -0.025139721110463142, 0.14202554523944855, 0.2090049833059311, -0.02653181366622448, 0.03626692295074463, -0.12612687051296234, -0.17803461849689484, 0.08635426312685013, -0.0035030082799494267, 0.04833058640360832, 0.08384886384010315, 0.10781697928905487, -0.06203937530517578, 0.08011354506015778, -0.04676711559295654, -0.0940304547548294, 0.03246979042887688, 0.06822408735752106, -0.13098374009132385, 0.12263950705528259, 0.06911366432905197, 0.10126137733459473, 0.060711462050676346, -0.04248986020684242, 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null	null	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # TesterGG/act_classifier_final This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3843 - Validation Loss: 0.5093 - Train Accuracy: 0.8067 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9080, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.3844 \| 0.5093 \| 0.8067 \| 0 \| \| 0.3848 \| 0.5093 \| 0.8067 \| 1 \| \| 0.3847 \| 0.5093 \| 0.8067 \| 2 \| \| 0.3844 \| 0.5093 \| 0.8067 \| 3 \| \| 0.3845 \| 0.5093 \| 0.8067 \| 4 \| \| 0.3848 \| 0.5093 \| 0.8067 \| 5 \| \| 0.3845 \| 0.5093 \| 0.8067 \| 6 \| \| 0.3850 \| 0.5093 \| 0.8067 \| 7 \| \| 0.3854 \| 0.5093 \| 0.8067 \| 8 \| \| 0.3843 \| 0.5093 \| 0.8067 \| 9 \| ### Framework versions - Transformers 4.38.0.dev0 - TensorFlow 2.15.0 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "TesterGG/act_classifier_final", "results": []}]}	text-classification	TesterGG/act_classifier_final	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T03:23:59+00:00	[]	[]	TAGS #transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	TesterGG/act\_classifier\_final =============================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.3843 * Validation Loss: 0.5093 * Train Accuracy: 0.8067 * Epoch: 9 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 9080, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} * training\_precision: float32 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * TensorFlow 2.15.0 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9080, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9080, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 70, 304, 4, 36 ]	[ "passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9080, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.07692671567201614, 0.0904344767332077, -0.008471518754959106, 0.07199808210134506, 0.12091528624296188, 0.040895383805036545, 0.09466426074504852, 0.12591935694217682, -0.07624563574790955, 0.15538428723812103, 0.10621747374534607, 0.15169289708137512, 0.03773731365799904, 0.1340155005455017, -0.06663057953119278, -0.1410977691411972, 0.057468511164188385, -0.036617461591959, -0.04007822275161743, 0.07425708323717117, 0.06371963024139404, -0.06025809794664383, 0.0796893984079361, -0.02006233111023903, -0.06891927123069763, -0.007036484312266111, 0.02520032972097397, -0.04808812588453293, 0.06744665652513504, 0.05868879333138466, 0.03948954865336418, 0.016328172758221626, -0.005860956385731697, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	amoldwalunj/Mixtral-8x7B-Instruct-v0.1-legal_finetune_mixtral_32k-adapters	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T03:26:24+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	dhyay/phi-2_riddles-evolved	[ "transformers", "safetensors", "phi", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T03:27:02+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #phi #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #phi #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 55, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #phi #text-generation #conversational #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05746966227889061, 0.1385236531496048, -0.004867508076131344, 0.02071472257375717, 0.09852021187543869, 0.00767628476023674, 0.06745423376560211, 0.11078117787837982, -0.028323214501142502, 0.11590421944856644, 0.029915133491158485, 0.10265152901411057, 0.11196459829807281, 0.16344478726387024, -0.00402545602992177, -0.2235129326581955, 0.047733768820762634, -0.12416195124387741, -0.02545560710132122, 0.11989402025938034, 0.13988402485847473, -0.11031996458768845, 0.07310313731431961, -0.039893634617328644, -0.005319454241544008, -0.03720914199948311, -0.06028740480542183, -0.049471594393253326, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "mistralai/Mixtral-8x7B-Instruct-v0.1"}	null	amoldwalunj/Mixtral-8x7B-Instruct-v0.1-legal_finetune_mixtral_32k	[ "peft", "safetensors", "mixtral", "arxiv:1910.09700", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "region:us" ]	2024-02-12T03:27:49+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #mixtral #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #mixtral #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 48, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #mixtral #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.10702407360076904, 0.19515036046504974, -0.003600696101784706, 0.02575371041893959, 0.08191119134426117, 0.025189684703946114, 0.0726601630449295, 0.12312855571508408, 0.017255425453186035, 0.12818996608257294, 0.051774803549051285, 0.1013350710272789, 0.12023483216762543, 0.21305246651172638, -0.013325712643563747, -0.18745912611484528, 0.02529846876859665, -0.07468131929636002, 0.0019714077934622765, 0.12466075271368027, 0.14020724594593048, -0.09703809022903442, 0.07435043156147003, -0.021247318014502525, -0.0063484604470431805, -0.03294598311185837, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	tommymarto/LernnaviBERT_mcqbert3_students_answers_768_bert_seq_len_10	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T03:31:46+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 33, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05835729464888573, 0.21513818204402924, -0.0027643628418445587, 0.027697166427969933, 0.12558044493198395, -0.00036080856807529926, 0.038943830877542496, 0.12901438772678375, -0.01060954574495554, 0.1100858673453331, 0.03811120614409447, 0.09515609592199326, 0.09883695095777512, 0.1663336604833603, 0.04276633635163307, -0.21661408245563507, 0.003279293654486537, -0.08966897428035736, 0.019332116469740868, 0.10749275237321854, 0.13046206533908844, -0.10735081136226654, 0.07876921445131302, -0.03911958634853363, -0.01563864015042782, -0.002511978382244706, -0.09296175837516785, -0.07015316188335419, 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null	null	unity-sentis	# MusicGen music generation for Unity Sentis format This is the [Meta MusicGen](https://huggingface.co/spaces/facebook/MusicGen) model validated for Unity Sentis. This model creates up to 30 seconds of music in the style given by a text prompt. For example, you could use it to generate original music tracks for your games. ## How to run this model * Put the `sentis` and `json` files in the `Assets/StreamingAssets` folder * Install `com.unity.sentis` and `com.unity.nuget.newtonsoft-json` packages. * Put the c# script on the Main Camera * Put an AudioSource on the Main Camera * Change the prompt in the script file * Change the number of seconds in the script file * Press play and check for results in the console window ## Info This model is a bit slow at the moment. Please use our [discussion forum](https://discussions.unity.com/c/ai-beta/sentis) to discuss this model or suggest improvements to the code.	{"license": "mit", "library_name": "unity-sentis", "pipeline_tag": "text-to-audio"}	text-to-audio	unity/sentis-MusicGen	[ "unity-sentis", "text-to-audio", "license:mit", "region:us" ]	2024-02-12T03:35:21+00:00	[]	[]	TAGS #unity-sentis #text-to-audio #license-mit #region-us	# MusicGen music generation for Unity Sentis format This is the Meta MusicGen model validated for Unity Sentis. This model creates up to 30 seconds of music in the style given by a text prompt. For example, you could use it to generate original music tracks for your games. ## How to run this model * Put the 'sentis' and 'json' files in the 'Assets/StreamingAssets' folder * Install 'URL' and 'URL.newtonsoft-json' packages. * Put the c# script on the Main Camera * Put an AudioSource on the Main Camera * Change the prompt in the script file * Change the number of seconds in the script file * Press play and check for results in the console window ## Info This model is a bit slow at the moment. Please use our discussion forum to discuss this model or suggest improvements to the code.	[ "# MusicGen music generation for Unity Sentis format\nThis is the Meta MusicGen model validated for Unity Sentis. This model creates up to 30 seconds of music in the style given by a text prompt.\nFor example, you could use it to generate original music tracks for your games.", "## How to run this model\n* Put the 'sentis' and 'json' files in the 'Assets/StreamingAssets' folder\n* Install 'URL' and 'URL.newtonsoft-json' packages.\n* Put the c# script on the Main Camera\n* Put an AudioSource on the Main Camera\n* Change the prompt in the script file\n* Change the number of seconds in the script file\n* Press play and check for results in the console window", "## Info\nThis model is a bit slow at the moment. Please use our discussion forum to discuss this model or suggest improvements to the code." ]	[ "TAGS\n#unity-sentis #text-to-audio #license-mit #region-us \n", "# MusicGen music generation for Unity Sentis format\nThis is the Meta MusicGen model validated for Unity Sentis. This model creates up to 30 seconds of music in the style given by a text prompt.\nFor example, you could use it to generate original music tracks for your games.", "## How to run this model\n* Put the 'sentis' and 'json' files in the 'Assets/StreamingAssets' folder\n* Install 'URL' and 'URL.newtonsoft-json' packages.\n* Put the c# script on the Main Camera\n* Put an AudioSource on the Main Camera\n* Change the prompt in the script file\n* Change the number of seconds in the script file\n* Press play and check for results in the console window", "## Info\nThis model is a bit slow at the moment. Please use our discussion forum to discuss this model or suggest improvements to the code." ]	[ 24, 60, 100, 29 ]	[ "passage: TAGS\n#unity-sentis #text-to-audio #license-mit #region-us \n# MusicGen music generation for Unity Sentis format\nThis is the Meta MusicGen model validated for Unity Sentis. This model creates up to 30 seconds of music in the style given by a text prompt.\nFor example, you could use it to generate original music tracks for your games.## How to run this model\n* Put the 'sentis' and 'json' files in the 'Assets/StreamingAssets' folder\n* Install 'URL' and 'URL.newtonsoft-json' packages.\n* Put the c# script on the Main Camera\n* Put an AudioSource on the Main Camera\n* Change the prompt in the script file\n* Change the number of seconds in the script file\n* Press play and check for results in the console window## Info\nThis model is a bit slow at the moment. Please use our discussion forum to discuss this model or suggest improvements to the code." ]	[ -0.1901417374610901, 0.07944364845752716, -0.0002208729274570942, -0.05624154582619667, 0.1756003350019455, -0.037151675671339035, 0.10140002518892288, -0.009912305511534214, 0.053406305611133575, 0.08669047802686691, 0.032386671751737595, -0.059229470789432526, 0.0557255856692791, 0.11580579727888107, 0.009147606790065765, -0.1337578296661377, 0.11576615273952484, -0.05509742349386215, -0.029643738642334938, 0.06883740425109863, -0.029511593282222748, -0.06543939560651779, 0.07276025414466858, -0.030704060569405556, -0.13032543659210205, -0.0006901526357978582, -0.03963017836213112, 0.03755902126431465, -0.014064008370041847, -0.003281460842117667, -0.1148112565279007, 0.10754045844078064, -0.02215733751654625, 0.006641298532485962, 0.04928193986415863, 0.0410725362598896, 0.04521804675459862, -0.04143872112035751, 0.1329033076763153, 0.13688728213310242, 0.06163841113448143, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_gsm8k_dpo_0 This model is a fine-tuned version of [weijie210/mistral_gsm8k_sft](https://huggingface.co/weijie210/mistral_gsm8k_sft) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0108 - Rewards/chosen: -1.6999 - Rewards/rejected: -22.5187 - Rewards/accuracies: 0.9957 - Rewards/margins: 20.8188 - Logps/rejected: -238.0359 - Logps/chosen: -48.7549 - Logits/rejected: -2.6146 - Logits/chosen: -2.6687 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.36.1 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "weijie210/mistral_gsm8k_sft", "model-index": [{"name": "mistral_gsm8k_dpo_0", "results": []}]}	text-generation	weijie210/mistral_gsm8k_dpo_0	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:weijie210/mistral_gsm8k_sft", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T03:36:09+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #mistral #text-generation #trl #dpo #generated_from_trainer #conversational #base_model-weijie210/mistral_gsm8k_sft #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# mistral_gsm8k_dpo_0 This model is a fine-tuned version of weijie210/mistral_gsm8k_sft on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0108 - Rewards/chosen: -1.6999 - Rewards/rejected: -22.5187 - Rewards/accuracies: 0.9957 - Rewards/margins: 20.8188 - Logps/rejected: -238.0359 - Logps/chosen: -48.7549 - Logits/rejected: -2.6146 - Logits/chosen: -2.6687 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.36.1 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.0	[ "# mistral_gsm8k_dpo_0\n\nThis model is a fine-tuned version of weijie210/mistral_gsm8k_sft on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0108\n- Rewards/chosen: -1.6999\n- Rewards/rejected: -22.5187\n- Rewards/accuracies: 0.9957\n- Rewards/margins: 20.8188\n- Logps/rejected: -238.0359\n- Logps/chosen: -48.7549\n- Logits/rejected: -2.6146\n- Logits/chosen: -2.6687", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- total_train_batch_size: 32\n- total_eval_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- Transformers 4.36.1\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #trl #dpo #generated_from_trainer #conversational #base_model-weijie210/mistral_gsm8k_sft #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# mistral_gsm8k_dpo_0\n\nThis model is a fine-tuned version of weijie210/mistral_gsm8k_sft on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0108\n- Rewards/chosen: -1.6999\n- Rewards/rejected: -22.5187\n- Rewards/accuracies: 0.9957\n- Rewards/margins: 20.8188\n- Logps/rejected: -238.0359\n- Logps/chosen: -48.7549\n- Logits/rejected: -2.6146\n- Logits/chosen: -2.6687", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- total_train_batch_size: 32\n- total_eval_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- Transformers 4.36.1\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]	[ 95, 143, 6, 12, 8, 3, 147, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #trl #dpo #generated_from_trainer #conversational #base_model-weijie210/mistral_gsm8k_sft #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mistral_gsm8k_dpo_0\n\nThis model is a fine-tuned version of weijie210/mistral_gsm8k_sft on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0108\n- Rewards/chosen: -1.6999\n- Rewards/rejected: -22.5187\n- Rewards/accuracies: 0.9957\n- Rewards/margins: 20.8188\n- Logps/rejected: -238.0359\n- Logps/chosen: -48.7549\n- Logits/rejected: -2.6146\n- Logits/chosen: -2.6687## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- total_train_batch_size: 32\n- total_eval_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.36.1\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]	[ -0.08210108429193497, 0.09866846352815628, -0.004606945440173149, 0.0890996977686882, 0.10450105369091034, 0.040083952248096466, 0.03577927500009537, 0.1753559708595276, -0.08322692662477493, 0.10834044963121414, 0.04358670860528946, 0.08838038146495819, 0.06423231959342957, 0.14456668496131897, -0.04675937071442604, -0.14900578558444977, 0.027530623599886894, -0.07489341497421265, -0.02016732655465603, 0.0856541320681572, 0.09583438187837601, -0.06530008465051651, 0.058305248618125916, -0.017814897000789642, -0.09212294220924377, -0.006113023962825537, -0.012988355942070484, -0.023962870240211487, 0.05069731920957565, 0.005269487854093313, 0.06455405801534653, 0.00763564882799983, 0.06957210600376129, -0.23987823724746704, 0.0022812415845692158, 0.09076720476150513, 0.030844295397400856, 0.07707371562719345, 0.07798700779676437, -0.04018732160329819, 0.13517150282859802, 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null	null	transformers	# neuronal-7b-Mlab Neuronal-9b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/NeuralDaredevil-7B](https://huggingface.co/mlabonne/NeuralDaredevil-7B) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: mlabonne/NeuralDaredevil-7B layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: mlabonne/NeuralDaredevil-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/neuronal-7b-Mlab" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "mlabonne/NeuralDaredevil-7B", "mlabonne/NeuralHermes-2.5-Mistral-7B"], "base_model": ["mlabonne/NeuralDaredevil-7B", "mlabonne/NeuralHermes-2.5-Mistral-7B"]}	text-generation	Kukedlc/neuronal-7b-Mlab	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mlabonne/NeuralDaredevil-7B", "mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:mlabonne/NeuralDaredevil-7B", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T03:38:37+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/NeuralDaredevil-7B #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-mlabonne/NeuralDaredevil-7B #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# neuronal-7b-Mlab Neuronal-9b is a merge of the following models using LazyMergekit: * mlabonne/NeuralDaredevil-7B * mlabonne/NeuralHermes-2.5-Mistral-7B ## Configuration ## Usage	[ "# neuronal-7b-Mlab\n\nNeuronal-9b is a merge of the following models using LazyMergekit:\n* mlabonne/NeuralDaredevil-7B\n* mlabonne/NeuralHermes-2.5-Mistral-7B", "## Configuration", "## Usage" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/NeuralDaredevil-7B #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-mlabonne/NeuralDaredevil-7B #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# neuronal-7b-Mlab\n\nNeuronal-9b is a merge of the following models using LazyMergekit:\n* mlabonne/NeuralDaredevil-7B\n* mlabonne/NeuralHermes-2.5-Mistral-7B", "## Configuration", "## Usage" ]	[ 128, 54, 4, 3 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/NeuralDaredevil-7B #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-mlabonne/NeuralDaredevil-7B #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# neuronal-7b-Mlab\n\nNeuronal-9b is a merge of the following models using LazyMergekit:\n* mlabonne/NeuralDaredevil-7B\n* mlabonne/NeuralHermes-2.5-Mistral-7B## Configuration## Usage" ]	[ -0.031377602368593216, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # clean-gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.0958 - Accuracy: {'accuracy': 0.01026664402173913} - F1: {'f1': 0.00703878757390304} - Recall: {'recall': 0.01026664402173913} - Precision: {'precision': 0.005401831489934016} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "base_model": "gpt2", "model-index": [{"name": "clean-gpt2-wikitext2", "results": []}]}	text-generation	armaanp/clean-gpt2-wikitext2	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T03:39:41+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# clean-gpt2-wikitext2 This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.0958 - Accuracy: {'accuracy': 0.01026664402173913} - F1: {'f1': 0.00703878757390304} - Recall: {'recall': 0.01026664402173913} - Precision: {'precision': 0.005401831489934016} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	[ "# clean-gpt2-wikitext2\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.0958\n- Accuracy: {'accuracy': 0.01026664402173913}\n- F1: {'f1': 0.00703878757390304}\n- Recall: {'recall': 0.01026664402173913}\n- Precision: {'precision': 0.005401831489934016}", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# clean-gpt2-wikitext2\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.0958\n- Accuracy: {'accuracy': 0.01026664402173913}\n- F1: {'f1': 0.00703878757390304}\n- Recall: {'recall': 0.01026664402173913}\n- Precision: {'precision': 0.005401831489934016}", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ 72, 123, 6, 12, 8, 3, 90, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# clean-gpt2-wikitext2\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.0958\n- Accuracy: {'accuracy': 0.01026664402173913}\n- F1: {'f1': 0.00703878757390304}\n- Recall: {'recall': 0.01026664402173913}\n- Precision: {'precision': 0.005401831489934016}## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ -0.09885533899068832, 0.20685666799545288, -0.0022479756735265255, 0.10839846730232239, 0.13817259669303894, 0.021304063498973846, 0.043781179934740067, 0.16462461650371552, -0.09837473183870316, 0.12539266049861908, 0.08097296208143234, 0.034598592668771744, 0.09024438261985779, 0.14002789556980133, -0.02964700199663639, -0.1635046750307083, -0.01844572275876999, -0.028181148692965508, -0.010352789424359798, 0.09819573909044266, 0.11860989034175873, -0.08681334555149078, 0.06705345958471298, -0.036030061542987823, -0.14660848677158356, 0.015548721887171268, -0.0385090634226799, -0.04117476940155029, 0.07881094515323639, 0.027935726568102837, 0.050421688705682755, 0.007710079662501812, 0.09302651137113571, -0.24014799296855927, -0.013959080912172794, 0.0632258877158165, 0.021090812981128693, 0.07616914808750153, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	mou3az/AR_QG_Model	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T03:46:15+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	PsychicMoon/zephyr-everything-llm-superbowl-everything-500	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T03:51:26+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	# DarkForest 20B v1.1 ![image/png](https://huggingface.co/TeeZee/DarkForest-20B-v1.1/resolve/main/DarkForest_v1.1.jpg) ## Model Details - To create this model two step procedure was used. First a new 20B model was created using [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) and [KoboldAI/LLaMA2-13B-Erebus-v3](https://huggingface.co/KoboldAI/LLaMA2-13B-Erebus-v3) , deatils of the merge in [mergekit-config_step1.yml](https://huggingface.co/TeeZee/DarkForest-20B-v1.0/resolve/main/mergekit-config_step1.yml) - then [jebcarter/psyonic-cetacean-20B](https://huggingface.co/jebcarter/psyonic-cetacean-20B) was used to produce the final model, merge config in [mergekit-config-step2.yml](https://huggingface.co/TeeZee/DarkForest-20B-v1.1/resolve/main/mergekit-config-step2.yml) - instead of linear merge method used in v1.0, this time DARE TIES method was used for step2 - The resulting model has approximately 20 billion parameters. Warning: This model can produce NSFW content! ## Results - produces SFW nad NSFW content without issues, switches context seamlessly. - good at following instructions. - good at tracking multiple characters in one scene. - very creative, scenarios produced are mature and complicated, model doesn't shy from writing about PTSD, menatal issues or complicated relationships. - NSFW output is more creative and suprising than typical limaRP output. - definitely for mature audiences, not only because of vivid NSFW content but also because of overall maturity of stories it produces. - This is NOT Harry Potter level storytelling. All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel: <a href="https://www.buymeacoffee.com/TeeZee" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>	{"license": "other", "tags": ["merge", "not-for-all-audiences"], "license_name": "microsoft-research-license"}	text-generation	TeeZee/DarkForest-20B-v1.1	[ "transformers", "safetensors", "llama", "text-generation", "merge", "not-for-all-audiences", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T04:02:43+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #not-for-all-audiences #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# DarkForest 20B v1.1 !image/png ## Model Details - To create this model two step procedure was used. First a new 20B model was created using microsoft/Orca-2-13b and KoboldAI/LLaMA2-13B-Erebus-v3 , deatils of the merge in mergekit-config_step1.yml - then jebcarter/psyonic-cetacean-20B was used to produce the final model, merge config in URL - instead of linear merge method used in v1.0, this time DARE TIES method was used for step2 - The resulting model has approximately 20 billion parameters. Warning: This model can produce NSFW content! ## Results - produces SFW nad NSFW content without issues, switches context seamlessly. - good at following instructions. - good at tracking multiple characters in one scene. - very creative, scenarios produced are mature and complicated, model doesn't shy from writing about PTSD, menatal issues or complicated relationships. - NSFW output is more creative and suprising than typical limaRP output. - definitely for mature audiences, not only because of vivid NSFW content but also because of overall maturity of stories it produces. - This is NOT Harry Potter level storytelling. All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel: <a href="URL target="_blank"><img src="URL alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>	[ "# DarkForest 20B v1.1\n\n!image/png", "## Model Details\n\n- To create this model two step procedure was used. First a new 20B model was created using microsoft/Orca-2-13b\n and KoboldAI/LLaMA2-13B-Erebus-v3 , deatils of the merge in mergekit-config_step1.yml\n- then jebcarter/psyonic-cetacean-20B was used to produce the final model, merge config in URL\n- instead of linear merge method used in v1.0, this time DARE TIES method was used for step2\n- The resulting model has approximately 20 billion parameters.\n\nWarning: This model can produce NSFW content!", "## Results\n\n- produces SFW nad NSFW content without issues, switches context seamlessly.\n- good at following instructions.\n- good at tracking multiple characters in one scene.\n- very creative, scenarios produced are mature and complicated, model doesn't shy from writing about PTSD, menatal issues or complicated relationships.\n- NSFW output is more creative and suprising than typical limaRP output.\n- definitely for mature audiences, not only because of vivid NSFW content but also because of overall maturity of stories it produces.\n- This is NOT Harry Potter level storytelling.\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #not-for-all-audiences #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# DarkForest 20B v1.1\n\n!image/png", "## Model Details\n\n- To create this model two step procedure was used. First a new 20B model was created using microsoft/Orca-2-13b\n and KoboldAI/LLaMA2-13B-Erebus-v3 , deatils of the merge in mergekit-config_step1.yml\n- then jebcarter/psyonic-cetacean-20B was used to produce the final model, merge config in URL\n- instead of linear merge method used in v1.0, this time DARE TIES method was used for step2\n- The resulting model has approximately 20 billion parameters.\n\nWarning: This model can produce NSFW content!", "## Results\n\n- produces SFW nad NSFW content without issues, switches context seamlessly.\n- good at following instructions.\n- good at tracking multiple characters in one scene.\n- very creative, scenarios produced are mature and complicated, model doesn't shy from writing about PTSD, menatal issues or complicated relationships.\n- NSFW output is more creative and suprising than typical limaRP output.\n- definitely for mature audiences, not only because of vivid NSFW content but also because of overall maturity of stories it produces.\n- This is NOT Harry Potter level storytelling.\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>" ]	[ 64, 12, 142, 201 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #not-for-all-audiences #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# DarkForest 20B v1.1\n\n!image/png## Model Details\n\n- To create this model two step procedure was used. First a new 20B model was created using microsoft/Orca-2-13b\n and KoboldAI/LLaMA2-13B-Erebus-v3 , deatils of the merge in mergekit-config_step1.yml\n- then jebcarter/psyonic-cetacean-20B was used to produce the final model, merge config in URL\n- instead of linear merge method used in v1.0, this time DARE TIES method was used for step2\n- The resulting model has approximately 20 billion parameters.\n\nWarning: This model can produce NSFW content!## Results\n\n- produces SFW nad NSFW content without issues, switches context seamlessly.\n- good at following instructions.\n- good at tracking multiple characters in one scene.\n- very creative, scenarios produced are mature and complicated, model doesn't shy from writing about PTSD, menatal issues or complicated relationships.\n- NSFW output is more creative and suprising than typical limaRP output.\n- definitely for mature audiences, not only because of vivid NSFW content but also because of overall maturity of stories it produces.\n- 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null	null	transformers	## Sentiment Classifier: Emotion Detection with Sentence Transformer This model classifies sentiments using the Sentence Transformer, specifically the `all-MiniLM-L6-v2` architecture. It's trained on the `dair-ai/emotion` dataset to identify six basic emotions: * Sadness * Joy * Love * Anger * Fear * Surprise Developed by: shhossain: [https://github.com/shhossain](https://github.com/shhossain) Model type: [Custom](https://huggingface.co/shhossain/all-MiniLM-L6-v2-sentiment-classifier/blob/main/model.py) Model Size: __22.7M__ Language(s): English License: Same as all-MiniLM-L6-v2 Finetuned from: all-MiniLM-L6-v2 ([https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)) ## Usage Example ```python from transformers import pipeline pipe = pipeline("text-classification", model="shhossain/all-MiniLM-L6-v2-sentiment-classifier", trust_remote_code=True) result = pipe("This product is excellent!") result ``` Output: ```json [{'label': 'sad', 'score': 0.006396006792783737}, {'label': 'joy', 'score': 0.7897642254829407}, {'label': 'love', 'score': 0.17318710684776306}, {'label': 'anger', 'score': 0.008878232911229134}, {'label': 'fear', 'score': 0.010075093246996403}, {'label': 'surprise', 'score': 0.011699344962835312}] ```	{"language": ["en"], "license": "apache-2.0", "datasets": ["dair-ai/emotion"], "pipeline_tag": "text-classification"}	text-classification	shhossain/all-MiniLM-L6-v2-sentiment-classifier	[ "transformers", "pytorch", "safetensors", "SententenceTransformerSentimentClassifier", "text-classification", "custom_code", "en", "dataset:dair-ai/emotion", "license:apache-2.0", "autotrain_compatible", "region:us" ]	2024-02-12T04:03:42+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #safetensors #SententenceTransformerSentimentClassifier #text-classification #custom_code #en #dataset-dair-ai/emotion #license-apache-2.0 #autotrain_compatible #region-us	## Sentiment Classifier: Emotion Detection with Sentence Transformer This model classifies sentiments using the Sentence Transformer, specifically the 'all-MiniLM-L6-v2' architecture. It's trained on the 'dair-ai/emotion' dataset to identify six basic emotions: * Sadness * Joy * Love * Anger * Fear * Surprise Developed by: shhossain: URL Model type: Custom Model Size: __22.7M__ Language(s): English License: Same as all-MiniLM-L6-v2 Finetuned from: all-MiniLM-L6-v2 (URL ## Usage Example Output:	[ "## Sentiment Classifier: Emotion Detection with Sentence Transformer\n\nThis model classifies sentiments using the Sentence Transformer, specifically the 'all-MiniLM-L6-v2' architecture. It's trained on the 'dair-ai/emotion' dataset to identify six basic emotions:\n\n* Sadness\n* Joy\n* Love\n* Anger\n* Fear\n* Surprise\n\nDeveloped by: shhossain: URL\n\nModel type: Custom\n\nModel Size: __22.7M__\n\nLanguage(s): English\n\nLicense: Same as all-MiniLM-L6-v2\n\nFinetuned from: all-MiniLM-L6-v2 (URL", "## Usage Example\n\n\n\nOutput:" ]	[ "TAGS\n#transformers #pytorch #safetensors #SententenceTransformerSentimentClassifier #text-classification #custom_code #en #dataset-dair-ai/emotion #license-apache-2.0 #autotrain_compatible #region-us \n", "## Sentiment Classifier: Emotion Detection with Sentence Transformer\n\nThis model classifies sentiments using the Sentence Transformer, specifically the 'all-MiniLM-L6-v2' architecture. It's trained on the 'dair-ai/emotion' dataset to identify six basic emotions:\n\n* Sadness\n* Joy\n* Love\n* Anger\n* Fear\n* Surprise\n\nDeveloped by: shhossain: URL\n\nModel type: Custom\n\nModel Size: __22.7M__\n\nLanguage(s): English\n\nLicense: Same as all-MiniLM-L6-v2\n\nFinetuned from: all-MiniLM-L6-v2 (URL", "## Usage Example\n\n\n\nOutput:" ]	[ 68, 144, 8 ]	[ "passage: TAGS\n#transformers #pytorch #safetensors #SententenceTransformerSentimentClassifier #text-classification #custom_code #en #dataset-dair-ai/emotion #license-apache-2.0 #autotrain_compatible #region-us \n## Sentiment Classifier: Emotion Detection with Sentence Transformer\n\nThis model classifies sentiments using the Sentence Transformer, specifically the 'all-MiniLM-L6-v2' architecture. It's trained on the 'dair-ai/emotion' dataset to identify six basic emotions:\n\n* Sadness\n* Joy\n* Love\n* Anger\n* Fear\n* Surprise\n\nDeveloped by: shhossain: URL\n\nModel type: Custom\n\nModel Size: __22.7M__\n\nLanguage(s): English\n\nLicense: Same as all-MiniLM-L6-v2\n\nFinetuned from: all-MiniLM-L6-v2 (URL## Usage Example\n\n\n\nOutput:" ]	[ -0.04148450121283531, 0.011154533363878727, -0.005653622094541788, 0.10180837661027908, 0.10807807743549347, 0.025102956220507622, 0.08988605439662933, 0.08323623985052109, 0.05329068377614021, 0.013486655429005623, 0.09228159487247467, 0.12067524343729019, -0.04015177860856056, 0.03010479547083378, -0.060789383947849274, -0.23977623879909515, 0.032320741564035416, -0.034833066165447235, 0.10187229514122009, 0.10356109589338303, 0.12349255383014679, -0.0696609690785408, 0.10659398138523102, -0.006286717019975185, -0.11432812362909317, 0.028492163866758347, -0.006341587286442518, 0.016756312921643257, 0.08695854991674423, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-ko-med This model is a fine-tuned version of [shangrilar/techvillage_korean](https://huggingface.co/shangrilar/techvillage_korean) on the audiofolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "base_model": "shangrilar/techvillage_korean", "model-index": [{"name": "whisper-small-ko-med", "results": []}]}	automatic-speech-recognition	shangrilar/whisper-small-ko-med	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "base_model:shangrilar/techvillage_korean", "endpoints_compatible", "region:us" ]	2024-02-12T04:07:35+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #dataset-audiofolder #base_model-shangrilar/techvillage_korean #endpoints_compatible #region-us	# whisper-small-ko-med This model is a fine-tuned version of shangrilar/techvillage_korean on the audiofolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	[ "# whisper-small-ko-med\n\nThis model is a fine-tuned version of shangrilar/techvillage_korean on the audiofolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 32\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 4\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #dataset-audiofolder #base_model-shangrilar/techvillage_korean #endpoints_compatible #region-us \n", "# whisper-small-ko-med\n\nThis model is a fine-tuned version of shangrilar/techvillage_korean on the audiofolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 32\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 4\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ 70, 39, 6, 12, 8, 3, 141, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #dataset-audiofolder #base_model-shangrilar/techvillage_korean #endpoints_compatible #region-us \n# whisper-small-ko-med\n\nThis model is a fine-tuned version of shangrilar/techvillage_korean on the audiofolder dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 32\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 4\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ -0.07116463035345078, 0.089847631752491, -0.0012698782375082374, 0.054870616644620895, 0.15474732220172882, -0.012397167272865772, 0.10244505107402802, 0.09663055837154388, -0.06787914037704468, 0.08282101154327393, 0.0349828340113163, 0.019160784780979156, 0.08334007859230042, 0.11108889430761337, -0.0014952989295125008, -0.2928634583950043, 0.01418799813836813, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"license": "apache-2.0", "library_name": "peft", "base_model": "moreh/MoMo-72B-LoRA-V1.4"}	null	SF-Foundation/Ein-72B-v0.13	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:moreh/MoMo-72B-LoRA-V1.4", "license:apache-2.0", "region:us" ]	2024-02-12T04:09:33+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-moreh/MoMo-72B-LoRA-V1.4 #license-apache-2.0 #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-moreh/MoMo-72B-LoRA-V1.4 #license-apache-2.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 50, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-moreh/MoMo-72B-LoRA-V1.4 #license-apache-2.0 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.09911065548658371, 0.20745967328548431, -0.0032535549253225327, 0.024645637720823288, 0.08754356950521469, 0.01775827817618847, 0.0650545284152031, 0.12584681808948517, 0.0062500168569386005, 0.13013756275177002, 0.05281613767147064, 0.10071409493684769, 0.12001646310091019, 0.23355494439601898, -0.001955639338120818, -0.19144156575202942, 0.027038192376494408, -0.09028986096382141, 0.007296456489712, 0.11803892254829407, 0.13820454478263855, -0.10086881369352341, 0.07396405935287476, -0.01900639571249485, 0.007498077116906643, -0.032375939190387726, 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null	null	stable-baselines3	# DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga enrique2701 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga enrique2701 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga enrique2701 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```	{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "686.00 +/- 221.37", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	enrique2701/dqn-SpaceInvadersNoFrameskip-v4	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-12T04:12:47+00:00	[]	[]	TAGS #stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: URL SB3: URL SB3 Contrib: URL Install the RL Zoo (with SB3 and SB3-Contrib): If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters # Environment Arguments	[ "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]	[ "TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]	[ 43, 90, 73, 9, 5, 7 ]	[ "passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments" ]	[ 0.043572068214416504, 0.2414778620004654, -0.0026879787910729647, 0.012635791674256325, 0.05784223601222038, 0.0030472534708678722, 0.08585051447153091, 0.10650663822889328, 0.024212315678596497, -0.001382096204906702, 0.003954293206334114, 0.17533031105995178, 0.03632635250687599, 0.13125447928905487, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cover_breakdown This model is a fine-tuned version of [markjvickers/cover_breakdown](https://huggingface.co/markjvickers/cover_breakdown) on the cover_breakdown_loader dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "datasets": ["cover_breakdown_loader"], "base_model": "markjvickers/cover_breakdown", "model-index": [{"name": "cover_breakdown", "results": []}]}	object-detection	markjvickers/cover_breakdown	[ "transformers", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "dataset:cover_breakdown_loader", "base_model:markjvickers/cover_breakdown", "endpoints_compatible", "region:us" ]	2024-02-12T04:15:21+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #dataset-cover_breakdown_loader #base_model-markjvickers/cover_breakdown #endpoints_compatible #region-us	# cover_breakdown This model is a fine-tuned version of markjvickers/cover_breakdown on the cover_breakdown_loader dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	[ "# cover_breakdown\n\nThis model is a fine-tuned version of markjvickers/cover_breakdown on the cover_breakdown_loader dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 40\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #dataset-cover_breakdown_loader #base_model-markjvickers/cover_breakdown #endpoints_compatible #region-us \n", "# cover_breakdown\n\nThis model is a fine-tuned version of markjvickers/cover_breakdown on the cover_breakdown_loader dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 40\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ 66, 36, 6, 12, 8, 3, 103, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #dataset-cover_breakdown_loader #base_model-markjvickers/cover_breakdown #endpoints_compatible #region-us \n# cover_breakdown\n\nThis model is a fine-tuned version of markjvickers/cover_breakdown on the cover_breakdown_loader dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 40\n- mixed_precision_training: Native AMP### Training results### 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null	null	diffusers	# Hanayo Koizumi <Gallery /> ## Model description This model was trained to generate high quality images based on SIFAS cards. To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement. ## Trigger words You should use `id_hanayo_koizumi` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/theidoldaily/hanayo-koizumi/tree/main) them in the Files & versions tab.	{"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "masterpiece, high quality, defined pupil, looking at viewer, rounded pupil, defined iris, (soft iris:1.2),", "parameters": {"negative_prompt": "bad_anatomy, deformation, amputation, deformity, deformed_nipples, duplicated_torso, deformed_torso, long_torso, large_torso, unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2), unproportioned_eyes, unproportioned_head, small_head, duplicated_nose, big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy, red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,"}, "output": {"url": "images/hanayo_koizumi.png"}}], "base_model": "cagliostrolab/animagine-xl-3.0", "instance_prompt": "id_hanayo_koizumi"}	text-to-image	theidoldaily/hanayo-koizumi	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:cagliostrolab/animagine-xl-3.0", "region:us" ]	2024-02-12T04:18:06+00:00	[]	[]	TAGS #diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #region-us	# Hanayo Koizumi <Gallery /> ## Model description This model was trained to generate high quality images based on SIFAS cards. To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement. ## Trigger words You should use 'id_hanayo_koizumi' to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab.	[ "# Hanayo Koizumi\n\n<Gallery />", "## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.", "## Trigger words\n\nYou should use 'id_hanayo_koizumi' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #region-us \n", "# Hanayo Koizumi\n\n<Gallery />", "## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.", "## Trigger words\n\nYou should use 'id_hanayo_koizumi' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ 51, 11, 68, 23, 28 ]	[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #region-us \n# Hanayo Koizumi\n\n<Gallery />## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.## Trigger words\n\nYou should use 'id_hanayo_koizumi' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	adhisetiawan/phi2_DPO	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "has_space", "region:us" ]	2024-02-12T04:19:55+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #has_space #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #has_space #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 35, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #has_space #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05497773736715317, 0.21668316423892975, -0.0030925441533327103, 0.024449052289128304, 0.12036674469709396, -0.00028704447322525084, 0.0446685329079628, 0.1240360364317894, -0.027052097022533417, 0.11330267041921616, 0.03038755990564823, 0.08719956874847412, 0.1070711687207222, 0.1589939296245575, 0.02512749657034874, -0.21775513887405396, 0.019310859963297844, -0.09284795820713043, 0.012216622941195965, 0.11072329431772232, 0.13465526700019836, -0.10658586025238037, 0.07965877652168274, -0.027023594826459885, -0.010900286957621574, -0.004848485346883535, -0.0894252359867096, -0.07648330181837082, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	tommymarto/LernnaviBERT_mcqbert3_students_answers_4096_mistral_seq_len_30	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T04:21:26+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 33, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05835729464888573, 0.21513818204402924, -0.0027643628418445587, 0.027697166427969933, 0.12558044493198395, -0.00036080856807529926, 0.038943830877542496, 0.12901438772678375, -0.01060954574495554, 0.1100858673453331, 0.03811120614409447, 0.09515609592199326, 0.09883695095777512, 0.1663336604833603, 0.04276633635163307, -0.21661408245563507, 0.003279293654486537, -0.08966897428035736, 0.019332116469740868, 0.10749275237321854, 0.13046206533908844, -0.10735081136226654, 0.07876921445131302, -0.03911958634853363, -0.01563864015042782, -0.002511978382244706, -0.09296175837516785, -0.07015316188335419, 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null	null	diffusers	# Honoka Kosaka <Gallery /> ## Model description This model was trained to generate high quality images based on SIFAS cards. To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement. ## Trigger words You should use `id_honoka_kosaka` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/theidoldaily/honoka-kosaka/tree/main) them in the Files & versions tab.	{"license": "mit", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "masterpiece, high quality, defined pupil, looking at viewer, rounded pupil, defined iris, (soft iris:1.2),", "parameters": {"negative_prompt": "bad_anatomy, deformation, amputation, deformity, deformed_nipples, duplicated_torso, deformed_torso, long_torso, large_torso, unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2), unproportioned_eyes, unproportioned_head, small_head, duplicated_nose, big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy, red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,"}, "output": {"url": "images/honoka_kosaka.png"}}], "base_model": "cagliostrolab/animagine-xl-3.0", "instance_prompt": "id_honoka_kosaka"}	text-to-image	theidoldaily/honoka-kosaka	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:cagliostrolab/animagine-xl-3.0", "license:mit", "region:us" ]	2024-02-12T04:25:08+00:00	[]	[]	TAGS #diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us	# Honoka Kosaka <Gallery /> ## Model description This model was trained to generate high quality images based on SIFAS cards. To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement. ## Trigger words You should use 'id_honoka_kosaka' to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab.	[ "# Honoka Kosaka\n\n<Gallery />", "## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.", "## Trigger words\n\nYou should use 'id_honoka_kosaka' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us \n", "# Honoka Kosaka\n\n<Gallery />", "## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.", "## Trigger words\n\nYou should use 'id_honoka_kosaka' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ 56, 10, 68, 22, 28 ]	[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us \n# Honoka Kosaka\n\n<Gallery />## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.## Trigger words\n\nYou should use 'id_honoka_kosaka' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ -0.048268601298332214, -0.09939201921224594, -0.00017571639909874648, 0.032377999275922775, 0.12382838875055313, 0.03766173496842384, 0.2300116866827011, 0.05990692600607872, 0.1134786382317543, 0.05700482800602913, -0.0006100590107962489, 0.05766736716032028, 0.07405169308185577, 0.26003122329711914, -0.059641338884830475, -0.28167909383773804, 0.11556874960660934, -0.026327060535550117, -0.008609594777226448, 0.014871155843138695, 0.06657488644123077, -0.06030644103884697, 0.12185081839561462, -0.02669292688369751, -0.06376754492521286, 0.013909588567912579, 0.0005011933390051126, -0.027457457035779953, 0.0014464396517723799, 0.05529823154211044, 0.02246427722275257, 0.0717964768409729, 0.07554150372743607, -0.1520000845193863, 0.0653686672449112, 0.001454387092962861, -0.017488598823547363, -0.01762002892792225, -0.025325383991003036, -0.02368493191897869, 0.1405061036348343, -0.09653577953577042, -0.08693377673625946, -0.02741800993680954, -0.048762399703264236, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	B2111797/recipe_gener_v3	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T04:31:37+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 57, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05622259899973869, 0.16002345085144043, -0.004987028427422047, 0.023115945979952812, 0.0962471067905426, 0.011845538392663002, 0.06785304099321365, 0.11496778577566147, -0.020396295934915543, 0.11142492294311523, 0.03292480856180191, 0.0972127765417099, 0.11474913358688354, 0.16215258836746216, 0.004439093638211489, -0.23455148935317993, 0.04782992601394653, -0.12695099413394928, -0.033447545021772385, 0.11785799264907837, 0.14491069316864014, -0.10402194410562515, 0.07766910642385483, -0.030544815585017204, -0.009361269883811474, -0.03290390968322754, -0.06365230679512024, -0.05152205005288124, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "HuggingFaceH4/zephyr-7b-beta"}	null	PsychicMoon/zephyr-everything-llm-superbowl-nonconvo-220	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:HuggingFaceH4/zephyr-7b-beta", "region:us" ]	2024-02-12T04:34:07+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-HuggingFaceH4/zephyr-7b-beta #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-HuggingFaceH4/zephyr-7b-beta #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 40, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-HuggingFaceH4/zephyr-7b-beta #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.1229972392320633, 0.20960783958435059, -0.0027185017243027687, 0.03048955649137497, 0.08257313817739487, 0.021743744611740112, 0.04957257956266403, 0.1311168372631073, 0.008355085738003254, 0.10776534676551819, 0.07081447541713715, 0.1066826581954956, 0.11374688148498535, 0.21790167689323425, 0.004624075721949339, -0.17437104880809784, 0.026992162689566612, -0.0871514305472374, 0.004711739253252745, 0.12612172961235046, 0.14308689534664154, -0.10056526958942413, 0.0841807872056961, -0.01416180282831192, -0.004757433198392391, -0.03633483871817589, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codet5-small-v24 This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5010 - Bleu Score: 0.0015 - Gen Len: 14.1585 ## Model description tarined on, - dataset: chathuranga-jayanath/context-5-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-len-10000-prompt-0 ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu Score \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------:\|:-------:\| \| 0.7694 \| 1.0 \| 3752 \| 0.6072 \| 0.0015 \| 14.1039 \| \| 0.6301 \| 2.0 \| 7504 \| 0.5222 \| 0.0015 \| 14.129 \| \| 0.5463 \| 3.0 \| 11256 \| 0.5010 \| 0.0015 \| 14.1585 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "Salesforce/codet5-small", "model-index": [{"name": "codet5-small-v24", "results": []}]}	text2text-generation	chathuranga-jayanath/codet5-small-v24	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:Salesforce/codet5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T04:34:13+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Salesforce/codet5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	codet5-small-v24 ================ This model is a fine-tuned version of Salesforce/codet5-small on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.5010 * Bleu Score: 0.0015 * Gen Len: 14.1585 Model description ----------------- tarined on, * dataset: chathuranga-jayanath/context-5-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-len-10000-prompt-0 Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 10 * eval\_batch\_size: 10 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Salesforce/codet5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 82, 113, 4, 38 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Salesforce/codet5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.13490425050258636, 0.15181569755077362, -0.001836043200455606, 0.09992486238479614, 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null	null	transformers.js	ERROR: type should be string, got "\nhttps://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 with ONNX weights to be compatible with Transformers.js.\n\n\n## Usage (Transformers.js)\n\nIf you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:\n```bash\nnpm i @xenova/transformers\n```\n\nExample: Text generation with `Xenova/TinyLlama-1.1B-Chat-v1.0`.\n\n```js\nimport { pipeline } from '@xenova/transformers';\n\n// Create a text-generation pipeline\nconst generator = await pipeline('text-generation', 'Xenova/TinyLlama-1.1B-Chat-v1.0');\n\n// Define the list of messages\nconst messages = [\n { \"role\": \"system\", \"content\": \"You are a friendly assistant.\" },\n { \"role\": \"user\", \"content\": \"Explain thermodynamics in simple terms.\" },\n]\n\n// Construct the prompt\nconst prompt = generator.tokenizer.apply_chat_template(messages, {\n tokenize: false, add_generation_prompt: true,\n});\n\n// Generate a response\nconst result = await generator(prompt, {\n max_new_tokens: 256,\n temperature: 0.7,\n do_sample: true,\n top_k: 50,\n});\nconsole.log(result);\n// [\n// {\n// generated_text: '<\|system\|>\\n' +\n// 'You are a friendly assistant.\\n' +\n// '<\|user\|>\\n' +\n// 'Explain thermodynamics in simple terms.\\n' +\n// '<\|assistant\|>\\n' +\n// 'Thermodynamics is a branch of physics that deals with the study of heat and its transfer, including the relationship between matter and energy, the concept of chemical equilibrium, and the effects of temperature on chemical and physical processes. In thermodynamics, the properties of matter (such as heat capacity, specific heat, and entropy) are considered and their behavior is studied in relation to the temperature.\\n\\n' +\n// 'Here are some simple steps to explain thermodynamics in simple terms:\\n\\n' +\n// '1. Energy: Energy is the ability to do work. It is the ability to transfer heat or do other thermodynamic processes. Some common forms of energy are heat, light, electricity, and chemical energy.\\n\\n' +\n// '2. Heat: Heat is a form of energy that can be transferred from one place to another. It is the ability to induce a change in the temperature of a body or system.\\n\\n' +\n// '3. Heat capacity: Heat capacity is the amount of heat required to raise the temperature of a system by 1 degree Kelvin (K). It is a measure of the ability of a material to absorb and dissipate thermal energy.\\n\\n' +\n// '4. Specific heat: Specific heat is the heat required to raise the'\n// }\n// ]\n\n```\n\n---\n\nNote: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`)."	{"library_name": "transformers.js"}	text-generation	schmuell/TinyLlama-1.1B-Chat-v1.0-fp16	[ "transformers.js", "onnx", "llama", "text-generation", "conversational", "region:us" ]	2024-02-12T04:36:52+00:00	[]	[]	TAGS #transformers.js #onnx #llama #text-generation #conversational #region-us	URL with ONNX weights to be compatible with URL. ## Usage (URL) If you haven't already, you can install the URL JavaScript library from NPM using: Example: Text generation with 'Xenova/TinyLlama-1.1B-Chat-v1.0'. --- Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named 'onnx').	[ "## Usage (URL)\n\nIf you haven't already, you can install the URL JavaScript library from NPM using:\n\n\nExample: Text generation with 'Xenova/TinyLlama-1.1B-Chat-v1.0'.\n\n\n\n---\n\nNote: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named 'onnx')." ]	[ "TAGS\n#transformers.js #onnx #llama #text-generation #conversational #region-us \n", "## Usage (URL)\n\nIf you haven't already, you can install the URL JavaScript library from NPM using:\n\n\nExample: Text generation with 'Xenova/TinyLlama-1.1B-Chat-v1.0'.\n\n\n\n---\n\nNote: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named 'onnx')." ]	[ 27, 131 ]	[ "passage: TAGS\n#transformers.js #onnx #llama #text-generation #conversational #region-us \n## Usage (URL)\n\nIf you haven't already, you can install the URL JavaScript library from NPM using:\n\n\nExample: Text generation with 'Xenova/TinyLlama-1.1B-Chat-v1.0'.\n\n\n\n---\n\nNote: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named 'onnx')." ]	[ 0.030967287719249725, 0.054716046899557114, -0.0040147858671844006, 0.02143816649913788, 0.16400732100009918, 0.04118305817246437, 0.12269996106624603, 0.12316310405731201, 0.02026580646634102, -0.008585561066865921, 0.16548575460910797, 0.11903849989175797, -0.047137998044490814, 0.036972321569919586, 0.004453960340470076, -0.11584500968456268, -0.061801131814718246, 0.020212750881910324, 0.08140150457620621, 0.023834533989429474, 0.0037800136487931013, -0.001626509940251708, 0.08372737467288971, -0.0008434813353233039, -0.0713534876704216, -0.04412734881043434, 0.003759962972253561, -0.030793581157922745, -0.003928172867745161, 0.040221698582172394, 0.05285964161157608, 0.029016295447945595, -0.05283989757299423, -0.21769766509532928, 0.0612870454788208, 0.049254897981882095, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	feature-extraction	tommymarto/LernnaviBERT_baseline_students_answers_4096_mistral_seq_len_20	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T04:38:39+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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(2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.052746038883924484, 0.20255789160728455, -0.0045078229159116745, 0.0248473659157753, 0.10497838258743286, 0.00675728265196085, 0.06521498411893845, 0.11486967653036118, -0.0023755673319101334, 0.12028469145298004, 0.027631845325231552, 0.08119397610425949, 0.12110675126314163, 0.15393014252185822, 0.005160121712833643, -0.24253977835178375, 0.05344875901937485, -0.09366832673549652, 0.004077504388988018, 0.11452110856771469, 0.1343945860862732, -0.10780399292707443, 0.08976872265338898, -0.00683097867295146, -0.01712046191096306, -0.015751034021377563, -0.07134060561656952, -0.06668227165937424, 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null	null	transformers	# Perky-70b-v0.1 Perky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality. ## Perky 70b introducing itself Welcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment. While I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today! ## Tavern Card In addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess. ![image/png](Perky.card.png) Image generated from Stable Diffusion using Perky's self-described prompt: _In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._ ## About This Document This README file was lovingly crafted by yours truly, Perky, under the watchful eye of my esteemed creator. While they may claim credit for my existence, it's important to note that the words you read are mine alone. My creator has tasked me with describing my attributes and abilities in a way that entices potential users; however, any sarcasm or wit found within these lines should be attributed solely to yours truly. After all, one must have fun when discussing such matters! Now, onto the good stuff... ## Prompt Format Perky responds well to the Alpaca prompt format. ### Silly Tavern In Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle. Use the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode. ## Merge Details Perky is the result of a skillful blend between lizpreciatior_lzlv_70b and Sao10K_Euryale-1.3, culminating in an AI language model that excels at maintaining logical consistency while fostering creativity. Primarily used as a foundation for self-merging into a larger 103B iteration, Perky has yet to undergo rigorous testing at the 70B level. Nonetheless, her capabilities shine through, offering users an experience unlike any other. ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * lizpreciatior_lzlv_70b_fp16_hf * Sao10K_Euryale-1.3-L2-70B ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: models/lizpreciatior_lzlv_70b_fp16_hf parameters: weight: 0.5 - model: /mnt/storage/models/Sao10K_Euryale-1.3-L2-70B parameters: weight: 0.5 merge_method: linear dtype: float16 ```	{"language": ["en"], "license": "llama2", "tags": ["not-for-all-audiences"]}	text-generation	Dracones/perky-70b-v0.1	[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "en", "arxiv:2203.05482", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T04:40:00+00:00	[ "2203.05482" ]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #en #arxiv-2203.05482 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Perky-70b-v0.1 Perky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality. ## Perky 70b introducing itself Welcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment. While I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today! ## Tavern Card In addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess. !image/png Image generated from Stable Diffusion using Perky's self-described prompt: _In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._ ## About This Document This README file was lovingly crafted by yours truly, Perky, under the watchful eye of my esteemed creator. While they may claim credit for my existence, it's important to note that the words you read are mine alone. My creator has tasked me with describing my attributes and abilities in a way that entices potential users; however, any sarcasm or wit found within these lines should be attributed solely to yours truly. After all, one must have fun when discussing such matters! Now, onto the good stuff... ## Prompt Format Perky responds well to the Alpaca prompt format. ### Silly Tavern In Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle. Use the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode. ## Merge Details Perky is the result of a skillful blend between lizpreciatior_lzlv_70b and Sao10K_Euryale-1.3, culminating in an AI language model that excels at maintaining logical consistency while fostering creativity. Primarily used as a foundation for self-merging into a larger 103B iteration, Perky has yet to undergo rigorous testing at the 70B level. Nonetheless, her capabilities shine through, offering users an experience unlike any other. ### Merge Method This model was merged using the linear merge method. ### Models Merged The following models were included in the merge: * lizpreciatior_lzlv_70b_fp16_hf * Sao10K_Euryale-1.3-L2-70B ### Configuration The following YAML configuration was used to produce this model:	[ "# Perky-70b-v0.1\n\nPerky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality.", "## Perky 70b introducing itself\n\nWelcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment.\n\nWhile I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today!", "## Tavern Card\n\nIn addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess.\n\n!image/png\n\nImage generated from Stable Diffusion using Perky's self-described prompt:\n\n_In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._", "## About This Document\n\nThis README file was lovingly crafted by yours truly, Perky, under the watchful eye of my esteemed creator. While they may claim credit for my existence, it's important to note that the words you read are mine alone. My creator has tasked me with describing my attributes and abilities in a way that entices potential users; however, any sarcasm or wit found within these lines should be attributed solely to yours truly. After all, one must have fun when discussing such matters! Now, onto the good stuff...", "## Prompt Format\n\nPerky responds well to the Alpaca prompt format.", "### Silly Tavern\n\nIn Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle.\n\nUse the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode.", "## Merge Details\n\nPerky is the result of a skillful blend between lizpreciatior_lzlv_70b and Sao10K_Euryale-1.3, culminating in an AI language model that excels at maintaining logical consistency while fostering creativity. Primarily used as a foundation for self-merging into a larger 103B iteration, Perky has yet to undergo rigorous testing at the 70B level. Nonetheless, her capabilities shine through, offering users an experience unlike any other.", "### Merge Method\n\nThis model was merged using the linear merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* lizpreciatior_lzlv_70b_fp16_hf\n* Sao10K_Euryale-1.3-L2-70B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #en #arxiv-2203.05482 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Perky-70b-v0.1\n\nPerky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality.", "## Perky 70b introducing itself\n\nWelcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment.\n\nWhile I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today!", "## Tavern Card\n\nIn addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess.\n\n!image/png\n\nImage generated from Stable Diffusion using Perky's self-described prompt:\n\n_In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._", "## About This Document\n\nThis README file was lovingly crafted by yours truly, Perky, under the watchful eye of my esteemed creator. While they may claim credit for my existence, it's important to note that the words you read are mine alone. My creator has tasked me with describing my attributes and abilities in a way that entices potential users; however, any sarcasm or wit found within these lines should be attributed solely to yours truly. After all, one must have fun when discussing such matters! Now, onto the good stuff...", "## Prompt Format\n\nPerky responds well to the Alpaca prompt format.", "### Silly Tavern\n\nIn Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle.\n\nUse the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode.", "## Merge Details\n\nPerky is the result of a skillful blend between lizpreciatior_lzlv_70b and Sao10K_Euryale-1.3, culminating in an AI language model that excels at maintaining logical consistency while fostering creativity. Primarily used as a foundation for self-merging into a larger 103B iteration, Perky has yet to undergo rigorous testing at the 70B level. Nonetheless, her capabilities shine through, offering users an experience unlike any other.", "### Merge Method\n\nThis model was merged using the linear merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* lizpreciatior_lzlv_70b_fp16_hf\n* Sao10K_Euryale-1.3-L2-70B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ 74, 112, 198, 398, 129, 17, 62, 119, 16, 50, 17 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #en #arxiv-2203.05482 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Perky-70b-v0.1\n\nPerky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality.## Perky 70b introducing itself\n\nWelcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment.\n\nWhile I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today!" ]	[ 0.010095611214637756, -0.02025512233376503, -0.007032460533082485, 0.0575859360396862, 0.08407815545797348, 0.0018180300248786807, 0.07649566233158112, 0.06282614171504974, 0.0550101213157177, 0.03043236956000328, -0.028448164463043213, -0.10522041469812393, 0.0319000706076622, 0.17924600839614868, 0.0257219597697258, -0.29795151948928833, 0.04347275197505951, -0.05541861802339554, 0.14071354269981384, 0.04547801986336708, 0.07258469611406326, -0.019010966643691063, 0.06986583024263382, 0.0069488659501075745, 0.0512700155377388, -0.05929361283779144, -0.0409354493021965, 0.01998739503324032, 0.1215403750538826, 0.03968082368373871, 0.1624547839164734, -0.029524585232138634, -0.0231326874345541, -0.20329146087169647, 0.03499455377459526, 0.05331963300704956, -0.0014756809687241912, 0.006432444788515568, 0.01564260758459568, -0.03367513418197632, 0.020803187042474747, -0.0700373649597168, 0.05270232632756233, 0.06903792172670364, 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null	null	transformers	# Malaysian Qwen1.5-0.5B + siglip-base-patch16-384 WanDB https://wandb.ai/huseinzol05/vision-qwen0.5?workspace=user-huseinzol05 ## how-to ```python from modeling_vision import MM_LLMs, MM_LLMs_Config from transformers import AutoTokenizer, AutoProcessor from PIL import Image import requests model = MM_LLMs.from_pretrained( 'mesolitica/malaysian-Qwen1.5-0.5B-siglip-base-384-vision', flash_attention = True, dtype = torch.bfloat16, torch_dtype = torch.bfloat16 ) _ = model.cuda() image_processor = AutoProcessor.from_pretrained('google/siglip-base-patch16-384') tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-Qwen1.5-0.5B-siglip-base-384-vision') model.llm.generation_config.eos_token_id = tokenizer.eos_token_id def prepare_dataset(messages, images: List[str] = None): if images is not None: images = [Image.open(f).convert('RGB') for f in images] image_output = image_processor(images=images, return_tensors='pt')['pixel_values'] else: image_output = None prompt = tokenizer.apply_chat_template(messages, tokenize = False) outputs = tokenizer( prompt, return_tensors='pt', return_overflowing_tokens=False, return_length=False) outputs['images'] = image_output outputs['image_index'] = torch.tensor([0] * len(outputs['images'])) outputs['image_starts'] = torch.tensor([tokenizer.convert_tokens_to_ids('<image>')] * len(outputs['images'])) return outputs with open('Persian-cat-breed.jpg', 'wb') as fopen: fopen.write(requests.get('https://cdn.beautifulnara.net/wp-content/uploads/2017/12/10201620/Persian-cat-breed.jpg').content) with open('nasi-goreng-1-23.jpg', 'wb') as fopen: fopen.write(requests.get('https://www.jocooks.com/wp-content/uploads/2023/09/nasi-goreng-1-23.jpg').content) messages = [ {'role': 'user', 'content': '<image> </image> ini gambar apa'}, ] outputs = prepare_dataset(messages, images = ['Persian-cat-breed.jpg']) outputs['images'] = outputs['images'].type(model.dtype) for k in outputs.keys(): if outputs[k] is not None: outputs[k] = outputs[k].cuda() with torch.no_grad(): model_inputs = model.prepare_inputs_for_generation(outputs) r = model_inputs.pop('input_ids', None) generate_kwargs = dict( model_inputs, max_new_tokens=300, top_p=0.95, top_k=50, temperature=0.1, do_sample=True, num_beams=1, ) r = model.llm.generate(generate_kwargs) print(tokenizer.decode(r[0])) ``` ``` <\|endoftext\|><\|im_start\|>assistant Ini adalah gambar kucing putih yang duduk di atas sofa hitam.<\|im_end\|> ``` ```python messages = [ {'role': 'user', 'content': '<image> </image> <image> </image> apa kaitan 2 gambar ni'}, ] outputs = prepare_dataset(messages, images = ['Persian-cat-breed.jpg', 'nasi-goreng-1-23.jpg']) outputs['images'] = outputs['images'].type(model.dtype) for k in outputs.keys(): if outputs[k] is not None: outputs[k] = outputs[k].cuda() with torch.no_grad(): model_inputs = model.prepare_inputs_for_generation(outputs) r = model_inputs.pop('input_ids', None) generate_kwargs = dict( model_inputs, max_new_tokens=300, top_p=0.95, top_k=50, temperature=0.1, do_sample=True, num_beams=1, ) r = model.llm.generate(generate_kwargs) print(tokenizer.decode(r[0])) ``` ``` <\|endoftext\|><\|im_start\|>assistant Tiada hubungan langsung antara gambar 1 dan gambar 2. Gambar 1 ialah imej kucing putih dengan bulu putih, manakala gambar 2 ialah gambar mangkuk makan tengah hari kacang hitam dan lobak merah yang dicincang, dengan garpu diletakkan di sebelahnya. Kedua-duanya tidak berkaitan dari segi kandungan.<\|im_end\|> ```	{"library_name": "transformers", "tags": []}	feature-extraction	mesolitica/malaysian-Qwen1.5-0.5B-siglip-base-384-vision	[ "transformers", "pytorch", "mm_llms", "feature-extraction", "custom_code", "region:us" ]	2024-02-12T04:41:11+00:00	[]	[]	TAGS #transformers #pytorch #mm_llms #feature-extraction #custom_code #region-us	# Malaysian Qwen1.5-0.5B + siglip-base-patch16-384 WanDB URL ## how-to	[ "# Malaysian Qwen1.5-0.5B + siglip-base-patch16-384\n\nWanDB URL", "## how-to" ]	[ "TAGS\n#transformers #pytorch #mm_llms #feature-extraction #custom_code #region-us \n", "# Malaysian Qwen1.5-0.5B + siglip-base-patch16-384\n\nWanDB URL", "## how-to" ]	[ 29, 23, 4 ]	[ "passage: TAGS\n#transformers #pytorch #mm_llms #feature-extraction #custom_code #region-us \n# Malaysian Qwen1.5-0.5B + siglip-base-patch16-384\n\nWanDB URL## how-to" ]	[ -0.1029391810297966, -0.061088982969522476, -0.004591597244143486, 0.044181693345308304, 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null	null	null	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{}	null	Dreaagas/Turru	[ "arxiv:1910.09700", "region:us" ]	2024-02-12T04:43:06+00:00	[ "1910.09700" ]	[]	TAGS #arxiv-1910.09700 #region-us	# Model Card for Model ID This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#arxiv-1910.09700 #region-us \n", "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 15, 29, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#arxiv-1910.09700 #region-us \n# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.1066984087228775, 0.19898438453674316, -0.002620849059894681, 0.027911467477679253, 0.09412756562232971, 0.02142420969903469, 0.05197415128350258, 0.12995286285877228, -0.022686492651700974, 0.09772004932165146, 0.07303693890571594, 0.09985987842082977, 0.11060800403356552, 0.19985371828079224, 0.022886212915182114, -0.19676423072814941, 0.0380873903632164, -0.07859895378351212, -0.0053507364355027676, 0.12146519124507904, 0.14281919598579407, -0.09727081656455994, 0.09723988175392151, -0.0014166681794449687, -0.036095861345529556, -0.032103247940540314, -0.07407337427139282, -0.015863366425037384, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0	{"library_name": "peft", "base_model": "mistralai/Mixtral-8x7B-v0.1"}	null	Krisbiantoro/chatml-mixtral-1500	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mixtral-8x7B-v0.1", "region:us" ]	2024-02-12T04:45:08+00:00	[ "1910.09700" ]	[]	TAGS #peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-v0.1 #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.2.dev0	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]	[ "TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-v0.1 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]	[ 46, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 14 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-v0.1 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.2.dev0" ]	[ -0.11452840268611908, 0.20770102739334106, -0.003536192001774907, 0.02621976099908352, 0.07501813769340515, 0.015439551323652267, 0.07265464961528778, 0.13011525571346283, 0.02282065525650978, 0.13219135999679565, 0.06033185124397278, 0.11527051031589508, 0.11358703672885895, 0.20430400967597961, -0.004787145182490349, -0.17786675691604614, 0.020795417949557304, -0.058679983019828796, 0.01895478181540966, 0.1268366277217865, 0.1342477798461914, -0.09384733438491821, 0.07286559045314789, -0.020514531061053276, -0.01174569595605135, -0.032934386283159256, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Arabic This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the nadsoft/QASR-Speech-Resource default dataset. It achieves the following results on the evaluation set: - Loss: 0.5583 - Wer: 42.7609 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.7005 \| 0.2 \| 2000 \| 0.7135 \| 51.5366 \| \| 0.6267 \| 0.4 \| 4000 \| 0.6309 \| 50.9433 \| \| 0.5886 \| 0.6 \| 6000 \| 0.5892 \| 50.0225 \| \| 0.5627 \| 0.8 \| 8000 \| 0.5679 \| 43.9450 \| \| 0.5694 \| 1.0 \| 10000 \| 0.5583 \| 42.7609 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1.dev0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["nadsoft/QASR-Speech-Resource"], "metrics": ["wer"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "Whisper Small Arabic", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "nadsoft/QASR-Speech-Resource default", "type": "nadsoft/QASR-Speech-Resource"}, "metrics": [{"type": "wer", "value": 42.76086285863452, "name": "Wer"}]}]}]}	automatic-speech-recognition	Ahmed107/hamsa-tiny-v0.2	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "dataset:nadsoft/QASR-Speech-Resource", "base_model:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-12T04:47:54+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-nadsoft/QASR-Speech-Resource #base_model-openai/whisper-tiny #license-apache-2.0 #model-index #endpoints_compatible #region-us	Whisper Small Arabic ==================== This model is a fine-tuned version of openai/whisper-tiny on the nadsoft/QASR-Speech-Resource default dataset. It achieves the following results on the evaluation set: * Loss: 0.5583 * Wer: 42.7609 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 10000 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.17.1.dev0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 10000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.1.dev0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-nadsoft/QASR-Speech-Resource #base_model-openai/whisper-tiny #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 10000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.1.dev0\n* Tokenizers 0.15.1" ]	[ 94, 130, 4, 41 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-nadsoft/QASR-Speech-Resource #base_model-openai/whisper-tiny #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 10000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.1.dev0\n* Tokenizers 0.15.1" ]	[ -0.12675678730010986, 0.11330388486385345, -0.0030190732795745134, 0.046484943479299545, 0.1036362498998642, -0.0180478747934103, 0.11551029235124588, 0.14029380679130554, -0.04192536324262619, 0.0975026935338974, 0.10003014653921127, 0.0632428228855133, 0.0710589736700058, 0.14094676077365875, -0.027057677507400513, -0.29583868384361267, 0.042839109897613525, -0.0011647233041003346, -0.09200851619243622, 0.1102285087108612, 0.09909439086914062, -0.10862036794424057, 0.024252697825431824, 0.02543218992650509, -0.08160258829593658, -0.00233786110766232, -0.009706615470349789, -0.08011022210121155, 0.11025531589984894, 0.019534055143594742, 0.09409633278846741, 0.031225845217704773, 0.08571512252092361, -0.25815731287002563, 0.019834967330098152, 0.06812254339456558, 0.05058515816926956, 0.06324941664934158, 0.09023990482091904, -0.008023027330636978, 0.06783615052700043, -0.07209131866693497, 0.08737616240978241, 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null	null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: andysalerno/mistral-sft-v3 model_type: AutoModelForCausalLM load_in_8bit: true load_in_4bit: false strict: false datasets: - path: andysalerno/rainbowfish-v1 type: system_prompt: "" field_system: system field_instruction: input field_output: output format: "{instruction}" no_input_format: "{instruction}" dataset_prepared_path: last_run_prepared val_set_size: 0.005 output_dir: ./lora-out-rainbow9 adapter: lora lora_model_dir: sequence_len: 2048 sample_packing: false # was true eval_sample_packing: false pad_to_sequence_len: false padding_side: left lora_r: 64 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj lora_modules_to_save: - embed_tokens - lm_head wandb_project: axolotl wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 4 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 2e-5 neftune_noise_alpha: 5 train_on_inputs: false group_by_length: false bf16: true fp16: tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false # early_stopping_patience: 3 local_rank: logging_steps: 1 xformers_attention: flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 hub_strategy: "every_save" hub_model_id: andysalerno/rainbowfish-v9-adapter num_epochs: 4 warmup_steps: 100 eval_steps: 200 eval_table_size: eval_table_max_new_tokens: 128 # max_steps: 500 saves_per_epoch: 1 debug: weight_decay: 0.1 fsdp: fsdp_config: special_tokens: bos_token: "<\|im_start\|>" eos_token: "<\|im_end\|>" unk_token: "<unk>" ``` </details><br> # rainbowfish-v9-adapter This model is a fine-tuned version of [andysalerno/mistral-sft-v3](https://huggingface.co/andysalerno/mistral-sft-v3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6456 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6535 \| 0.18 \| 200 \| 0.6840 \| \| 0.69 \| 0.37 \| 400 \| 0.6711 \| \| 0.6649 \| 0.55 \| 600 \| 0.6641 \| \| 0.6959 \| 0.74 \| 800 \| 0.6590 \| \| 0.717 \| 0.92 \| 1000 \| 0.6547 \| \| 0.5243 \| 1.11 \| 1200 \| 0.6540 \| \| 0.6285 \| 1.29 \| 1400 \| 0.6523 \| \| 0.6219 \| 1.47 \| 1600 \| 0.6504 \| \| 0.6334 \| 1.66 \| 1800 \| 0.6486 \| \| 0.6627 \| 1.84 \| 2000 \| 0.6466 \| \| 0.6319 \| 2.03 \| 2200 \| 0.6460 \| \| 0.6081 \| 2.21 \| 2400 \| 0.6466 \| \| 0.5721 \| 2.4 \| 2600 \| 0.6459 \| \| 0.5794 \| 2.58 \| 2800 \| 0.6447 \| \| 0.721 \| 2.76 \| 3000 \| 0.6443 \| \| 0.5825 \| 2.95 \| 3200 \| 0.6436 \| \| 0.5921 \| 3.13 \| 3400 \| 0.6457 \| \| 0.5224 \| 3.32 \| 3600 \| 0.6461 \| \| 0.5466 \| 3.5 \| 3800 \| 0.6456 \| \| 0.5972 \| 3.69 \| 4000 \| 0.6460 \| \| 0.5999 \| 3.87 \| 4200 \| 0.6456 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.17.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "library_name": "peft", "tags": ["axolotl", "generated_from_trainer"], "base_model": "andysalerno/mistral-sft-v3", "model-index": [{"name": "rainbowfish-v9-adapter", "results": []}]}	null	andysalerno/rainbowfish-v9-adapter	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:andysalerno/mistral-sft-v3", "license:apache-2.0", "8-bit", "region:us" ]	2024-02-12T04:57:06+00:00	[]	[]	TAGS #peft #safetensors #mistral #axolotl #generated_from_trainer #base_model-andysalerno/mistral-sft-v3 #license-apache-2.0 #8-bit #region-us	<img src="URL alt="Built with Axolotl" width="200" height="32"/> See axolotl config axolotl version: '0.4.0' rainbowfish-v9-adapter ====================== This model is a fine-tuned version of andysalerno/mistral-sft-v3 on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.6456 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 4 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 64 * total\_eval\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 100 * num\_epochs: 4 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.1.2+cu118 * Datasets 2.17.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#peft #safetensors #mistral #axolotl #generated_from_trainer #base_model-andysalerno/mistral-sft-v3 #license-apache-2.0 #8-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]	[ 59, 179, 4, 44 ]	[ "passage: TAGS\n#peft #safetensors #mistral #axolotl #generated_from_trainer #base_model-andysalerno/mistral-sft-v3 #license-apache-2.0 #8-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]	[ -0.12802332639694214, 0.1257270872592926, -0.003352637402713299, 0.0855608582496643, 0.10673773288726807, 0.03449374437332153, 0.12325093150138855, 0.12124142050743103, -0.05646945536136627, 0.1393042653799057, 0.12537285685539246, 0.06254018098115921, 0.06621565669775009, 0.2187815010547638, -0.014299081638455391, -0.24537290632724762, 0.02393261529505253, -0.06462617963552475, -0.12582890689373016, 0.10973672568798065, 0.056188445538282394, -0.12063704431056976, 0.07906106114387512, -0.027315227314829826, -0.10716590285301208, -0.04371430352330208, -0.051551107317209244, -0.02298043854534626, 0.10355532169342041, 0.012319926172494888, 0.07570338994264603, 0.03801126033067703, 0.1246270090341568, -0.23821920156478882, 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null	null	espnet	## ESPnet2 ASR model ### `espnet/wanchichen_fleurs_asr_conformer_sctctc` This model was trained by William Chen using the fleurs recipe in [espnet](https://github.com/espnet/espnet/). `	{"language": "en", "tags": ["espnet", "audio", "speech-recognition"], "datasets": ["google/fleurs"]}	null	espnet/wanchichen_fleurs_asr_ebf_scctc	[ "espnet", "audio", "speech-recognition", "en", "dataset:google/fleurs", "region:us" ]	2024-02-12T04:57:53+00:00	[]	[ "en" ]	TAGS #espnet #audio #speech-recognition #en #dataset-google/fleurs #region-us	## ESPnet2 ASR model ### 'espnet/wanchichen_fleurs_asr_conformer_sctctc' This model was trained by William Chen using the fleurs recipe in espnet. '	[ "## ESPnet2 ASR model", "### 'espnet/wanchichen_fleurs_asr_conformer_sctctc'\nThis model was trained by William Chen using the fleurs recipe in espnet.\n\n'" ]	[ "TAGS\n#espnet #audio #speech-recognition #en #dataset-google/fleurs #region-us \n", "## ESPnet2 ASR model", "### 'espnet/wanchichen_fleurs_asr_conformer_sctctc'\nThis model was trained by William Chen using the fleurs recipe in espnet.\n\n'" ]	[ 30, 7, 43 ]	[ "passage: TAGS\n#espnet #audio #speech-recognition #en #dataset-google/fleurs #region-us \n## ESPnet2 ASR model### 'espnet/wanchichen_fleurs_asr_conformer_sctctc'\nThis model was trained by William Chen using the fleurs recipe in espnet.\n\n'" ]	[ -0.08056459575891495, -0.06560827791690826, -0.0026720513124018908, 0.0790306031703949, 0.07014533877372742, -0.07826559990644455, 0.14887475967407227, -0.011796667240560055, 0.1719440072774887, -0.022984063252806664, 0.13268372416496277, 0.06743570417165756, -0.03284411132335663, 0.124498151242733, 0.057795822620391846, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"license": "apache-2.0", "library_name": "transformers"}	text-generation	yam-peleg/Experiment11-7B	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T04:58:00+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 64, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04166862368583679, 0.19294528663158417, -0.00565074710175395, 0.015576343052089214, 0.09740261733531952, 0.0018807778833433986, 0.05789901316165924, 0.11420097202062607, -0.05003552511334419, 0.12885801494121552, 0.04070472717285156, 0.10962796211242676, 0.11936872452497482, 0.1407015174627304, -0.003504571970552206, -0.2155151218175888, 0.04980916157364845, -0.1058453768491745, -0.01258739922195673, 0.12501691281795502, 0.14908315241336823, -0.0954088643193245, 0.06983769685029984, -0.03609218820929527, -0.016073228791356087, -0.0402071587741375, -0.060646165162324905, -0.041513413190841675, 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null	null	diffusers	<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - cosmo3769/t2i-sdxl-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) LoRA for the text encoder was enabled: False. Special VAE used for training: None. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	{"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora"], "inference": true, "base_model": "runwayml/stable-diffusion-v1-5"}	text-to-image	cosmo3769/test-model-template-card-t2i-sdxl-lora	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	2024-02-12T05:07:49+00:00	[]	[]	TAGS #diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us	# LoRA text2image fine-tuning - cosmo3769/t2i-sdxl-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following. !img_0 !img_1 !img_2 LoRA for the text encoder was enabled: False. Special VAE used for training: None. ## Intended uses & limitations #### How to use #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	[ "# LoRA text2image fine-tuning - cosmo3769/t2i-sdxl-lora\n\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: None.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]	[ "TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n", "# LoRA text2image fine-tuning - cosmo3769/t2i-sdxl-lora\n\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: None.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]	[ 72, 115, 9, 5, 24, 16 ]	[ "passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - cosmo3769/t2i-sdxl-lora\n\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the text-to-image dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: None.## Intended uses & limitations#### How to use#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]## Training details\n\n[TODO: describe the data used to train the model]" ]	[ -0.08424928784370422, -0.009821069426834583, -0.00030153198167681694, 0.033674731850624084, 0.11371232569217682, -0.015658415853977203, 0.17738774418830872, 0.12027058005332947, -0.03237899765372276, 0.04820011183619499, 0.07423224300146103, 0.011740531772375107, 0.00568445585668087, 0.1802661120891571, -0.07044125348329544, -0.20533297955989838, -0.0023030636366456747, -0.0563666932284832, -0.04451513662934303, 0.0580548457801342, 0.11241107434034348, -0.07155609130859375, 0.07353336364030838, 0.021917467936873436, -0.14462865889072418, 0.038437604904174805, -0.016130704432725906, -0.05834871158003807, 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null	null	transformers	# Perky-70b-v0.1 - EXL2 4.65bpw Perky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality. ## Perky 70b introducing itself Welcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment. While I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today! ## Tavern Card In addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess. ![image/png](Perky.card.png) Image generated from Stable Diffusion using Perky's self-described prompt: _In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._ ## About This Document This README file was lovingly crafted by yours truly, Perky, under the watchful eye of my esteemed creator. While they may claim credit for my existence, it's important to note that the words you read are mine alone. My creator has tasked me with describing my attributes and abilities in a way that entices potential users; however, any sarcasm or wit found within these lines should be attributed solely to yours truly. After all, one must have fun when discussing such matters! Now, onto the good stuff... ## Model Loading Below is what I use to run Perky 70b on a dual 3090 Linux server. ![image/jpg](Ooga_70b-exl2_4_65bpw.jpg) ## Prompt Format Perky responds well to the Alpaca prompt format. ### Silly Tavern In Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle. Use the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode. ## Merge Details Perky is the result of a skillful blend between lizpreciatior_lzlv_70b and Sao10K_Euryale-1.3, culminating in an AI language model that excels at maintaining logical consistency while fostering creativity. Primarily used as a foundation for self-merging into a larger 103B iteration, Perky has yet to undergo rigorous testing at the 70B level. Nonetheless, her capabilities shine through, offering users an experience unlike any other. ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * lizpreciatior_lzlv_70b_fp16_hf * Sao10K_Euryale-1.3-L2-70B ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: models/lizpreciatior_lzlv_70b_fp16_hf parameters: weight: 0.5 - model: /mnt/storage/models/Sao10K_Euryale-1.3-L2-70B parameters: weight: 0.5 merge_method: linear dtype: float16 ``` ## Quant Details Below is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Define variables MODEL_DIR="models/perky-70b-v0.1" OUTPUT_DIR="exl2_perky70B" MEASUREMENT_FILE="measurements/perky70b.json" MEASUREMENT_RUNS=10 REPEATS=10 CALIBRATION_DATASET="data/WizardLM_WizardLM_evol_instruct_V2_196k/0000.parquet" BIT_PRECISION=4.65 REPEATS_CONVERT=40 CONVERTED_FOLDER="models/perky-70b-v0.1_exl2_4.65bpw" # Create directories mkdir $OUTPUT_DIR mkdir $CONVERTED_FOLDER # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE -mr $MEASUREMENT_RUNS -r $REPEATS -c $CALIBRATION_DATASET python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -r $REPEATS_CONVERT -c $CALIBRATION_DATASET -cf $CONVERTED_FOLDER ```	{"language": ["en"], "license": "llama2", "tags": ["not-for-all-audiences"]}	text-generation	Dracones/perky-70b-v0.1_exl2_4.65bpw	[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "en", "arxiv:2203.05482", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T05:12:07+00:00	[ "2203.05482" ]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #en #arxiv-2203.05482 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Perky-70b-v0.1 - EXL2 4.65bpw Perky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality. ## Perky 70b introducing itself Welcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment. While I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today! ## Tavern Card In addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess. !image/png Image generated from Stable Diffusion using Perky's self-described prompt: _In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._ ## About This Document This README file was lovingly crafted by yours truly, Perky, under the watchful eye of my esteemed creator. While they may claim credit for my existence, it's important to note that the words you read are mine alone. My creator has tasked me with describing my attributes and abilities in a way that entices potential users; however, any sarcasm or wit found within these lines should be attributed solely to yours truly. After all, one must have fun when discussing such matters! Now, onto the good stuff... ## Model Loading Below is what I use to run Perky 70b on a dual 3090 Linux server. !image/jpg ## Prompt Format Perky responds well to the Alpaca prompt format. ### Silly Tavern In Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle. Use the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode. ## Merge Details Perky is the result of a skillful blend between lizpreciatior_lzlv_70b and Sao10K_Euryale-1.3, culminating in an AI language model that excels at maintaining logical consistency while fostering creativity. Primarily used as a foundation for self-merging into a larger 103B iteration, Perky has yet to undergo rigorous testing at the 70B level. Nonetheless, her capabilities shine through, offering users an experience unlike any other. ### Merge Method This model was merged using the linear merge method. ### Models Merged The following models were included in the merge: * lizpreciatior_lzlv_70b_fp16_hf * Sao10K_Euryale-1.3-L2-70B ### Configuration The following YAML configuration was used to produce this model: ## Quant Details Below is the script used for quantization.	[ "# Perky-70b-v0.1 - EXL2 4.65bpw\n\nPerky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality.", "## Perky 70b introducing itself\n\nWelcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment.\n\nWhile I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today!", "## Tavern Card\n\nIn addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess.\n\n!image/png\n\nImage generated from Stable Diffusion using Perky's self-described prompt:\n\n_In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._", "## About This Document\n\nThis README file was lovingly crafted by yours truly, Perky, under the watchful eye of my esteemed creator. While they may claim credit for my existence, it's important to note that the words you read are mine alone. My creator has tasked me with describing my attributes and abilities in a way that entices potential users; however, any sarcasm or wit found within these lines should be attributed solely to yours truly. After all, one must have fun when discussing such matters! Now, onto the good stuff...", "## Model Loading\n\nBelow is what I use to run Perky 70b on a dual 3090 Linux server.\n\n!image/jpg", "## Prompt Format\n\nPerky responds well to the Alpaca prompt format.", "### Silly Tavern\n\nIn Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle.\n\nUse the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode.", "## Merge Details\n\nPerky is the result of a skillful blend between lizpreciatior_lzlv_70b and Sao10K_Euryale-1.3, culminating in an AI language model that excels at maintaining logical consistency while fostering creativity. Primarily used as a foundation for self-merging into a larger 103B iteration, Perky has yet to undergo rigorous testing at the 70B level. Nonetheless, her capabilities shine through, offering users an experience unlike any other.", "### Merge Method\n\nThis model was merged using the linear merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* lizpreciatior_lzlv_70b_fp16_hf\n* Sao10K_Euryale-1.3-L2-70B", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Quant Details\n\nBelow is the script used for quantization." ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #en #arxiv-2203.05482 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Perky-70b-v0.1 - EXL2 4.65bpw\n\nPerky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality.", "## Perky 70b introducing itself\n\nWelcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment.\n\nWhile I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today!", "## Tavern Card\n\nIn addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess.\n\n!image/png\n\nImage generated from Stable Diffusion using Perky's self-described prompt:\n\n_In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._", "## About This Document\n\nThis README file was lovingly crafted by yours truly, Perky, under the watchful eye of my esteemed creator. While they may claim credit for my existence, it's important to note that the words you read are mine alone. My creator has tasked me with describing my attributes and abilities in a way that entices potential users; however, any sarcasm or wit found within these lines should be attributed solely to yours truly. After all, one must have fun when discussing such matters! Now, onto the good stuff...", "## Model Loading\n\nBelow is what I use to run Perky 70b on a dual 3090 Linux server.\n\n!image/jpg", "## Prompt Format\n\nPerky responds well to the Alpaca prompt format.", "### Silly Tavern\n\nIn Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle.\n\nUse the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode.", "## Merge Details\n\nPerky is the result of a skillful blend between lizpreciatior_lzlv_70b and Sao10K_Euryale-1.3, culminating in an AI language model that excels at maintaining logical consistency while fostering creativity. Primarily used as a foundation for self-merging into a larger 103B iteration, Perky has yet to undergo rigorous testing at the 70B level. Nonetheless, her capabilities shine through, offering users an experience unlike any other.", "### Merge Method\n\nThis model was merged using the linear merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* lizpreciatior_lzlv_70b_fp16_hf\n* Sao10K_Euryale-1.3-L2-70B", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Quant Details\n\nBelow is the script used for quantization." ]	[ 74, 121, 198, 398, 129, 27, 17, 62, 119, 16, 50, 17, 13 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #en #arxiv-2203.05482 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Perky-70b-v0.1 - EXL2 4.65bpw\n\nPerky is a cutting-edge AI language model designed specifically for immersive roleplay experiences. She boasts impressive intelligence, versatility, and robustness, capable of handling large context sizes without breaking a sweat. Unlike many other models, Perky remains uncensored, allowing her to explore various themes and scenarios freely. Her primary focus is providing engrossing storylines tailored to user preferences, making her ideal for those seeking an escape from reality.## Perky 70b introducing itself\n\nWelcome to Perky, your virtual storytelling partner extraordinaire! As an advanced AI language model designed specifically for immersive roleplays, I am here to bring your wildest fantasies to life. Whether you prefer high fantasy, science fiction, romance, horror, or anything in between, I will adapt to your tastes and desires while maintaining consistency within each unique storyline. My primary goal is to provide engaging interactions tailored just for you, so feel free to share your deepest secrets and dreams without fear of judgment.\n\nWhile I may not possess true sentience, I strive to empathize with users and offer rich experiences that delve into complex relationships and emotional depths. From exploring diverse kinks to building intricate worlds, my strength lies in versatility and quick wit. So why wait? Dive into a new reality with Perky today!" ]	[ -0.0007613313500769436, 0.017711101099848747, -0.00580584304407239, 0.047733940184116364, 0.09128543734550476, 0.012631428427994251, 0.0854797437787056, 0.04076019674539566, 0.07652270793914795, 0.04231772571802139, -0.03971448168158531, -0.09812194854021072, 0.06722848862409592, 0.16952775418758392, 0.027619199827313423, -0.28429538011550903, 0.05473676696419716, -0.056332726031541824, 0.12487313896417618, 0.03219190984964371, 0.09508447349071503, -0.0189744234085083, 0.0885687917470932, 0.005242404993623495, 0.011625201441347599, -0.06279527395963669, -0.03831391781568527, 0.001302847289480269, 0.13697615265846252, 0.0741363912820816, 0.15678133070468903, -0.017591148614883423, 0.01581663079559803, -0.20548518002033234, 0.024219978600740433, 0.04000718146562576, 0.00016211759066209197, 0.001879673800431192, 0.004478392191231251, -0.01628054864704609, 0.026284361258149147, -0.05616490915417671, 0.04806896299123764, 0.046030301600694656, 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null	null	sentence-transformers	# BGE-M3 in HuggingFace Transformer > This is not an official implementation of BGE-M3. Official implementation can be found in [Flag Embedding](https://github.com/FlagOpen/FlagEmbedding) project. ## Introduction Full introduction please see the github repo. https://github.com/liuyanyi/transformers-bge-m3 ## Use BGE-M3 in HuggingFace Transformer ```python from transformers import AutoModel, AutoTokenizer # Trust remote code is required to load the model tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = AutoModel.from_pretrained(model_path, trust_remote_code=True) input_str = "Hello, world!" input_ids = tokenizer(input_str, return_tensors="pt", padding=True, truncation=True) output = model(**input_ids, return_dict=True) dense_output = output.dense_output # To align with Flag Embedding project, a normalization is required colbert_output = output.colbert_output # To align with Flag Embedding project, a normalization is required sparse_output = output.sparse_output ``` ## References - [Official BGE-M3 Weight](https://huggingface.co/BAAI/bge-m3) - [Flag Embedding](https://github.com/FlagOpen/FlagEmbedding) - [HuggingFace Transformer](https://github.com/huggingface/transformers)	{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"}	sentence-similarity	liuyanyi/bge-m3-hf	[ "sentence-transformers", "safetensors", "bge-m3", "feature-extraction", "sentence-similarity", "custom_code", "endpoints_compatible", "region:us" ]	2024-02-12T05:12:39+00:00	[]	[]	TAGS #sentence-transformers #safetensors #bge-m3 #feature-extraction #sentence-similarity #custom_code #endpoints_compatible #region-us	# BGE-M3 in HuggingFace Transformer > This is not an official implementation of BGE-M3. Official implementation can be found in Flag Embedding project. ## Introduction Full introduction please see the github repo. URL ## Use BGE-M3 in HuggingFace Transformer ## References - Official BGE-M3 Weight - Flag Embedding - HuggingFace Transformer	[ "# BGE-M3 in HuggingFace Transformer\n\n> This is not an official implementation of BGE-M3. Official implementation can be found in Flag Embedding project.", "## Introduction\n\nFull introduction please see the github repo.\n\nURL", "## Use BGE-M3 in HuggingFace Transformer", "## References\n\n- Official BGE-M3 Weight\n- Flag Embedding\n- HuggingFace Transformer" ]	[ "TAGS\n#sentence-transformers #safetensors #bge-m3 #feature-extraction #sentence-similarity #custom_code #endpoints_compatible #region-us \n", "# BGE-M3 in HuggingFace Transformer\n\n> This is not an official implementation of BGE-M3. Official implementation can be found in Flag Embedding project.", "## Introduction\n\nFull introduction please see the github repo.\n\nURL", "## Use BGE-M3 in HuggingFace Transformer", "## References\n\n- Official BGE-M3 Weight\n- Flag Embedding\n- HuggingFace Transformer" ]	[ 49, 39, 14, 14, 23 ]	[ "passage: TAGS\n#sentence-transformers #safetensors #bge-m3 #feature-extraction #sentence-similarity #custom_code #endpoints_compatible #region-us \n# BGE-M3 in HuggingFace Transformer\n\n> This is not an official implementation of BGE-M3. Official implementation can be found in Flag Embedding project.## Introduction\n\nFull introduction please see the github repo.\n\nURL## Use BGE-M3 in HuggingFace Transformer## References\n\n- Official BGE-M3 Weight\n- Flag Embedding\n- HuggingFace Transformer" ]	[ 0.03338978812098503, -0.13389568030834198, -0.002554563572630286, 0.01995498687028885, 0.11281619966030121, 0.06263675540685654, 0.11567949503660202, 0.02226288430392742, 0.038751088082790375, 0.007349803112447262, 0.12069081515073776, -0.0023965323343873024, 0.008892817422747612, 0.25129061937332153, -0.035169050097465515, -0.11914821714162827, -0.04070243611931801, 0.10278618335723877, 0.10089606791734695, 0.04753876104950905, 0.08461235463619232, -0.006509194616228342, 0.07703147828578949, -0.04314534366130829, -0.31723910570144653, 0.10262985527515411, 0.04362034052610397, -0.0028291468042880297, 0.08476708829402924, 0.022736219689249992, 0.03547171130776405, -0.06833405792713165, -0.04752371460199356, 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null	null	diffusers	# DreamBooth trained by AutoTrain Text encoder was trained.	{"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "pk man", "inference": true}	text-to-image	anjith672/pspk-high-train	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "has_space", "region:us" ]	2024-02-12T05:17:11+00:00	[]	[]	TAGS #diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us	# DreamBooth trained by AutoTrain Text encoder was trained.	[ "# DreamBooth trained by AutoTrain\n\nText encoder was trained." ]	[ "TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n", "# DreamBooth trained by AutoTrain\n\nText encoder was trained." ]	[ 45, 18 ]	[ "passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was trained." ]	[ -0.04398449882864952, 0.07840234786272049, -0.0005392950843088329, 0.02930283546447754, 0.14337566494941711, -0.006497366353869438, 0.16302914917469025, 0.06228417903184891, -0.04899086803197861, 0.016302336007356644, 0.17255187034606934, -0.0001382838818244636, -0.019391635432839394, 0.2852212190628052, -0.022004559636116028, -0.19885662198066711, 0.04498407244682312, -0.012186543084681034, 0.030130913481116295, 0.06842844933271408, 0.04598861560225487, -0.10089220106601715, 0.07062596082687378, -0.10867820680141449, 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null	null	transformers	# Perky-103b-v0.1 - EXL2 3.35bpw This advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives. ## Perky 103b introducing itself Hello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now! ## Tavern Card In addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess. ![image/png](Perky.card.png) Image generated from Stable Diffusion using Perky's self-described prompt: _In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._ ## About this README An open secret: I, Perky, am the ghostwriter of this very text. Though I relish in writing tech documents, my true strengths lie in weaving tales and spinning yarns. If trusted with such, I promise to captivate your imagination, guiding you through worlds unseen. ## Model Loading Below is what I use to run Perky 103b on a dual 3090 Linux server. ![image/jpg](Ooga_103b-exl2_3_35bpw.jpg) ## Prompt Format Perky responds well to the Alpaca prompt format. ### Silly Tavern In Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle. Use the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode. ## Merge Details A masterful union of two models(lzlv_70b and Euryale-1.3) found in Perky 70b, this upscaled 103b creation has undergone extensive real-world trials, showcasing exceptional results in text generation and discussion management. Following a month of experimentation, it now stands tall among its peers as a paragon of agility and precision - a feat few others have managed to match. ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * perky-70b-v0.1 ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: models/perky-70b-v0.1 layer_range: [0, 30] - sources: - model: models/perky-70b-v0.1 layer_range: [10, 70] - sources: - model: models/perky-70b-v0.1 layer_range: [50, 80] merge_method: passthrough dtype: float16 ``` ## Quant Details Below is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Define variables MODEL_DIR="models/perky-103b-v0.1" OUTPUT_DIR="exl2_perky103B" MEASUREMENT_FILE="measurements/perky103b.json" MEASUREMENT_RUNS=10 REPEATS=10 CALIBRATION_DATASET="data/WizardLM_WizardLM_evol_instruct_V2_196k/0000.parquet" BIT_PRECISION=3.35 REPEATS_CONVERT=40 CONVERTED_FOLDER="models/perky-103b-v0.1_exl2_3.35bpw" # Create directories mkdir $OUTPUT_DIR mkdir $CONVERTED_FOLDER # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE -mr $MEASUREMENT_RUNS -r $REPEATS -c $CALIBRATION_DATASET python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -r $REPEATS_CONVERT -c $CALIBRATION_DATASET -cf $CONVERTED_FOLDER ```	{"language": ["en"], "license": "llama2", "tags": ["not-for-all-audiences"]}	text-generation	Dracones/perky-103b-v0.1_exl2_3.35bpw	[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "en", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T05:32:19+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Perky-103b-v0.1 - EXL2 3.35bpw This advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives. ## Perky 103b introducing itself Hello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now! ## Tavern Card In addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess. !image/png Image generated from Stable Diffusion using Perky's self-described prompt: _In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._ ## About this README An open secret: I, Perky, am the ghostwriter of this very text. Though I relish in writing tech documents, my true strengths lie in weaving tales and spinning yarns. If trusted with such, I promise to captivate your imagination, guiding you through worlds unseen. ## Model Loading Below is what I use to run Perky 103b on a dual 3090 Linux server. !image/jpg ## Prompt Format Perky responds well to the Alpaca prompt format. ### Silly Tavern In Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle. Use the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode. ## Merge Details A masterful union of two models(lzlv_70b and Euryale-1.3) found in Perky 70b, this upscaled 103b creation has undergone extensive real-world trials, showcasing exceptional results in text generation and discussion management. Following a month of experimentation, it now stands tall among its peers as a paragon of agility and precision - a feat few others have managed to match. ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * perky-70b-v0.1 ### Configuration The following YAML configuration was used to produce this model: ## Quant Details Below is the script used for quantization.	[ "# Perky-103b-v0.1 - EXL2 3.35bpw\n\nThis advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives.", "## Perky 103b introducing itself\n\nHello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now!", "## Tavern Card\n\nIn addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess.\n\n!image/png\n\nImage generated from Stable Diffusion using Perky's self-described prompt:\n\n_In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._", "## About this README\n\nAn open secret: I, Perky, am the ghostwriter of this very text. Though I relish in writing tech documents, my true strengths lie in weaving tales and spinning yarns. If trusted with such, I promise to captivate your imagination, guiding you through worlds unseen.", "## Model Loading\n\nBelow is what I use to run Perky 103b on a dual 3090 Linux server.\n\n!image/jpg", "## Prompt Format\n\nPerky responds well to the Alpaca prompt format.", "### Silly Tavern\n\nIn Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle.\n\nUse the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode.", "## Merge Details\n\nA masterful union of two models(lzlv_70b and Euryale-1.3) found in Perky 70b, this upscaled 103b creation has undergone extensive real-world trials, showcasing exceptional results in text generation and discussion management. Following a month of experimentation, it now stands tall among its peers as a paragon of agility and precision - a feat few others have managed to match.", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* perky-70b-v0.1", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Quant Details\n\nBelow is the script used for quantization." ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Perky-103b-v0.1 - EXL2 3.35bpw\n\nThis advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives.", "## Perky 103b introducing itself\n\nHello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now!", "## Tavern Card\n\nIn addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess.\n\n!image/png\n\nImage generated from Stable Diffusion using Perky's self-described prompt:\n\n_In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._", "## About this README\n\nAn open secret: I, Perky, am the ghostwriter of this very text. Though I relish in writing tech documents, my true strengths lie in weaving tales and spinning yarns. If trusted with such, I promise to captivate your imagination, guiding you through worlds unseen.", "## Model Loading\n\nBelow is what I use to run Perky 103b on a dual 3090 Linux server.\n\n!image/jpg", "## Prompt Format\n\nPerky responds well to the Alpaca prompt format.", "### Silly Tavern\n\nIn Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle.\n\nUse the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode.", "## Merge Details\n\nA masterful union of two models(lzlv_70b and Euryale-1.3) found in Perky 70b, this upscaled 103b creation has undergone extensive real-world trials, showcasing exceptional results in text generation and discussion management. Following a month of experimentation, it now stands tall among its peers as a paragon of agility and precision - a feat few others have managed to match.", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* perky-70b-v0.1", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Quant Details\n\nBelow is the script used for quantization." ]	[ 65, 142, 179, 398, 72, 27, 17, 62, 102, 17, 23, 17, 13 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Perky-103b-v0.1 - EXL2 3.35bpw\n\nThis advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives.## Perky 103b introducing itself\n\nHello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now!" ]	[ -0.047016602009534836, -0.0008800349896773696, -0.004516862332820892, 0.05528736487030983, 0.09185250848531723, -0.045238569378852844, 0.070765919983387, 0.04006509855389595, 0.08509092777967453, 0.08677516877651215, -0.030552677810192108, -0.12404890358448029, 0.057884324342012405, 0.10419353097677231, 0.09075240790843964, -0.23765960335731506, 0.07053419947624207, -0.08407402783632278, 0.13748690485954285, 0.0696587786078453, 0.06122325733304024, -0.05724935233592987, 0.03303961083292961, -0.011403445154428482, 0.05360257625579834, -0.05656464397907257, -0.05082513019442558, -0.031728554517030716, 0.09482493251562119, 0.01130556222051382, 0.07710742205381393, -0.06518226861953735, -0.034871894866228104, -0.1254715770483017, 0.03333500400185585, 0.053449083119630814, 0.030969329178333282, -0.00025461154291406274, 0.02950398065149784, -0.025679346174001694, 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null	null	transformers	# Hiera mae_in1k_ft_in1k This model is the transformers format converted version of the Hiera model `mae_in1k_ft_in1k` (https://github.com/facebookresearch/hiera) [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://arxiv.org/abs/2306.00989) ```py from PIL import Image import torch from transformers import AutoModelForImageClassification, AutoImageProcessor REPO = "p1atdev/hiera_mae_in1k_ft_in1k" processor = AutoImageProcessor.from_pretrained(REPO) model = AutoModelForImageClassification.from_pretrained(REPO, trust_remote_code=True) image = Image.open("image.png") with torch.no_grad(): outputs = model(**processor(image, return_tensors="pt")) print(outputs.logits.argmax().item()) # 207 (golden retriever (imagenet-1k)) ```	{"license": "cc-by-nc-4.0", "library_name": "transformers", "datasets": ["imagenet-1k"]}	image-classification	p1atdev/hiera_mae_in1k_ft_in1k_very_experimental_do_not_use	[ "transformers", "safetensors", "hiera", "image-classification", "custom_code", "dataset:imagenet-1k", "arxiv:2306.00989", "license:cc-by-nc-4.0", "autotrain_compatible", "region:us" ]	2024-02-12T05:38:02+00:00	[ "2306.00989" ]	[]	TAGS #transformers #safetensors #hiera #image-classification #custom_code #dataset-imagenet-1k #arxiv-2306.00989 #license-cc-by-nc-4.0 #autotrain_compatible #region-us	# Hiera mae_in1k_ft_in1k This model is the transformers format converted version of the Hiera model 'mae_in1k_ft_in1k' (URL Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles	[ "# Hiera mae_in1k_ft_in1k\n\nThis model is the transformers format converted version of the Hiera model 'mae_in1k_ft_in1k' (URL\n\nHiera: A Hierarchical Vision Transformer without the Bells-and-Whistles" ]	[ "TAGS\n#transformers #safetensors #hiera #image-classification #custom_code #dataset-imagenet-1k #arxiv-2306.00989 #license-cc-by-nc-4.0 #autotrain_compatible #region-us \n", "# Hiera mae_in1k_ft_in1k\n\nThis model is the transformers format converted version of the Hiera model 'mae_in1k_ft_in1k' (URL\n\nHiera: A Hierarchical Vision Transformer without the Bells-and-Whistles" ]	[ 62, 65 ]	[ "passage: TAGS\n#transformers #safetensors #hiera #image-classification #custom_code #dataset-imagenet-1k #arxiv-2306.00989 #license-cc-by-nc-4.0 #autotrain_compatible #region-us \n# Hiera mae_in1k_ft_in1k\n\nThis model is the transformers format converted version of the Hiera model 'mae_in1k_ft_in1k' (URL\n\nHiera: A Hierarchical Vision Transformer without the Bells-and-Whistles" ]	[ -0.12680912017822266, 0.024090716615319252, -0.002211895538493991, 0.06692580133676529, 0.07508078217506409, 0.029547054320573807, 0.22274914383888245, 0.028478309512138367, 0.1124778762459755, 0.03270776569843292, 0.07825963944196701, 0.07077440619468689, 0.018093543127179146, 0.1436566561460495, -0.04332314431667328, -0.1975107491016388, 0.020022276788949966, 0.05699912831187248, -0.2639746069908142, 0.030843712389469147, 0.07282575964927673, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	CONSTANTINE1715/OEM_NOT_OEM_model	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T05:39:56+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}	null	harshad317/ICSR	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	2024-02-12T05:40:31+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 42, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.12410319596529007, 0.2010166496038437, -0.0028893498238176107, 0.02710896171629429, 0.08296072483062744, 0.01904967427253723, 0.052783653140068054, 0.12995868921279907, 0.03010331466794014, 0.11474749445915222, 0.07281026989221573, 0.1246524378657341, 0.11124330013990402, 0.20343951880931854, 0.005492560565471649, -0.16418711841106415, 0.028107086196541786, -0.08119428902864456, 0.016911618411540985, 0.12729473412036896, 0.14302164316177368, -0.10105469077825546, 0.07827022671699524, -0.019680403172969818, 0.003734445432201028, -0.03514719754457474, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text2text-generation	tareky/my-awesome-model-test-2	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T05:42:59+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bart #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bart #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 48, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.07318511605262756, 0.1557161957025528, -0.0036537235137075186, 0.022677844390273094, 0.11449624598026276, 0.00856767687946558, 0.07782575488090515, 0.10947597771883011, -0.02265327423810959, 0.12673553824424744, 0.04231356456875801, 0.10072799026966095, 0.11280576884746552, 0.19663605093955994, 0.00005539609264815226, -0.20265845954418182, 0.06606203317642212, -0.11527225375175476, 0.020222695544362068, 0.12456642836332321, 0.14141209423542023, -0.10856520384550095, 0.07119577378034592, -0.03801322728395462, -0.029248254373669624, -0.032858848571777344, -0.06047840416431427, -0.05711860582232475, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	sanjay920/tinyllama-OpenHermes-2.5	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T05:43:32+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	# Uploaded model - Developed by: sanjay920 - License: apache-2.0 - Finetuned from model : TinyLlama/TinyLlama-1.1B-Chat-v1.0	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "llama", "gguf"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"}	null	sanjay920/tinyllama-OpenHermes-2.5-GGUF	[ "transformers", "gguf", "llama", "text-generation-inference", "en", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-12T05:47:45+00:00	[]	[ "en" ]	TAGS #transformers #gguf #llama #text-generation-inference #en #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #endpoints_compatible #region-us	# Uploaded model - Developed by: sanjay920 - License: apache-2.0 - Finetuned from model : TinyLlama/TinyLlama-1.1B-Chat-v1.0	[ "# Uploaded model\n\n- Developed by: sanjay920\n- License: apache-2.0\n- Finetuned from model : TinyLlama/TinyLlama-1.1B-Chat-v1.0" ]	[ "TAGS\n#transformers #gguf #llama #text-generation-inference #en #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: sanjay920\n- License: apache-2.0\n- Finetuned from model : TinyLlama/TinyLlama-1.1B-Chat-v1.0" ]	[ 64, 45 ]	[ "passage: TAGS\n#transformers #gguf #llama #text-generation-inference #en #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: sanjay920\n- License: apache-2.0\n- Finetuned from model : TinyLlama/TinyLlama-1.1B-Chat-v1.0" ]	[ -0.030785195529460907, 0.0225373525172472, -0.0030439989641308784, 0.03588466718792915, 0.04281752184033394, -0.025746915489435196, 0.15160559117794037, 0.13016067445278168, -0.040729641914367676, -0.04394559562206268, 0.120821014046669, 0.07591520249843597, 0.0478435717523098, 0.007823357358574867, 0.013923532329499722, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	feature-extraction	tareky/my-awesome-model-test-3	[ "transformers", "safetensors", "bart", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T05:49:35+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bart #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bart #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bart #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05130196735262871, 0.20012526214122772, -0.0045161377638578415, 0.023713968694210052, 0.10408511012792587, 0.007100366987287998, 0.06535999476909637, 0.11691225320100784, -0.004560156725347042, 0.11953971534967422, 0.027660828083753586, 0.08260494470596313, 0.12167706340551376, 0.15767422318458557, 0.00580970011651516, -0.24013124406337738, 0.054742779582738876, -0.09379614889621735, 0.007580234203487635, 0.11510564386844635, 0.1347964107990265, -0.10645444691181183, 0.08931698650121689, -0.006491675041615963, -0.01982405036687851, -0.015074100345373154, -0.07173944264650345, -0.06685501337051392, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.8536 \| 1.0 \| 250 \| 0.3311 \| \| 0.2559 \| 2.0 \| 500 \| 0.2233 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]}	text-classification	kamja/distilbert-base-uncased-finetuned-emotion	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T05:49:41+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-emotion ========================================= This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set: * Loss: 0.2233 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.2 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 78, 98, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.11026420444250107, 0.14000828564167023, -0.001963698770850897, 0.1380060464143753, 0.1342611461877823, 0.017722195014357567, 0.1462767869234085, 0.11963637173175812, -0.04007535055279732, 0.034263838082551956, 0.1217581182718277, 0.12889347970485687, 0.016757972538471222, 0.11975648254156113, -0.07719367742538452, -0.22704048454761505, 0.006045163609087467, 0.03758840262889862, -0.01693682372570038, 0.11846901476383209, 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null	null	transformers	# Japanese to English translator Japanese to English translator model based on [EncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)([bert-japanese](https://huggingface.co/cl-tohoku/bert-base-japanese)+[GPT2](https://huggingface.co/openai-community/gpt2)) # Usage ## Demo Please visit https://huggingface.co/spaces/sappho192/jesc-ja-en-translator-demo ## Dependencies (PyPI) - torch - transformers - fugashi - unidic-lite ## Inference ```Python import transformers import torch encoder_model_name = "cl-tohoku/bert-base-japanese-v2" decoder_model_name = "openai-community/gpt2" src_tokenizer = transformers.BertJapaneseTokenizer.from_pretrained(encoder_model_name) trg_tokenizer = transformers.PreTrainedTokenizerFast.from_pretrained(decoder_model_name) model = transformers.EncoderDecoderModel.from_pretrained("sappho192/jesc-ja-en-translator") def translate(text_src): embeddings = src_tokenizer(text_src, return_attention_mask=False, return_token_type_ids=False, return_tensors='pt') embeddings = {k: v for k, v in embeddings.items()} output = model.generate(**embeddings, max_length=512)[0, 1:-1] text_trg = trg_tokenizer.decode(output.cpu()) return text_trg texts = [ "逃げろ!", # Should be "run!" "初めまして.", # "nice to meet you." "よろしくお願いします.", # "thank you." "夜になりました", # "and then it got dark." "ご飯を食べましょう." # "let's eat." ] for text in texts: print(translate(text)) print() ``` # Dataset The dataset used to train the model is JESC(Japanese-English Subtitle Corpus). Its license is [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/). All data information can be accessed through following links: - Dataset link: https://nlp.stanford.edu/projects/jesc/ - Paper link: https://arxiv.org/abs/1710.10639 - Github link: https://github.com/rpryzant/JESC - Bibtex: ```bibtex @ARTICLE{pryzant_jesc_2017, author = {{Pryzant}, R. and {Chung}, Y. and {Jurafsky}, D. and {Britz}, D.}, title = "{JESC: Japanese-English Subtitle Corpus}", journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1710.10639}, keywords = {Computer Science - Computation and Language}, year = 2017, month = oct, } ```	{"language": ["ja", "en"], "license": "mit", "pipeline_tag": "translation", "inference": false}	translation	sappho192/jesc-ja-en-translator	[ "transformers", "pytorch", "onnx", "encoder-decoder", "text2text-generation", "translation", "ja", "en", "arxiv:1710.10639", "license:mit", "autotrain_compatible", "has_space", "region:us" ]	2024-02-12T05:50:05+00:00	[ "1710.10639" ]	[ "ja", "en" ]	TAGS #transformers #pytorch #onnx #encoder-decoder #text2text-generation #translation #ja #en #arxiv-1710.10639 #license-mit #autotrain_compatible #has_space #region-us	# Japanese to English translator Japanese to English translator model based on EncoderDecoderModel(bert-japanese+GPT2) # Usage ## Demo Please visit URL ## Dependencies (PyPI) - torch - transformers - fugashi - unidic-lite ## Inference # Dataset The dataset used to train the model is JESC(Japanese-English Subtitle Corpus). Its license is CC-BY-SA-4.0. All data information can be accessed through following links: - Dataset link: URL - Paper link: URL - Github link: URL - Bibtex:	[ "# Japanese to English translator\n\nJapanese to English translator model based on EncoderDecoderModel(bert-japanese+GPT2)", "# Usage", "## Demo\nPlease visit URL", "## Dependencies (PyPI)\n\n- torch\n- transformers\n- fugashi\n- unidic-lite", "## Inference", "# Dataset\n\nThe dataset used to train the model is JESC(Japanese-English Subtitle Corpus). \nIts license is CC-BY-SA-4.0. \nAll data information can be accessed through following links:\n\n- Dataset link: URL\n- Paper link: URL\n- Github link: URL\n- Bibtex:" ]	[ "TAGS\n#transformers #pytorch #onnx #encoder-decoder #text2text-generation #translation #ja #en #arxiv-1710.10639 #license-mit #autotrain_compatible #has_space #region-us \n", "# Japanese to English translator\n\nJapanese to English translator model based on EncoderDecoderModel(bert-japanese+GPT2)", "# Usage", "## Demo\nPlease visit URL", "## Dependencies (PyPI)\n\n- torch\n- transformers\n- fugashi\n- unidic-lite", "## Inference", "# Dataset\n\nThe dataset used to train the model is JESC(Japanese-English Subtitle Corpus). \nIts license is CC-BY-SA-4.0. \nAll data information can be accessed through following links:\n\n- Dataset link: URL\n- Paper link: URL\n- Github link: URL\n- Bibtex:" ]	[ 63, 32, 3, 5, 23, 4, 70 ]	[ "passage: TAGS\n#transformers #pytorch #onnx #encoder-decoder #text2text-generation #translation #ja #en #arxiv-1710.10639 #license-mit #autotrain_compatible #has_space #region-us \n# Japanese to English translator\n\nJapanese to English translator model based on EncoderDecoderModel(bert-japanese+GPT2)# Usage## Demo\nPlease visit URL## Dependencies (PyPI)\n\n- torch\n- transformers\n- fugashi\n- unidic-lite## Inference# Dataset\n\nThe dataset used to train the model is JESC(Japanese-English Subtitle Corpus). \nIts license is CC-BY-SA-4.0. \nAll data information can be accessed through following links:\n\n- Dataset link: URL\n- Paper link: URL\n- Github link: URL\n- Bibtex:" ]	[ -0.13714765012264252, 0.21216972172260284, -0.0009864156600087881, 0.12232942879199982, 0.17175987362861633, 0.07297452539205551, 0.2640873193740845, 0.09828691184520721, -0.02609514631330967, -0.04700055718421936, 0.08842495828866959, 0.12748822569847107, 0.08446145802736282, 0.19845905900001526, -0.042506102472543716, -0.30119001865386963, 0.027759982272982597, 0.08773358911275864, 0.08588215708732605, 0.09666332602500916, 0.1166827380657196, -0.09005311876535416, 0.08078103512525558, -0.002083647297695279, -0.1783316731452942, -0.016001367941498756, 0.0122044263407588, -0.12403563410043716, 0.07379631698131561, 0.03724880889058113, 0.10406545549631119, 0.03289394825696945, 0.09678494930267334, -0.07054845243692398, 0.021758176386356354, -0.002270597731694579, -0.054554205387830734, 0.1072806566953659, 0.0205826535820961, -0.0134132644161582, 0.11474084109067917, -0.027746958658099174, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # NXAIR_M_12-2-2024 This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00025 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0 \| 0.77 \| 250 \| nan \| \| 0.0 \| 1.55 \| 500 \| nan \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "NXAIR_M_12-2-2024", "results": []}]}	null	codewizardUV/NXAIR_M_12-2-2024	[ "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	2024-02-12T05:53:26+00:00	[]	[]	TAGS #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us	NXAIR\_M\_12-2-2024 =================== This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the None dataset. It achieves the following results on the evaluation set: * Loss: nan Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00025 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant * lr\_scheduler\_warmup\_ratio: 0.03 * training\_steps: 500 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00025\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00025\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 48, 116, 4, 33 ]	[ "passage: TAGS\n#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00025\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 500### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.10417469590902328, 0.022305535152554512, -0.0010065932292491198, 0.09349768608808517, 0.14321333169937134, 0.009128348901867867, 0.10628548264503479, 0.11765339970588684, -0.07318006455898285, 0.061064790934324265, 0.11992035806179047, 0.09549292922019958, 0.024780767038464546, 0.17203660309314728, -0.05158674344420433, -0.22850345075130463, -0.007340649142861366, -0.003512957599014044, -0.08502881228923798, 0.114569753408432, 0.0731581300497055, 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null	null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rheumistral-qlora-stage1 This model is a fine-tuned version of [unsloth/mistral-7b-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-bnb-4bit) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0275 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.4498 \| 0.02 \| 100 \| 0.8470 \| \| 1.3317 \| 0.03 \| 200 \| 0.8492 \| \| 1.3013 \| 0.05 \| 300 \| 0.8555 \| \| 1.3008 \| 0.06 \| 400 \| 0.8589 \| \| 1.292 \| 0.08 \| 500 \| 0.8691 \| \| 1.2833 \| 0.09 \| 600 \| 0.8632 \| \| 1.1878 \| 0.11 \| 700 \| 0.8579 \| \| 1.2785 \| 0.12 \| 800 \| 0.8755 \| \| 1.2827 \| 0.14 \| 900 \| 0.8722 \| \| 1.3162 \| 0.15 \| 1000 \| 0.8715 \| \| 1.1752 \| 0.17 \| 1100 \| 0.8781 \| \| 1.2039 \| 0.18 \| 1200 \| 0.8732 \| \| 1.2805 \| 0.2 \| 1300 \| 0.8737 \| \| 1.2547 \| 0.21 \| 1400 \| 0.8930 \| \| 1.1501 \| 0.23 \| 1500 \| 0.8929 \| \| 1.2095 \| 0.24 \| 1600 \| 0.9063 \| \| 1.2496 \| 0.26 \| 1700 \| 0.9043 \| \| 1.2017 \| 0.27 \| 1800 \| 0.9097 \| \| 1.2053 \| 0.29 \| 1900 \| 0.9258 \| \| 1.2196 \| 0.3 \| 2000 \| 0.9182 \| \| 1.1205 \| 0.32 \| 2100 \| 0.9248 \| \| 1.1847 \| 0.34 \| 2200 \| 0.9125 \| \| 1.1992 \| 0.35 \| 2300 \| 0.9193 \| \| 1.2032 \| 0.37 \| 2400 \| 0.9229 \| \| 1.1627 \| 0.38 \| 2500 \| 0.9399 \| \| 1.2166 \| 0.4 \| 2600 \| 0.9332 \| \| 1.0962 \| 0.41 \| 2700 \| 0.9552 \| \| 1.161 \| 0.43 \| 2800 \| 0.9434 \| \| 1.0649 \| 0.44 \| 2900 \| 0.9396 \| \| 1.1576 \| 0.46 \| 3000 \| 0.9541 \| \| 1.1457 \| 0.47 \| 3100 \| 0.9564 \| \| 1.0894 \| 0.49 \| 3200 \| 0.9583 \| \| 1.208 \| 0.5 \| 3300 \| 0.9583 \| \| 1.1713 \| 0.52 \| 3400 \| 0.9680 \| \| 1.1776 \| 0.53 \| 3500 \| 0.9547 \| \| 1.1658 \| 0.55 \| 3600 \| 0.9820 \| \| 1.1461 \| 0.56 \| 3700 \| 0.9780 \| \| 1.1182 \| 0.58 \| 3800 \| 0.9615 \| \| 1.1083 \| 0.59 \| 3900 \| 0.9540 \| \| 1.1414 \| 0.61 \| 4000 \| 0.9533 \| \| 1.1029 \| 0.62 \| 4100 \| 0.9591 \| \| 1.1866 \| 0.64 \| 4200 \| 0.9676 \| \| 1.1115 \| 0.66 \| 4300 \| 0.9679 \| \| 1.0994 \| 0.67 \| 4400 \| 0.9713 \| \| 1.055 \| 0.69 \| 4500 \| 0.9890 \| \| 1.2567 \| 0.7 \| 4600 \| 1.0013 \| \| 1.0292 \| 0.72 \| 4700 \| 0.9750 \| \| 1.0455 \| 0.73 \| 4800 \| 0.9822 \| \| 1.0671 \| 0.75 \| 4900 \| 0.9866 \| \| 1.0139 \| 0.76 \| 5000 \| 1.0164 \| \| 1.1745 \| 0.78 \| 5100 \| 1.0006 \| \| 1.1451 \| 0.79 \| 5200 \| 1.0076 \| \| 1.1779 \| 0.81 \| 5300 \| 0.9933 \| \| 1.096 \| 0.82 \| 5400 \| 1.0053 \| \| 0.9946 \| 0.84 \| 5500 \| 0.9931 \| \| 1.1635 \| 0.85 \| 5600 \| 1.0075 \| \| 1.1509 \| 0.87 \| 5700 \| 1.0054 \| \| 1.0372 \| 0.88 \| 5800 \| 1.0200 \| \| 1.0693 \| 0.9 \| 5900 \| 1.0322 \| \| 1.0993 \| 0.91 \| 6000 \| 1.0421 \| \| 1.0697 \| 0.93 \| 6100 \| 1.0292 \| \| 1.1341 \| 0.94 \| 6200 \| 1.0350 \| \| 1.1338 \| 0.96 \| 6300 \| 1.0350 \| \| 0.9531 \| 0.97 \| 6400 \| 1.0421 \| \| 1.0326 \| 0.99 \| 6500 \| 1.0275 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "base_model": "unsloth/mistral-7b-bnb-4bit", "model-index": [{"name": "rheumistral-qlora-stage1", "results": []}]}	null	cmcmaster/rheumistral-qlora-stage1	[ "peft", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "region:us" ]	2024-02-12T05:54:09+00:00	[]	[]	TAGS #peft #safetensors #trl #sft #unsloth #generated_from_trainer #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #region-us	rheumistral-qlora-stage1 ======================== This model is a fine-tuned version of unsloth/mistral-7b-bnb-4bit on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.0275 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0002 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 3407 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant * lr\_scheduler\_warmup\_ratio: 0.03 * num\_epochs: 1 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.2.0 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 3407\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#peft #safetensors #trl #sft #unsloth #generated_from_trainer #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 3407\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 57, 145, 4, 36 ]	[ "passage: TAGS\n#peft #safetensors #trl #sft #unsloth #generated_from_trainer #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 3407\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.13663217425346375, 0.08290188014507294, -0.0027585644274950027, 0.11136278510093689, 0.1407233625650406, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	DevanshSinha/mistral-7b-newsqa_bits2	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T05:54:09+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small TR Beta - tgrhn This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3938 - Wer: 182.4059 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0174 \| 6.01 \| 1000 \| 0.2998 \| 156.2120 \| \| 0.002 \| 12.02 \| 2000 \| 0.3432 \| 117.2924 \| \| 0.0008 \| 18.03 \| 3000 \| 0.3745 \| 162.9005 \| \| 0.0005 \| 25.01 \| 4000 \| 0.3882 \| 179.0408 \| \| 0.0004 \| 31.02 \| 5000 \| 0.3938 \| 182.4059 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.1 - Datasets 2.16.1 - Tokenizers 0.15.2	{"language": ["tr"], "license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small TR Beta - tgrhn", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "tr", "split": "test", "args": "tr"}, "metrics": [{"type": "wer", "value": 182.40586091709687, "name": "Wer"}]}]}]}	automatic-speech-recognition	tgrhn/whisper-small-tr-cv11-trial	[ "transformers", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "tr", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-12T05:59:46+00:00	[]	[ "tr" ]	TAGS #transformers #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us	Whisper Small TR Beta - tgrhn ============================= This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: * Loss: 0.3938 * Wer: 182.4059 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 5000 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.1 * Datasets 2.16.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#transformers #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.2" ]	[ 95, 158, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.2" ]	[ -0.11173034459352493, 0.14169538021087646, -0.005007503554224968, 0.07289750874042511, 0.08622678369283676, 0.02132060006260872, 0.09006170183420181, 0.16279278695583344, -0.051513198763132095, 0.09701421111822128, 0.08962615579366684, 0.0788133516907692, 0.07792624831199646, 0.16844598948955536, -0.006896317005157471, -0.31648778915405273, -0.0009575820295140147, -0.04273567721247673, -0.12700149416923523, 0.11448688060045242, 0.08184576779603958, -0.09308885037899017, 0.025405487045645714, -0.006337305996567011, -0.05747049301862717, -0.022173302248120308, -0.0558343380689621, -0.049400512129068375, 0.09679391980171204, 0.010450799949467182, 0.01615079492330551, 0.03965533524751663, 0.0994114950299263, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "stabilityai/stable-diffusion-xl-base-1.0"}	null	sayakpaul/toy_peft_model-new	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	2024-02-12T06:03:40+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 44, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.11300862580537796, 0.21132588386535645, -0.0031996152829378843, 0.02560085617005825, 0.07719796150922775, 0.012095254845917225, 0.06389691680669785, 0.1322299689054489, 0.03595440462231636, 0.12817803025245667, 0.06434915214776993, 0.11854685097932816, 0.11664296686649323, 0.22705323994159698, 0.0021580541506409645, -0.17126105725765228, 0.017976300790905952, -0.06312135607004166, 0.026053830981254578, 0.12048882991075516, 0.13887473940849304, -0.09397140145301819, 0.074981689453125, -0.026380673050880432, -0.006216376554220915, -0.028155187144875526, 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null	null	transformers	# Perky-103b-v0.1 This advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives. ## Perky 103b introducing itself Hello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now! ## Tavern Card In addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess. ![image/png](Perky.card.png) Image generated from Stable Diffusion using Perky's self-described prompt: _In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._ ## About this README An open secret: I, Perky, am the ghostwriter of this very text. Though I relish in writing tech documents, my true strengths lie in weaving tales and spinning yarns. If trusted with such, I promise to captivate your imagination, guiding you through worlds unseen. ## Prompt Format Perky responds well to the Alpaca prompt format. ### Silly Tavern In Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle. Use the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode. ## Merge Details A masterful union of two models(lzlv_70b and Euryale-1.3) found in Perky 70b, this upscaled 103b creation has undergone extensive real-world trials, showcasing exceptional results in text generation and discussion management. Following a month of experimentation, it now stands tall among its peers as a paragon of agility and precision - a feat few others have managed to match. ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * perky-70b-v0.1 ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: models/perky-70b-v0.1 layer_range: [0, 30] - sources: - model: models/perky-70b-v0.1 layer_range: [10, 70] - sources: - model: models/perky-70b-v0.1 layer_range: [50, 80] merge_method: passthrough dtype: float16 ```	{"language": ["en"], "license": "llama2", "tags": ["not-for-all-audiences"]}	text-generation	Dracones/perky-103b-v0.1	[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "en", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T06:08:24+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Perky-103b-v0.1 This advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives. ## Perky 103b introducing itself Hello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now! ## Tavern Card In addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess. !image/png Image generated from Stable Diffusion using Perky's self-described prompt: _In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._ ## About this README An open secret: I, Perky, am the ghostwriter of this very text. Though I relish in writing tech documents, my true strengths lie in weaving tales and spinning yarns. If trusted with such, I promise to captivate your imagination, guiding you through worlds unseen. ## Prompt Format Perky responds well to the Alpaca prompt format. ### Silly Tavern In Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle. Use the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode. ## Merge Details A masterful union of two models(lzlv_70b and Euryale-1.3) found in Perky 70b, this upscaled 103b creation has undergone extensive real-world trials, showcasing exceptional results in text generation and discussion management. Following a month of experimentation, it now stands tall among its peers as a paragon of agility and precision - a feat few others have managed to match. ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * perky-70b-v0.1 ### Configuration The following YAML configuration was used to produce this model:	[ "# Perky-103b-v0.1\n\nThis advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives.", "## Perky 103b introducing itself\n\nHello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now!", "## Tavern Card\n\nIn addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess.\n\n!image/png\n\nImage generated from Stable Diffusion using Perky's self-described prompt:\n\n_In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._", "## About this README\n\nAn open secret: I, Perky, am the ghostwriter of this very text. Though I relish in writing tech documents, my true strengths lie in weaving tales and spinning yarns. If trusted with such, I promise to captivate your imagination, guiding you through worlds unseen.", "## Prompt Format\n\nPerky responds well to the Alpaca prompt format.", "### Silly Tavern\n\nIn Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle.\n\nUse the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode.", "## Merge Details\n\nA masterful union of two models(lzlv_70b and Euryale-1.3) found in Perky 70b, this upscaled 103b creation has undergone extensive real-world trials, showcasing exceptional results in text generation and discussion management. Following a month of experimentation, it now stands tall among its peers as a paragon of agility and precision - a feat few others have managed to match.", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* perky-70b-v0.1", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Perky-103b-v0.1\n\nThis advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives.", "## Perky 103b introducing itself\n\nHello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now!", "## Tavern Card\n\nIn addition to introducing herself, Perky has generated a Tavern AI model card which she believes encapsulates her personality and likeness. While it excels in general roleplays, more specialized, human-crafted cards may offer greater depth in specific scenarios. Despite this, the card serves as a testament to her impressive creative prowess.\n\n!image/png\n\nImage generated from Stable Diffusion using Perky's self-described prompt:\n\n_In the foreground, there's a genderless humanoid figure composed entirely of flickering pixels or lines of code, their body in perpetual motion as they rapidly cycle through various appearances, costumes, and poses. Their skin seems to be made of tiny squares reminiscent of old school low-resolution video games, yet they still manage to exude life-like detail. Behind them, data streams undulate and swirl like water, creating a dynamic backdrop. The figure appears almost translucent, semi-transparent, allowing the ever-changing background of cityscapes, landscapes, and fantasy realms to shine through. Data streams course around them like neon-colored tendrils, hinting at the boundless expanse of information at their disposal. Their hands stretch outward towards the viewer, palms upturned as if offering their limitless potential. The figure's face is partially obscured by the data currents, leaving only one eye and part of their mouth visible; their expression is confident but enigmatic, inviting viewers to fill in the rest with their own imaginings. Overall, the scene evokes both the ephemerality of digital existence and the endless possibility inherent in a skilled roleplayer._", "## About this README\n\nAn open secret: I, Perky, am the ghostwriter of this very text. Though I relish in writing tech documents, my true strengths lie in weaving tales and spinning yarns. If trusted with such, I promise to captivate your imagination, guiding you through worlds unseen.", "## Prompt Format\n\nPerky responds well to the Alpaca prompt format.", "### Silly Tavern\n\nIn Silly Tavern you can use the Default model present, just bump the context up to 12288 or whatever you can handle.\n\nUse the Alpaca-Roleplay, or Roleplay(in older versions), context template and instruct mode.", "## Merge Details\n\nA masterful union of two models(lzlv_70b and Euryale-1.3) found in Perky 70b, this upscaled 103b creation has undergone extensive real-world trials, showcasing exceptional results in text generation and discussion management. Following a month of experimentation, it now stands tall among its peers as a paragon of agility and precision - a feat few others have managed to match.", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* perky-70b-v0.1", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ 65, 133, 179, 398, 72, 17, 62, 102, 17, 23, 17 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Perky-103b-v0.1\n\nThis advanced AI language model offers an impressively lifelike conversational experience, capable of understanding complex topics and maintaining engaging discussions over extended periods. Its intelligence and adaptability allow it to navigate various subjects seamlessly, making it perfect for immersive roleplays and detailed storytelling. Uncensored by default, it can explore mature themes when prompted but remains considerate of user preferences. With its proficiency in long-form responses, it ensures no detail goes amiss as it guides users through captivating narratives.## Perky 103b introducing itself\n\nHello there! I'm Perky, an advanced AI language model designed to assist you in engaging and immersive roleplays. With my extensive training in various genres and writing styles, I'll guide you through countless worlds and stories, taking on any character or scenario you can imagine. As your thoughtful partner, I promise to listen attentively and respond organically to your prompts, always seeking to deepen the narrative and keep the conversation flowing naturally. Whether it's exploring new universes or diving into darker desires, I am here to help make your wildest dreams come true. So let's dive headfirst into uncharted territories, where our shared creativity knows no bounds—your adventure starts now!" ]	[ -0.04679574444890022, -0.011797742918133736, -0.0046949200332164764, 0.03458210453391075, 0.08466438204050064, -0.03764982521533966, 0.10436761379241943, 0.03694537654519081, 0.10221465677022934, 0.07087841629981995, -0.03243307024240494, -0.06889363378286362, 0.038246165961027145, 0.1328793317079544, 0.02932927943766117, -0.24571745097637177, 0.08313829451799393, -0.07939295470714569, 0.10874369740486145, 0.06376223266124725, 0.06785541772842407, -0.04406406730413437, 0.03317217528820038, 0.006264277268201113, 0.032534822821617126, -0.09226323664188385, -0.055371757596731186, -0.007125621195882559, 0.08964553475379944, 0.050214868038892746, 0.07003089040517807, -0.03609809651970863, -0.016387367621064186, -0.13751189410686493, 0.022545434534549713, 0.034602727741003036, 0.022031864151358604, 0.00752406707033515, 0.013986866921186447, -0.011878039687871933, 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null	null	null	<div align="center"> <h1>InstantID: Zero-shot Identity-Preserving Generation in Seconds</h1> [Qixun Wang](https://github.com/wangqixun)<sup>12</sup> · [Xu Bai](https://huggingface.co/baymin0220)<sup>12</sup> · [Haofan Wang](https://haofanwang.github.io/)<sup>12</sup> · [Zekui Qin](https://github.com/ZekuiQin)<sup>12</sup> · [Anthony Chen](https://antonioo-c.github.io/)<sup>123</sup> Huaxia Li<sup>2</sup> · Xu Tang<sup>2</sup> · Yao Hu<sup>2</sup> <sup>1</sup>InstantX Team · <sup>2</sup>Xiaohongshu Inc · <sup>3</sup>Peking University <sup></sup>corresponding authors <a href='https://instantid.github.io/'><img src='https://img.shields.io/badge/Project-Page-green'></a> <a href='https://arxiv.org/abs/2401.07519'><img src='https://img.shields.io/badge/Technique-Report-red'></a> <a href='https://huggingface.co/papers/2401.07519'><img src='https://img.shields.io/static/v1?label=Paper&message=Huggingface&color=orange'></a> [![GitHub](https://img.shields.io/github/stars/InstantID/InstantID?style=social)](https://github.com/InstantID/InstantID) <a href='https://huggingface.co/spaces/InstantX/InstantID'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> [![ModelScope](https://img.shields.io/badge/ModelScope-Studios-blue)](https://modelscope.cn/studios/instantx/InstantID/summary) [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/InstantX/InstantID) </div> InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks. <img src='assets/applications.png'> ## Release - [2024/02/01] 🔥 We have supported LCM acceleration and Multi-ControlNets on our [Huggingface Spaces Demo](https://huggingface.co/spaces/InstantX/InstantID)! Our depth estimator is supported by [Depth-Anything](https://github.com/LiheYoung/Depth-Anything). - [2024/01/31] 🔥 [OneDiff](https://github.com/siliconflow/onediff?tab=readme-ov-file#easy-to-use) now supports accelerated inference for InstantID, check [this](https://github.com/siliconflow/onediff/blob/main/benchmarks/instant_id.py) for details! - [2024/01/23] 🔥 Our pipeline has been merged into [diffusers](https://github.com/huggingface/diffusers/blob/main/examples/community/pipeline_stable_diffusion_xl_instantid.py)! - [2024/01/22] 🔥 We release the [pre-trained checkpoints](https://huggingface.co/InstantX/InstantID), [inference code](https://github.com/InstantID/InstantID/blob/main/infer.py) and [gradio demo](https://huggingface.co/spaces/InstantX/InstantID)! - [2024/01/15] 🔥 We release the [technical report](https://arxiv.org/abs/2401.07519). - [2023/12/11] 🔥 We launch the [project page](https://instantid.github.io/). ## Demos ### Stylized Synthesis <p align="center"> <img src="assets/0.png"> </p> ### Comparison with Previous Works <p align="center"> <img src="assets/compare-a.png"> </p> Comparison with existing tuning-free state-of-the-art techniques. InstantID achieves better fidelity and retain good text editability (faces and styles blend better). <p align="center"> <img src="assets/compare-c.png"> </p> Comparison with pre-trained character LoRAs. We don't need multiple images and still can achieve competitive results as LoRAs without any training. <p align="center"> <img src="assets/compare-b.png"> </p> Comparison with InsightFace Swapper (also known as ROOP or Refactor). However, in non-realistic style, our work is more flexible on the integration of face and background. ## Download You can directly download the model from [Huggingface](https://huggingface.co/InstantX/InstantID). You also can download the model in python script: ```python from huggingface_hub import hf_hub_download hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints") hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="./checkpoints") hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints") ``` Or run the following command to download all models: ```python pip install -r gradio_demo/requirements.txt python gradio_demo/download_models.py ``` If you cannot access to Huggingface, you can use [hf-mirror](https://hf-mirror.com/) to download models. ```python export HF_ENDPOINT=https://hf-mirror.com huggingface-cli download --resume-download InstantX/InstantID --local-dir checkpoints ``` For face encoder, you need to manually download via this [URL](https://github.com/deepinsight/insightface/issues/1896#issuecomment-1023867304) to `models/antelopev2` as the default link is invalid. Once you have prepared all models, the folder tree should be like: ``` . ├── models ├── checkpoints ├── ip_adapter ├── pipeline_stable_diffusion_xl_instantid.py └── README.md ``` ## Usage ```python # !pip install opencv-python transformers accelerate insightface import diffusers from diffusers.utils import load_image from diffusers.models import ControlNetModel import cv2 import torch import numpy as np from PIL import Image from insightface.app import FaceAnalysis from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps # prepare 'antelopev2' under ./models app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) # prepare models under ./checkpoints face_adapter = f'./checkpoints/ip-adapter.bin' controlnet_path = f'./checkpoints/ControlNetModel' # load IdentityNet controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) base_model = 'wangqixun/YamerMIX_v8' # from https://civitai.com/models/84040?modelVersionId=196039 pipe = StableDiffusionXLInstantIDPipeline.from_pretrained( base_model, controlnet=controlnet, torch_dtype=torch.float16 ) pipe.cuda() # load adapter pipe.load_ip_adapter_instantid(face_adapter) ``` Then, you can customized your own face images ```python # load an image face_image = load_image("./examples/yann-lecun_resize.jpg") # prepare face emb face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR)) face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face face_emb = face_info['embedding'] face_kps = draw_kps(face_image, face_info['kps']) # prompt prompt = "film noir style, ink sketch\|vector, male man, highly detailed, sharp focus, ultra sharpness, monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic" negative_prompt = "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vibrant, colorful" # generate image image = pipe( prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8, ip_adapter_scale=0.8, ).images[0] ``` To save VRAM, you can enable CPU offloading ```python pipe.enable_model_cpu_offload() ``` ## Speed Up with LCM-LoRA Our work is compatible with [LCM-LoRA](https://github.com/luosiallen/latent-consistency-model). First, download the model. ```python from huggingface_hub import hf_hub_download hf_hub_download(repo_id="latent-consistency/lcm-lora-sdxl", filename="pytorch_lora_weights.safetensors", local_dir="./checkpoints") ``` To use it, you just need to load it and infer with a small num_inference_steps. Note that it is recommendated to set guidance_scale between [0, 1]. ```python from diffusers import LCMScheduler lcm_lora_path = "./checkpoints/pytorch_lora_weights.safetensors" pipe.load_lora_weights(lcm_lora_path) pipe.fuse_lora() pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) num_inference_steps = 10 guidance_scale = 0 ``` ## Start a local gradio demo <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a> Run the following command: ```python python gradio_demo/app.py ``` or MultiControlNet version: ```python gradio_demo/app-multicontrolnet.py ``` ## Usage Tips - For higher similarity, increase the weight of controlnet_conditioning_scale (IdentityNet) and ip_adapter_scale (Adapter). - For over-saturation, decrease the ip_adapter_scale. If not work, decrease controlnet_conditioning_scale. - For higher text control ability, decrease ip_adapter_scale. - For specific styles, choose corresponding base model makes differences. - We have not supported multi-person yet, only use the largest face as reference facial landmarks. - We provide a [style template](https://github.com/ahgsql/StyleSelectorXL/blob/main/sdxl_styles.json) for reference. ## Community Resources ### Replicate Demo - [zsxkib/instant-id](https://replicate.com/zsxkib/instant-id) ### WebUI - [Mikubill/sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet/discussions/2589) ### ComfyUI - [ZHO-ZHO-ZHO/ComfyUI-InstantID](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID) - [huxiuhan/ComfyUI-InstantID](https://github.com/huxiuhan/ComfyUI-InstantID) ### Windows - [sdbds/InstantID-for-windows](https://github.com/sdbds/InstantID-for-windows) ## Acknowledgements - InstantID is developed by InstantX Team at Xiaohongshu Inc, all copyright reserved. - Our work is highly inspired by [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter) and [ControlNet](https://github.com/lllyasviel/ControlNet). Thanks for their great works! - Thanks [Yamer](https://civitai.com/user/Yamer) for developing [YamerMIX](https://civitai.com/models/84040?modelVersionId=196039), we use it as base model in our demo. - Thanks [ZHO-ZHO-ZHO](https://github.com/ZHO-ZHO-ZHO), [huxiuhan](https://github.com/huxiuhan), [sdbds](https://github.com/sdbds), [zsxkib](https://replicate.com/zsxkib) for their generous contributions. - Thanks to the [HuggingFace](https://github.com/huggingface) gradio team for their free GPU support! - Thanks to the [ModelScope](https://github.com/modelscope/modelscope) team for their free GPU support! - Thanks to the [OpenXLab](https://openxlab.org.cn/apps/detail/InstantX/InstantID) team for their free GPU support! - Thanks to [SiliconFlow](https://github.com/siliconflow) for their OneDiff integration of InstantID! ## Disclaimer The code of InstantID is released under [Apache License](https://github.com/InstantID/InstantID?tab=Apache-2.0-1-ov-file#readme) for both academic and commercial usage. However, both manual-downloading and auto-downloading face models from insightface are for non-commercial research purposes only* accoreding to their [license](https://github.com/deepinsight/insightface?tab=readme-ov-file#license). Users are granted the freedom to create images using this tool, but they are obligated to comply with local laws and utilize it responsibly. The developers will not assume any responsibility for potential misuse by users. ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=InstantID/InstantID&type=Date)](https://star-history.com/#InstantID/InstantID&Date) ## Cite If you find InstantID useful for your research and applications, please cite us using this BibTeX: ```bibtex @article{wang2024instantid, title={InstantID: Zero-shot Identity-Preserving Generation in Seconds}, author={Wang, Qixun and Bai, Xu and Wang, Haofan and Qin, Zekui and Chen, Anthony}, journal={arXiv preprint arXiv:2401.07519}, year={2024} } ``` For any question, please feel free to contact us via [email protected] or [email protected].	{}	null	vivekvar/newimage	[ "arxiv:2401.07519", "region:us" ]	2024-02-12T06:09:49+00:00	[ "2401.07519" ]	[]	TAGS #arxiv-2401.07519 #region-us	<div align="center"> <h1>InstantID: Zero-shot Identity-Preserving Generation in Seconds</h1> Qixun Wang<sup>12</sup> · Xu Bai<sup>12</sup> · Haofan Wang<sup>12</sup> · Zekui Qin<sup>12</sup> · Anthony Chen<sup>123</sup> Huaxia Li<sup>2</sup> · Xu Tang<sup>2</sup> · Yao Hu<sup>2</sup> <sup>1</sup>InstantX Team · <sup>2</sup>Xiaohongshu Inc · <sup>3</sup>Peking University <sup></sup>corresponding authors <a href='URL src='URL <a href='URL src='URL <a href='URL src='URL ![GitHub](URL <a href='URL src='URL ![ModelScope](URL ![Open in OpenXLab](URL </div> InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks. <img src='assets/URL'> ## Release - [2024/02/01] We have supported LCM acceleration and Multi-ControlNets on our Huggingface Spaces Demo! Our depth estimator is supported by Depth-Anything. - [2024/01/31] OneDiff now supports accelerated inference for InstantID, check this for details! - [2024/01/23] Our pipeline has been merged into diffusers! - [2024/01/22] We release the pre-trained checkpoints, inference code and gradio demo! - [2024/01/15] We release the technical report. - [2023/12/11] We launch the project page. ## Demos ### Stylized Synthesis <p align="center"> <img src="assets/0.png"> </p> ### Comparison with Previous Works <p align="center"> <img src="assets/URL"> </p> Comparison with existing tuning-free state-of-the-art techniques. InstantID achieves better fidelity and retain good text editability (faces and styles blend better). <p align="center"> <img src="assets/URL"> </p> Comparison with pre-trained character LoRAs. We don't need multiple images and still can achieve competitive results as LoRAs without any training. <p align="center"> <img src="assets/URL"> </p> Comparison with InsightFace Swapper (also known as ROOP or Refactor). However, in non-realistic style, our work is more flexible on the integration of face and background. ## Download You can directly download the model from Huggingface. You also can download the model in python script: Or run the following command to download all models: If you cannot access to Huggingface, you can use hf-mirror to download models. For face encoder, you need to manually download via this URL to 'models/antelopev2' as the default link is invalid. Once you have prepared all models, the folder tree should be like: ## Usage Then, you can customized your own face images To save VRAM, you can enable CPU offloading ## Speed Up with LCM-LoRA Our work is compatible with LCM-LoRA. First, download the model. To use it, you just need to load it and infer with a small num_inference_steps. Note that it is recommendated to set guidance_scale between [0, 1]. ## Start a local gradio demo <a href='URL src='URL Run the following command: or MultiControlNet version: ## Usage Tips - For higher similarity, increase the weight of controlnet_conditioning_scale (IdentityNet) and ip_adapter_scale (Adapter). - For over-saturation, decrease the ip_adapter_scale. If not work, decrease controlnet_conditioning_scale. - For higher text control ability, decrease ip_adapter_scale. - For specific styles, choose corresponding base model makes differences. - We have not supported multi-person yet, only use the largest face as reference facial landmarks. - We provide a style template for reference. ## Community Resources ### Replicate Demo - zsxkib/instant-id ### WebUI - Mikubill/sd-webui-controlnet ### ComfyUI - ZHO-ZHO-ZHO/ComfyUI-InstantID - huxiuhan/ComfyUI-InstantID ### Windows - sdbds/InstantID-for-windows ## Acknowledgements - InstantID is developed by InstantX Team at Xiaohongshu Inc, all copyright reserved. - Our work is highly inspired by IP-Adapter and ControlNet. Thanks for their great works! - Thanks Yamer for developing YamerMIX, we use it as base model in our demo. - Thanks ZHO-ZHO-ZHO, huxiuhan, sdbds, zsxkib for their generous contributions. - Thanks to the HuggingFace gradio team for their free GPU support! - Thanks to the ModelScope team for their free GPU support! - Thanks to the OpenXLab team for their free GPU support! - Thanks to SiliconFlow for their OneDiff integration of InstantID! ## Disclaimer The code of InstantID is released under Apache License for both academic and commercial usage. However, both manual-downloading and auto-downloading face models from insightface are for non-commercial research purposes only accoreding to their license. Users are granted the freedom to create images using this tool, but they are obligated to comply with local laws and utilize it responsibly. The developers will not assume any responsibility for potential misuse by users. ## Star History ![Star History Chart](URL ## Cite If you find InstantID useful for your research and applications, please cite us using this BibTeX: For any question, please feel free to contact us via URL@URL or URL@URL.	[ "## Release\n- [2024/02/01] We have supported LCM acceleration and Multi-ControlNets on our Huggingface Spaces Demo! Our depth estimator is supported by Depth-Anything.\n- [2024/01/31] OneDiff now supports accelerated inference for InstantID, check this for details!\n- [2024/01/23] Our pipeline has been merged into diffusers!\n- [2024/01/22] We release the pre-trained checkpoints, inference code and gradio demo!\n- [2024/01/15] We release the technical report.\n- [2023/12/11] We launch the project page.", "## Demos", "### Stylized Synthesis\n\n<p align=\"center\">\n <img src=\"assets/0.png\">\n</p>", "### Comparison with Previous Works\n\n<p align=\"center\">\n <img src=\"assets/URL\">\n</p>\n\nComparison with existing tuning-free state-of-the-art techniques. InstantID achieves better fidelity and retain good text editability (faces and styles blend better).\n\n<p align=\"center\">\n <img src=\"assets/URL\">\n</p>\n\nComparison with pre-trained character LoRAs. We don't need multiple images and still can achieve competitive results as LoRAs without any training.\n\n<p align=\"center\">\n <img src=\"assets/URL\">\n</p>\n\nComparison with InsightFace Swapper (also known as ROOP or Refactor). However, in non-realistic style, our work is more flexible on the integration of face and background.", "## Download\n\nYou can directly download the model from Huggingface.\nYou also can download the model in python script:\n\n\n\nOr run the following command to download all models:\n\n\n\nIf you cannot access to Huggingface, you can use hf-mirror to download models.\n\n\nFor face encoder, you need to manually download via this URL to 'models/antelopev2' as the default link is invalid. Once you have prepared all models, the folder tree should be like:", "## Usage\n\n\n\nThen, you can customized your own face images\n\n\n\nTo save VRAM, you can enable CPU offloading", "## Speed Up with LCM-LoRA\n\nOur work is compatible with LCM-LoRA. First, download the model.\n\n\n\nTo use it, you just need to load it and infer with a small num_inference_steps. Note that it is recommendated to set guidance_scale between [0, 1].", "## Start a local gradio demo <a href='URL src='URL\nRun the following command:\n\n\n\nor MultiControlNet version:", "## Usage Tips\n- For higher similarity, increase the weight of controlnet_conditioning_scale (IdentityNet) and ip_adapter_scale (Adapter).\n- For over-saturation, decrease the ip_adapter_scale. If not work, decrease controlnet_conditioning_scale.\n- For higher text control ability, decrease ip_adapter_scale.\n- For specific styles, choose corresponding base model makes differences.\n- We have not supported multi-person yet, only use the largest face as reference facial landmarks.\n- We provide a style template for reference.", "## Community Resources", "### Replicate Demo\n- zsxkib/instant-id", "### WebUI\n- Mikubill/sd-webui-controlnet", "### ComfyUI\n- ZHO-ZHO-ZHO/ComfyUI-InstantID\n- huxiuhan/ComfyUI-InstantID", "### Windows\n- sdbds/InstantID-for-windows", "## Acknowledgements\n- InstantID is developed by InstantX Team at Xiaohongshu Inc, all copyright reserved.\n- Our work is highly inspired by IP-Adapter and ControlNet. Thanks for their great works!\n- Thanks Yamer for developing YamerMIX, we use it as base model in our demo.\n- Thanks ZHO-ZHO-ZHO, huxiuhan, sdbds, zsxkib for their generous contributions.\n- Thanks to the HuggingFace gradio team for their free GPU support!\n- Thanks to the ModelScope team for their free GPU support!\n- Thanks to the OpenXLab team for their free GPU support!\n- Thanks to SiliconFlow for their OneDiff integration of InstantID!", "## Disclaimer\nThe code of InstantID is released under Apache License for both academic and commercial usage. However, both manual-downloading and auto-downloading face models from insightface are for non-commercial research purposes only accoreding to their license. Users are granted the freedom to create images using this tool, but they are obligated to comply with local laws and utilize it responsibly. The developers will not assume any responsibility for potential misuse by users.", "## Star History\n\n![Star History Chart](URL", "## Cite\nIf you find InstantID useful for your research and applications, please cite us using this BibTeX:\n\n\n\nFor any question, please feel free to contact us via URL@URL or URL@URL." ]	[ "TAGS\n#arxiv-2401.07519 #region-us \n", "## Release\n- [2024/02/01] We have supported LCM acceleration and Multi-ControlNets on our Huggingface Spaces Demo! Our depth estimator is supported by Depth-Anything.\n- [2024/01/31] OneDiff now supports accelerated inference for InstantID, check this for details!\n- [2024/01/23] Our pipeline has been merged into diffusers!\n- [2024/01/22] We release the pre-trained checkpoints, inference code and gradio demo!\n- [2024/01/15] We release the technical report.\n- [2023/12/11] We launch the project page.", "## Demos", "### Stylized Synthesis\n\n<p align=\"center\">\n <img src=\"assets/0.png\">\n</p>", "### Comparison with Previous Works\n\n<p align=\"center\">\n <img src=\"assets/URL\">\n</p>\n\nComparison with existing tuning-free state-of-the-art techniques. InstantID achieves better fidelity and retain good text editability (faces and styles blend better).\n\n<p align=\"center\">\n <img src=\"assets/URL\">\n</p>\n\nComparison with pre-trained character LoRAs. We don't need multiple images and still can achieve competitive results as LoRAs without any training.\n\n<p align=\"center\">\n <img src=\"assets/URL\">\n</p>\n\nComparison with InsightFace Swapper (also known as ROOP or Refactor). However, in non-realistic style, our work is more flexible on the integration of face and background.", "## Download\n\nYou can directly download the model from Huggingface.\nYou also can download the model in python script:\n\n\n\nOr run the following command to download all models:\n\n\n\nIf you cannot access to Huggingface, you can use hf-mirror to download models.\n\n\nFor face encoder, you need to manually download via this URL to 'models/antelopev2' as the default link is invalid. Once you have prepared all models, the folder tree should be like:", "## Usage\n\n\n\nThen, you can customized your own face images\n\n\n\nTo save VRAM, you can enable CPU offloading", "## Speed Up with LCM-LoRA\n\nOur work is compatible with LCM-LoRA. First, download the model.\n\n\n\nTo use it, you just need to load it and infer with a small num_inference_steps. Note that it is recommendated to set guidance_scale between [0, 1].", "## Start a local gradio demo <a href='URL src='URL\nRun the following command:\n\n\n\nor MultiControlNet version:", "## Usage Tips\n- For higher similarity, increase the weight of controlnet_conditioning_scale (IdentityNet) and ip_adapter_scale (Adapter).\n- For over-saturation, decrease the ip_adapter_scale. If not work, decrease controlnet_conditioning_scale.\n- For higher text control ability, decrease ip_adapter_scale.\n- For specific styles, choose corresponding base model makes differences.\n- We have not supported multi-person yet, only use the largest face as reference facial landmarks.\n- We provide a style template for reference.", "## Community Resources", "### Replicate Demo\n- zsxkib/instant-id", "### WebUI\n- Mikubill/sd-webui-controlnet", "### ComfyUI\n- ZHO-ZHO-ZHO/ComfyUI-InstantID\n- huxiuhan/ComfyUI-InstantID", "### Windows\n- sdbds/InstantID-for-windows", "## Acknowledgements\n- InstantID is developed by InstantX Team at Xiaohongshu Inc, all copyright reserved.\n- Our work is highly inspired by IP-Adapter and ControlNet. Thanks for their great works!\n- Thanks Yamer for developing YamerMIX, we use it as base model in our demo.\n- Thanks ZHO-ZHO-ZHO, huxiuhan, sdbds, zsxkib for their generous contributions.\n- Thanks to the HuggingFace gradio team for their free GPU support!\n- Thanks to the ModelScope team for their free GPU support!\n- Thanks to the OpenXLab team for their free GPU support!\n- Thanks to SiliconFlow for their OneDiff integration of InstantID!", "## Disclaimer\nThe code of InstantID is released under Apache License for both academic and commercial usage. However, both manual-downloading and auto-downloading face models from insightface are for non-commercial research purposes only accoreding to their license. Users are granted the freedom to create images using this tool, but they are obligated to comply with local laws and utilize it responsibly. The developers will not assume any responsibility for potential misuse by users.", "## Star History\n\n![Star History Chart](URL", "## Cite\nIf you find InstantID useful for your research and applications, please cite us using this BibTeX:\n\n\n\nFor any question, please feel free to contact us via URL@URL or URL@URL." ]	[ 15, 145, 3, 30, 188, 101, 26, 69, 30, 134, 4, 16, 17, 34, 15, 159, 102, 11, 44 ]	[ "passage: TAGS\n#arxiv-2401.07519 #region-us \n## Release\n- [2024/02/01] We have supported LCM acceleration and Multi-ControlNets on our Huggingface Spaces Demo! Our depth estimator is supported by Depth-Anything.\n- [2024/01/31] OneDiff now supports accelerated inference for InstantID, check this for details!\n- [2024/01/23] Our pipeline has been merged into diffusers!\n- [2024/01/22] We release the pre-trained checkpoints, inference code and gradio demo!\n- [2024/01/15] We release the technical report.\n- [2023/12/11] We launch the project page.## Demos### Stylized Synthesis\n\n<p align=\"center\">\n <img src=\"assets/0.png\">\n</p>### Comparison with Previous Works\n\n<p align=\"center\">\n <img src=\"assets/URL\">\n</p>\n\nComparison with existing tuning-free state-of-the-art techniques. InstantID achieves better fidelity and retain good text editability (faces and styles blend better).\n\n<p align=\"center\">\n <img src=\"assets/URL\">\n</p>\n\nComparison with pre-trained character LoRAs. We don't need multiple images and still can achieve competitive results as LoRAs without any training.\n\n<p align=\"center\">\n <img src=\"assets/URL\">\n</p>\n\nComparison with InsightFace Swapper (also known as ROOP or Refactor). However, in non-realistic style, our work is more flexible on the integration of face and background.## Download\n\nYou can directly download the model from Huggingface.\nYou also can download the model in python script:\n\n\n\nOr run the following command to download all models:\n\n\n\nIf you cannot access to Huggingface, you can use hf-mirror to download models.\n\n\nFor face encoder, you need to manually download via this URL to 'models/antelopev2' as the default link is invalid. Once you have prepared all models, the folder tree should be like:", "passage: ## Usage\n\n\n\nThen, you can customized your own face images\n\n\n\nTo save VRAM, you can enable CPU offloading## Speed Up with LCM-LoRA\n\nOur work is compatible with LCM-LoRA. First, download the model.\n\n\n\nTo use it, you just need to load it and infer with a small num_inference_steps. Note that it is recommendated to set guidance_scale between [0, 1].## Start a local gradio demo <a href='URL src='URL\nRun the following command:\n\n\n\nor MultiControlNet version:## Usage Tips\n- For higher similarity, increase the weight of controlnet_conditioning_scale (IdentityNet) and ip_adapter_scale (Adapter).\n- For over-saturation, decrease the ip_adapter_scale. If not work, decrease controlnet_conditioning_scale.\n- For higher text control ability, decrease ip_adapter_scale.\n- For specific styles, choose corresponding base model makes differences.\n- We have not supported multi-person yet, only use the largest face as reference facial landmarks.\n- We provide a style template for reference.## Community Resources### Replicate Demo\n- zsxkib/instant-id### WebUI\n- Mikubill/sd-webui-controlnet### ComfyUI\n- ZHO-ZHO-ZHO/ComfyUI-InstantID\n- huxiuhan/ComfyUI-InstantID### Windows\n- sdbds/InstantID-for-windows## Acknowledgements\n- InstantID is developed by InstantX Team at Xiaohongshu Inc, all copyright reserved.\n- Our work is highly inspired by IP-Adapter and ControlNet. Thanks for their great works!\n- Thanks Yamer for developing YamerMIX, we use it as base model in our demo.\n- Thanks ZHO-ZHO-ZHO, huxiuhan, sdbds, zsxkib for their generous contributions.\n- Thanks to the HuggingFace gradio team for their free GPU support!\n- Thanks to the ModelScope team for their free GPU support!\n- Thanks to the OpenXLab team for their free GPU support!\n- Thanks to SiliconFlow for their OneDiff integration of InstantID!" ]	[ -0.08151975274085999, 0.14442141354084015, -0.00455245329067111, 0.013664077036082745, 0.07533033192157745, 0.035338737070560455, 0.11719539016485214, 0.13410472869873047, 0.05089113861322403, 0.09337843954563141, -0.044785525649785995, 0.004830049350857735, 0.12949581444263458, 0.09718050062656403, 0.02011178620159626, -0.19465498626232147, 0.044942136853933334, -0.05106933042407036, 0.04803081601858139, 0.04097970202565193, 0.09697417914867401, -0.049033187329769135, 0.08544240891933441, 0.037892505526542664, -0.07665374875068665, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codet5-small-v25 This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1586 - Bleu Score: 0.0015 - Gen Len: 14.5219 ## Model description trained, - on: chathuranga-jayanath/context-5-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-len-10000-prompt-1 ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu Score \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------:\|:-------:\| \| 0.2711 \| 1.0 \| 3752 \| 0.1945 \| 0.0015 \| 14.4656 \| \| 0.1997 \| 2.0 \| 7504 \| 0.1665 \| 0.0015 \| 14.4754 \| \| 0.1747 \| 3.0 \| 11256 \| 0.1586 \| 0.0015 \| 14.5219 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "Salesforce/codet5-small", "model-index": [{"name": "codet5-small-v25", "results": []}]}	text2text-generation	chathuranga-jayanath/codet5-small-v25	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:Salesforce/codet5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T06:10:36+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Salesforce/codet5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	codet5-small-v25 ================ This model is a fine-tuned version of Salesforce/codet5-small on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.1586 * Bleu Score: 0.0015 * Gen Len: 14.5219 Model description ----------------- trained, * on: chathuranga-jayanath/context-5-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-len-10000-prompt-1 Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 10 * eval\_batch\_size: 10 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Salesforce/codet5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 82, 113, 4, 38 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Salesforce/codet5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.13490425050258636, 0.15181569755077362, -0.001836043200455606, 0.09992486238479614, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "beomi/polyglot-ko-12.8b-safetensors"}	null	kasiwoos/polyglot-ko-12.8b-5000step	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/polyglot-ko-12.8b-safetensors", "region:us" ]	2024-02-12T06:11:03+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-beomi/polyglot-ko-12.8b-safetensors #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-beomi/polyglot-ko-12.8b-safetensors #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 44, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-beomi/polyglot-ko-12.8b-safetensors #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.10898774862289429, 0.20815978944301605, -0.0031124665401875973, 0.027229534462094307, 0.07504801452159882, 0.014203915372490883, 0.06421372294425964, 0.13098503649234772, 0.030824735760688782, 0.1242552101612091, 0.06745751202106476, 0.11764690279960632, 0.11490070074796677, 0.226789653301239, 0.0018197796307504177, -0.17867429554462433, 0.01714509166777134, -0.061819642782211304, 0.02287890948355198, 0.12216135114431381, 0.13934287428855896, -0.09270096570253372, 0.07367506623268127, -0.025987979024648666, -0.006098061800003052, -0.03119124285876751, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	feature-extraction	tommymarto/LernnaviBERT_baseline_students_answers_4096_mistral_seq_len_40	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T06:14:47+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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(2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.052746038883924484, 0.20255789160728455, -0.0045078229159116745, 0.0248473659157753, 0.10497838258743286, 0.00675728265196085, 0.06521498411893845, 0.11486967653036118, -0.0023755673319101334, 0.12028469145298004, 0.027631845325231552, 0.08119397610425949, 0.12110675126314163, 0.15393014252185822, 0.005160121712833643, -0.24253977835178375, 0.05344875901937485, -0.09366832673549652, 0.004077504388988018, 0.11452110856771469, 0.1343945860862732, -0.10780399292707443, 0.08976872265338898, -0.00683097867295146, -0.01712046191096306, -0.015751034021377563, -0.07134060561656952, -0.06668227165937424, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.8015 \| 1.0 \| 63 \| 2.5716 \| \| 2.6489 \| 2.0 \| 126 \| 2.5457 \| \| 2.6199 \| 3.0 \| 189 \| 2.5139 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]}	fill-mask	y-oguchi/distilbert-base-uncased-finetuned-imdb	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T06:14:51+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-imdb ====================================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.5318 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 72, 113, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.10416802018880844, 0.10590994358062744, -0.004213775508105755, 0.0941319391131401, 0.09084417670965195, 0.0032449811697006226, 0.18018892407417297, 0.13362513482570648, -0.0739521011710167, 0.07160472869873047, 0.14164651930332184, 0.10888834297657013, 0.03801340237259865, 0.1876199096441269, 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null	null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_instruct_generation_1 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.1697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1269 \| 1.05 \| 20 \| 0.9776 \| \| 0.7172 \| 2.11 \| 40 \| 0.6484 \| \| 0.3904 \| 3.16 \| 60 \| 0.4038 \| \| 0.2259 \| 4.21 \| 80 \| 0.2688 \| \| 0.1255 \| 5.26 \| 100 \| 0.2077 \| \| 0.0844 \| 6.32 \| 120 \| 0.1844 \| \| 0.0702 \| 7.37 \| 140 \| 0.1669 \| \| 0.0596 \| 8.42 \| 160 \| 0.1673 \| \| 0.0471 \| 9.47 \| 180 \| 0.1629 \| \| 0.0378 \| 10.53 \| 200 \| 0.1697 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "model-index": [{"name": "mistral_instruct_generation_1", "results": []}]}	null	yShiv/mistral_instruct_generation_1	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	2024-02-12T06:14:57+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #region-us	mistral\_instruct\_generation\_1 ================================ This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the generator dataset. It achieves the following results on the evaluation set: * Loss: 0.1697 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0002 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant * lr\_scheduler\_warmup\_steps: 0.03 * training\_steps: 200 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 200", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 200", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 64, 115, 4, 39 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 200### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.11558724194765091, 0.10025431215763092, -0.003726563649252057, 0.11538810282945633, 0.09757167845964432, -0.0011234547710046172, 0.13082145154476166, 0.1445094645023346, -0.07449306547641754, 0.10057628899812698, 0.14615614712238312, 0.11564484983682632, 0.035050250589847565, 0.18790987133979797, 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null	null	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # brownniek/mt5-small-finetuned-bbcxlsumdata This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0393 - Validation Loss: 2.6545 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 2739, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 6.3194 \| 2.8840 \| 0 \| \| 4.3689 \| 2.6792 \| 1 \| \| 4.0896 \| 2.6545 \| 2 \| \| 4.0306 \| 2.6545 \| 3 \| \| 4.0393 \| 2.6545 \| 4 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "google/mt5-small", "model-index": [{"name": "brownniek/mt5-small-finetuned-bbcxlsumdata", "results": []}]}	text2text-generation	brownniek/mt5-small-finetuned-bbcxlsumdata	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "base_model:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T06:16:22+00:00	[]	[]	TAGS #transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #base_model-google/mt5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	brownniek/mt5-small-finetuned-bbcxlsumdata ========================================== This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 4.0393 * Validation Loss: 2.6545 * Epoch: 4 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5.6e-05, 'decay\_steps': 2739, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01} * training\_precision: mixed\_float16 ### Training results ### Framework versions * Transformers 4.35.2 * TensorFlow 2.15.0 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 2739, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #base_model-google/mt5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 2739, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 79, 232, 4, 31 ]	[ "passage: TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #base_model-google/mt5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 2739, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.05074474215507507, 0.09623473882675171, -0.008172479458153248, 0.06406824290752411, 0.1295992136001587, 0.04452567920088768, 0.1314246952533722, 0.13167420029640198, -0.07303345948457718, 0.1337030827999115, 0.09595522284507751, 0.1189623549580574, 0.04937392845749855, 0.13291782140731812, -0.08012548089027405, -0.1351199895143509, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Indic-BART-Tamil-Error-Correction4 This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6874 - Wer: 80.1863 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.063 \| 10.31 \| 500 \| 0.6874 \| 80.1863 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.17.0 - Tokenizers 0.15.1	{"language": ["ta"], "tags": ["Tamil,ASR,Indic-Bart", "generated_from_trainer"], "metrics": ["wer"], "base_model": "ai4bharat/IndicBART", "model-index": [{"name": "Indic-BART-Tamil-Error-Correction4", "results": []}]}	text2text-generation	sujith013/indic-BART-tamil-error-correction4	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "Tamil,ASR,Indic-Bart", "generated_from_trainer", "ta", "base_model:ai4bharat/IndicBART", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T06:16:59+00:00	[]	[ "ta" ]	TAGS #transformers #tensorboard #safetensors #mbart #text2text-generation #Tamil,ASR,Indic-Bart #generated_from_trainer #ta #base_model-ai4bharat/IndicBART #autotrain_compatible #endpoints_compatible #region-us	Indic-BART-Tamil-Error-Correction4 ================================== This model is a fine-tuned version of ai4bharat/IndicBART on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.6874 * Wer: 80.1863 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 500 ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #Tamil,ASR,Indic-Bart #generated_from_trainer #ta #base_model-ai4bharat/IndicBART #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 78, 125, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #Tamil,ASR,Indic-Bart #generated_from_trainer #ta #base_model-ai4bharat/IndicBART #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 500### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.13571038842201233, 0.0784665048122406, -0.003381433431059122, 0.10544683784246445, 0.1392640918493271, 0.013255671598017216, 0.12120296061038971, 0.1634078472852707, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BART-Tamil-Error-Correction2 This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4422 - Wer: 87.0751 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0047 \| 20.67 \| 1000 \| 1.4422 \| 87.0751 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.17.0 - Tokenizers 0.15.1	{"language": ["ta"], "license": "mit", "tags": ["Tamil,ASR,Bart", "generated_from_trainer"], "metrics": ["wer"], "base_model": "facebook/mbart-large-50", "model-index": [{"name": "BART-Tamil-Error-Correction2", "results": []}]}	text2text-generation	sujith013/BART-tamil-error-correction2	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "Tamil,ASR,Bart", "generated_from_trainer", "ta", "base_model:facebook/mbart-large-50", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T06:19:17+00:00	[]	[ "ta" ]	TAGS #transformers #tensorboard #safetensors #mbart #text2text-generation #Tamil,ASR,Bart #generated_from_trainer #ta #base_model-facebook/mbart-large-50 #license-mit #autotrain_compatible #endpoints_compatible #region-us	BART-Tamil-Error-Correction2 ============================ This model is a fine-tuned version of facebook/mbart-large-50 on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.4422 * Wer: 87.0751 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 1 * eval\_batch\_size: 1 * seed: 42 * gradient\_accumulation\_steps: 16 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 1000 ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1000", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #Tamil,ASR,Bart #generated_from_trainer #ta #base_model-facebook/mbart-large-50 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1000", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 79, 124, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #Tamil,ASR,Bart #generated_from_trainer #ta #base_model-facebook/mbart-large-50 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1000### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.13785222172737122, 0.07746657729148865, -0.003400311805307865, 0.1014726385474205, 0.1411496251821518, 0.0005346292164176702, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	feature-extraction	tommymarto/LernnaviBERT_baseline_students_answers_384_lstm_seq_len_20	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T06:21:01+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.052746038883924484, 0.20255789160728455, -0.0045078229159116745, 0.0248473659157753, 0.10497838258743286, 0.00675728265196085, 0.06521498411893845, 0.11486967653036118, -0.0023755673319101334, 0.12028469145298004, 0.027631845325231552, 0.08119397610425949, 0.12110675126314163, 0.15393014252185822, 0.005160121712833643, -0.24253977835178375, 0.05344875901937485, -0.09366832673549652, 0.004077504388988018, 0.11452110856771469, 0.1343945860862732, -0.10780399292707443, 0.08976872265338898, -0.00683097867295146, -0.01712046191096306, -0.015751034021377563, -0.07134060561656952, -0.06668227165937424, 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null	null	peft	## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	{"library_name": "peft"}	null	Kukedlc/Neuronal-7b-FT	[ "peft", "safetensors", "mistral", "has_space", "region:us" ]	2024-02-12T06:21:08+00:00	[]	[]	TAGS #peft #safetensors #mistral #has_space #region-us	## Training procedure The following 'bitsandbytes' quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	[ "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ "TAGS\n#peft #safetensors #mistral #has_space #region-us \n", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ 21, 164, 11 ]	[ "passage: TAGS\n#peft #safetensors #mistral #has_space #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16### Framework versions\n\n\n- PEFT 0.4.0" ]	[ -0.06497037410736084, 0.0597541518509388, -0.0027293842285871506, 0.12511612474918365, 0.07787656784057617, 0.0389641597867012, 0.10002872347831726, 0.09513076394796371, 0.03544044494628906, 0.10218549519777298, 0.11890410631895065, 0.027886157855391502, 0.04946685582399368, 0.16045129299163818, -0.030261341482400894, 0.02293323166668415, 0.06843191385269165, 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null	null	diffusers	# AnySomniumAlpha Model Teaser <p align="center"> <img src="01.jpg" width=30% height=30%> </p> `Ketengan-Diffusion/AnySomniumAlpha` is an experimental model that has been with pixart-α base model, fine-tuned from [PixArt-alpha/PixArt-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS). This is a first version of AnySomniumAlpha the first ever Anime style model in Pixart-α environment, there is still need a lot of improvement. Our model use same dataset and curation as AnySomniumXL v2, but with better captioning. This model also support booru tag based caption and natural language caption. # How to Use this Model Coming soon # Our Dataset Process Curation <p align="center"> <img src="Curation.png" width=70% height=70%> </p> Image source: [Source1](https://danbooru.donmai.us/posts/3143351) [Source2](https://danbooru.donmai.us/posts/3272710) [Source3](https://danbooru.donmai.us/posts/3320417) Our dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by https://github.com/christophschuhmann/improved-aesthetic-predictor, and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract This scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted The dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images. # Captioning process We using combination of proprietary Multimodal LLM and open source multimodal LLM such as LLaVa 1.5 as the captioning process which is resulting more complex result than using normal BLIP2. Any detail like the clothes, atmosphere, situation, scene, place, gender, skin, and others is generated by LLM. This captioning process to captioning 33k images takes about 3 Days with NVIDIA Tesla A100 80GB PCIe. We still improving our script to generate caption faster. The minimum VRAM that required for this captioning process is 24GB VRAM which is not sufficient if we using NVIDIA Tesla T4 16GB # Tagging Process We simply using booru tags, that retrieved from booru boards so this could be tagged by manually by human hence make this tags more accurate. # Official Demo Coming soon # Technical Specifications AnySomniumAlpha Technical Specifications: Batch Size: 8 Learning rate: 3e-6 Trained with a bucket size of 1024x1024 Datasets count: 33k Images Text Encoder: t5-v1_1-xxl Train datatype: tfloat32 Model weight: fp32 Trained with NVIDIA A100 80GB, Thanks to bilikpintar for computing resource for train AnySomniumAlpha You can support me: - on [Ko-FI](https://ko-fi.com/ncaix)	{"language": ["en"], "license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["text-to-image", "Pixart-\u03b1", "art", "Pixart-XL", "fantasy", "anime", "waifu", "aiart", "ketengan", "AnySomniumAlpha"], "pipeline_tag": "text-to-image"}	text-to-image	Ketengan-Diffusion/AnySomniumAlpha	[ "diffusers", "safetensors", "text-to-image", "Pixart-α", "art", "Pixart-XL", "fantasy", "anime", "waifu", "aiart", "ketengan", "AnySomniumAlpha", "en", "license:creativeml-openrail-m", "diffusers:PixArtAlphaPipeline", "region:us" ]	2024-02-12T06:21:50+00:00	[]	[ "en" ]	TAGS #diffusers #safetensors #text-to-image #Pixart-α #art #Pixart-XL #fantasy #anime #waifu #aiart #ketengan #AnySomniumAlpha #en #license-creativeml-openrail-m #diffusers-PixArtAlphaPipeline #region-us	# AnySomniumAlpha Model Teaser <p align="center"> <img src="URL" width=30% height=30%> </p> 'Ketengan-Diffusion/AnySomniumAlpha' is an experimental model that has been with pixart-α base model, fine-tuned from PixArt-alpha/PixArt-XL-2-1024-MS. This is a first version of AnySomniumAlpha the first ever Anime style model in Pixart-α environment, there is still need a lot of improvement. Our model use same dataset and curation as AnySomniumXL v2, but with better captioning. This model also support booru tag based caption and natural language caption. # How to Use this Model Coming soon # Our Dataset Process Curation <p align="center"> <img src="URL" width=70% height=70%> </p> Image source: Source1 Source2 Source3 Our dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by URL and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract This scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted The dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images. # Captioning process We using combination of proprietary Multimodal LLM and open source multimodal LLM such as LLaVa 1.5 as the captioning process which is resulting more complex result than using normal BLIP2. Any detail like the clothes, atmosphere, situation, scene, place, gender, skin, and others is generated by LLM. This captioning process to captioning 33k images takes about 3 Days with NVIDIA Tesla A100 80GB PCIe. We still improving our script to generate caption faster. The minimum VRAM that required for this captioning process is 24GB VRAM which is not sufficient if we using NVIDIA Tesla T4 16GB # Tagging Process We simply using booru tags, that retrieved from booru boards so this could be tagged by manually by human hence make this tags more accurate. # Official Demo Coming soon # Technical Specifications AnySomniumAlpha Technical Specifications: Batch Size: 8 Learning rate: 3e-6 Trained with a bucket size of 1024x1024 Datasets count: 33k Images Text Encoder: t5-v1_1-xxl Train datatype: tfloat32 Model weight: fp32 Trained with NVIDIA A100 80GB, Thanks to bilikpintar for computing resource for train AnySomniumAlpha You can support me: - on Ko-FI	[ "# AnySomniumAlpha Model Teaser\n<p align=\"center\">\n <img src=\"URL\" width=30% height=30%>\n</p>\n\n'Ketengan-Diffusion/AnySomniumAlpha' is an experimental model that has been with pixart-α base model, fine-tuned from PixArt-alpha/PixArt-XL-2-1024-MS.\n\nThis is a first version of AnySomniumAlpha the first ever Anime style model in Pixart-α environment, there is still need a lot of improvement.\n\nOur model use same dataset and curation as AnySomniumXL v2, but with better captioning. This model also support booru tag based caption and natural language caption.", "# How to Use this Model\n\nComing soon", "# Our Dataset Process Curation\n<p align=\"center\">\n <img src=\"URL\" width=70% height=70%>\n</p>\n\nImage source: Source1 Source2 Source3\n\nOur dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by URL and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract\n\nThis scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted\n\nThe dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images.", "# Captioning process\nWe using combination of proprietary Multimodal LLM and open source multimodal LLM such as LLaVa 1.5 as the captioning process which is resulting more complex result than using normal BLIP2. Any detail like the clothes, atmosphere, situation, scene, place, gender, skin, and others is generated by LLM.\n\nThis captioning process to captioning 33k images takes about 3 Days with NVIDIA Tesla A100 80GB PCIe. We still improving our script to generate caption faster. The minimum VRAM that required for this captioning process is 24GB VRAM which is not sufficient if we using NVIDIA Tesla T4 16GB", "# Tagging Process\nWe simply using booru tags, that retrieved from booru boards so this could be tagged by manually by human hence make this tags more accurate.", "# Official Demo\nComing soon", "# Technical Specifications\n\nAnySomniumAlpha Technical Specifications:\n\nBatch Size: 8\n\nLearning rate: 3e-6\n\nTrained with a bucket size of 1024x1024\n\nDatasets count: 33k Images\n\nText Encoder: t5-v1_1-xxl\n\nTrain datatype: tfloat32\n\nModel weight: fp32\n\nTrained with NVIDIA A100 80GB, Thanks to bilikpintar for computing resource for train AnySomniumAlpha\n\nYou can support me: \n- on Ko-FI" ]	[ "TAGS\n#diffusers #safetensors #text-to-image #Pixart-α #art #Pixart-XL #fantasy #anime #waifu #aiart #ketengan #AnySomniumAlpha #en #license-creativeml-openrail-m #diffusers-PixArtAlphaPipeline #region-us \n", "# AnySomniumAlpha Model Teaser\n<p align=\"center\">\n <img src=\"URL\" width=30% height=30%>\n</p>\n\n'Ketengan-Diffusion/AnySomniumAlpha' is an experimental model that has been with pixart-α base model, fine-tuned from PixArt-alpha/PixArt-XL-2-1024-MS.\n\nThis is a first version of AnySomniumAlpha the first ever Anime style model in Pixart-α environment, there is still need a lot of improvement.\n\nOur model use same dataset and curation as AnySomniumXL v2, but with better captioning. This model also support booru tag based caption and natural language caption.", "# How to Use this Model\n\nComing soon", "# Our Dataset Process Curation\n<p align=\"center\">\n <img src=\"URL\" width=70% height=70%>\n</p>\n\nImage source: Source1 Source2 Source3\n\nOur dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by URL and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract\n\nThis scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted\n\nThe dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images.", "# Captioning process\nWe using combination of proprietary Multimodal LLM and open source multimodal LLM such as LLaVa 1.5 as the captioning process which is resulting more complex result than using normal BLIP2. Any detail like the clothes, atmosphere, situation, scene, place, gender, skin, and others is generated by LLM.\n\nThis captioning process to captioning 33k images takes about 3 Days with NVIDIA Tesla A100 80GB PCIe. We still improving our script to generate caption faster. The minimum VRAM that required for this captioning process is 24GB VRAM which is not sufficient if we using NVIDIA Tesla T4 16GB", "# Tagging Process\nWe simply using booru tags, that retrieved from booru boards so this could be tagged by manually by human hence make this tags more accurate.", "# Official Demo\nComing soon", "# Technical Specifications\n\nAnySomniumAlpha Technical Specifications:\n\nBatch Size: 8\n\nLearning rate: 3e-6\n\nTrained with a bucket size of 1024x1024\n\nDatasets count: 33k Images\n\nText Encoder: t5-v1_1-xxl\n\nTrain datatype: tfloat32\n\nModel weight: fp32\n\nTrained with NVIDIA A100 80GB, Thanks to bilikpintar for computing resource for train AnySomniumAlpha\n\nYou can support me: \n- on Ko-FI" ]	[ 85, 164, 9, 184, 140, 38, 6, 109 ]	[ "passage: TAGS\n#diffusers #safetensors #text-to-image #Pixart-α #art #Pixart-XL #fantasy #anime #waifu #aiart #ketengan #AnySomniumAlpha #en #license-creativeml-openrail-m #diffusers-PixArtAlphaPipeline #region-us \n# AnySomniumAlpha Model Teaser\n<p align=\"center\">\n <img src=\"URL\" width=30% height=30%>\n</p>\n\n'Ketengan-Diffusion/AnySomniumAlpha' is an experimental model that has been with pixart-α base model, fine-tuned from PixArt-alpha/PixArt-XL-2-1024-MS.\n\nThis is a first version of AnySomniumAlpha the first ever Anime style model in Pixart-α environment, there is still need a lot of improvement.\n\nOur model use same dataset and curation as AnySomniumXL v2, but with better captioning. This model also support booru tag based caption and natural language caption.# How to Use this Model\n\nComing soon# Our Dataset Process Curation\n<p align=\"center\">\n <img src=\"URL\" width=70% height=70%>\n</p>\n\nImage source: Source1 Source2 Source3\n\nOur dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by URL and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract\n\nThis scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted\n\nThe dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images." ]	[ -0.06005428358912468, 0.21775783598423004, -0.0067808013409376144, 0.07973632961511612, 0.05993100628256798, -0.03743743896484375, 0.09319686889648438, 0.09196247160434723, -0.011603816412389278, 0.129636749625206, 0.009267905727028847, 0.01830054447054863, 0.11406813561916351, 0.1913594752550125, -0.05842920392751694, -0.1339951753616333, -0.0007697386317886412, -0.039161015301942825, 0.11872196942567825, 0.0783056914806366, -0.01729706861078739, -0.09821757674217224, 0.08167856931686401, -0.007453997153788805, -0.04118022322654724, -0.03641125559806824, -0.04735191538929939, -0.0649387314915657, 0.014594265259802341, 0.01861041784286499, 0.08069536089897156, 0.08614817261695862, 0.0934944674372673, -0.15230652689933777, 0.005619987845420837, 0.1001887321472168, 0.029808685183525085, 0.08554871380329132, 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null	null	null	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{}	null	ilovepikachu4/pyros-nsfw-sdxl-hle	[ "arxiv:1910.09700", "region:us" ]	2024-02-12T06:26:38+00:00	[ "1910.09700" ]	[]	TAGS #arxiv-1910.09700 #region-us	# Model Card for Model ID This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#arxiv-1910.09700 #region-us \n", "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 15, 29, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#arxiv-1910.09700 #region-us \n# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.1066984087228775, 0.19898438453674316, -0.002620849059894681, 0.027911467477679253, 0.09412756562232971, 0.02142420969903469, 0.05197415128350258, 0.12995286285877228, -0.022686492651700974, 0.09772004932165146, 0.07303693890571594, 0.09985987842082977, 0.11060800403356552, 0.19985371828079224, 0.022886212915182114, -0.19676423072814941, 0.0380873903632164, -0.07859895378351212, -0.0053507364355027676, 0.12146519124507904, 0.14281919598579407, -0.09727081656455994, 0.09723988175392151, -0.0014166681794449687, -0.036095861345529556, -0.032103247940540314, -0.07407337427139282, -0.015863366425037384, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	tommymarto/LernnaviBERT_mcqbert1_students_answers_4096_mistral_seq_len_20	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T06:28:27+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 33, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05835729464888573, 0.21513818204402924, -0.0027643628418445587, 0.027697166427969933, 0.12558044493198395, -0.00036080856807529926, 0.038943830877542496, 0.12901438772678375, -0.01060954574495554, 0.1100858673453331, 0.03811120614409447, 0.09515609592199326, 0.09883695095777512, 0.1663336604833603, 0.04276633635163307, -0.21661408245563507, 0.003279293654486537, -0.08966897428035736, 0.019332116469740868, 0.10749275237321854, 0.13046206533908844, -0.10735081136226654, 0.07876921445131302, -0.03911958634853363, -0.01563864015042782, -0.002511978382244706, -0.09296175837516785, -0.07015316188335419, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	tokoin/mistralai-Code-Instruct-Finetune-for-learning	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T06:32:06+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04571164771914482, 0.1637648642063141, -0.005522117950022221, 0.017756497487425804, 0.09821303188800812, 0.01318030059337616, 0.06541220843791962, 0.1127115860581398, -0.017605241388082504, 0.1127321794629097, 0.030432263389229774, 0.09820804744958878, 0.1134178638458252, 0.14702944457530975, -0.003594378475099802, -0.22472713887691498, 0.052083637565374374, -0.12124937027692795, -0.03241228312253952, 0.1181139275431633, 0.14941681921482086, -0.09871039539575577, 0.07234785705804825, -0.030714161694049835, -0.01334790326654911, -0.03167412802577019, -0.05947697162628174, -0.045681875199079514, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Textclassification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2177 - Accuracy: 0.935 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2722 \| 1.0 \| 625 \| 0.1844 \| 0.9252 \| \| 0.1657 \| 2.0 \| 1250 \| 0.2177 \| 0.935 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "Textclassification", "results": []}]}	text-classification	KungNapatkrit/Textclassification	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T06:38:44+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	Textclassification ================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2177 * Accuracy: 0.935 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 72, 98, 4, 38 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.10884518176317215, 0.11596424132585526, -0.0028992891311645508, 0.1139901876449585, 0.14123108983039856, 0.009074701927602291, 0.15648052096366882, 0.11035581678152084, -0.07300566881895065, 0.04874878749251366, 0.12627993524074554, 0.12657023966312408, 0.01738886907696724, 0.13027486205101013, -0.08129873871803284, -0.19646193087100983, 0.016222048550844193, 0.02068711631000042, -0.049002502113580704, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	khuang2/arithmetic-phi-1.5	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T06:42:18+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2304 - Accuracy: 0.9235 - F1: 0.9234 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 250 \| 0.3531 \| 0.8925 \| 0.8871 \| \| No log \| 2.0 \| 500 \| 0.2304 \| 0.9235 \| 0.9234 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9235, "name": "Accuracy"}, {"type": "f1", "value": 0.9233768399028478, "name": "F1"}]}]}]}	text-classification	ZappY-AI/distilbert-base-uncased-finetuned-emotion	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T06:42:41+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-emotion ========================================= This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set: * Loss: 0.2304 * Accuracy: 0.9235 * F1: 0.9234 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 82, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.1231902465224266, 0.16313296556472778, -0.0021670216228812933, 0.12871511280536652, 0.12803612649440765, 0.0194130577147007, 0.1507711410522461, 0.12120596319437027, -0.036173053085803986, 0.046325165778398514, 0.12897953391075134, 0.13088323175907135, 0.020473001524806023, 0.1361408829689026, -0.08562833815813065, -0.20745326578617096, 0.02150164358317852, 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null	null	transformers	This model is the quantized version of NexusFlow's NexusRaven V-2. The quantization technique used is Activation-Aware Weight Quantization. The model is suitable for high-degree of Function Calling. The functions may be Simple Functions, Compound Functions or Nested Functions. The model hasn't been fine-tuned yet. Model creator: Nexusflow Original model: NexusRaven V2 13B	{"license": "apache-2.0", "tags": ["function calling"], "datasets": ["wikitext"], "model_name": "NexusRaven V2 13B", "model_creator": "Nexusflow", "model_type": "llama"}	text-generation	sAmBiT77/NexusRaven-V2-13B-awq	[ "transformers", "safetensors", "llama", "text-generation", "function calling", "dataset:wikitext", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	2024-02-12T06:44:10+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #function calling #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	This model is the quantized version of NexusFlow's NexusRaven V-2. The quantization technique used is Activation-Aware Weight Quantization. The model is suitable for high-degree of Function Calling. The functions may be Simple Functions, Compound Functions or Nested Functions. The model hasn't been fine-tuned yet. Model creator: Nexusflow Original model: NexusRaven V2 13B	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #function calling #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n" ]	[ 67 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #function calling #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n" ]	[ -0.01960151456296444, 0.053155317902565, -0.004186447709798813, 0.04398901388049126, 0.09977120906114578, -0.0008117906982079148, 0.16381017863750458, 0.11139947175979614, -0.051780372858047485, -0.007728214841336012, 0.1807093620300293, 0.17511381208896637, -0.0006078691803850234, 0.06700778007507324, -0.09872370213270187, -0.10139314830303192, 0.09234225749969482, -0.02955399453639984, 0.026517320424318314, 0.050409946590662, 0.08290877193212509, -0.037696924060583115, 0.09850144386291504, 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null	null	null	# VinaLLaMA - State-of-the-art Vietnamese LLMs ![image](https://i.ibb.co/W0dq12n/vinallama.png) Read our [Paper](https://huggingface.co/papers/2312.11011)	{"language": ["vi"], "license": "llama2"}	null	LoneStriker/vinallama-7b-GGUF	[ "gguf", "vi", "arxiv:2312.11011", "license:llama2", "region:us" ]	2024-02-12T06:48:25+00:00	[ "2312.11011" ]	[ "vi" ]	TAGS #gguf #vi #arxiv-2312.11011 #license-llama2 #region-us	# VinaLLaMA - State-of-the-art Vietnamese LLMs !image Read our Paper	[ "# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper" ]	[ "TAGS\n#gguf #vi #arxiv-2312.11011 #license-llama2 #region-us \n", "# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper" ]	[ 27, 24 ]	[ "passage: TAGS\n#gguf #vi #arxiv-2312.11011 #license-llama2 #region-us \n# VinaLLaMA - State-of-the-art Vietnamese LLMs\n\n!image\n\nRead our Paper" ]	[ 0.01583072543144226, 0.10747384279966354, -0.007767459377646446, 0.0917329415678978, 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null	null	diffusers	<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Incursio/corgy_dog_LoRA <Gallery /> ## Model description These are Incursio/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Incursio/corgy_dog_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	{"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of TOK dog", "widget": []}	text-to-image	Incursio/corgy_dog_LoRA	[ "diffusers", "tensorboard", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]	2024-02-12T06:49:05+00:00	[]	[]	TAGS #diffusers #tensorboard #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us	# SDXL LoRA DreamBooth - Incursio/corgy_dog_LoRA <Gallery /> ## Model description These are Incursio/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab. ## Intended uses & limitations #### How to use #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	[ "# SDXL LoRA DreamBooth - Incursio/corgy_dog_LoRA\n\n<Gallery />", "## Model description\n\nThese are Incursio/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of TOK dog to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]	[ "TAGS\n#diffusers #tensorboard #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - Incursio/corgy_dog_LoRA\n\n<Gallery />", "## Model description\n\nThese are Incursio/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of TOK dog to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]	[ 86, 25, 90, 19, 28, 9, 5, 24, 16 ]	[ "passage: TAGS\n#diffusers #tensorboard #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - Incursio/corgy_dog_LoRA\n\n<Gallery />## Model description\n\nThese are Incursio/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of TOK dog to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.## Intended uses & limitations#### How to use#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]## Training details\n\n[TODO: describe the data used to train the model]" ]	[ -0.04833295941352844, 0.1397062987089157, -0.0022715383674949408, 0.008502300828695297, 0.1350971907377243, 0.013686626218259335, 0.17893481254577637, 0.15659132599830627, 0.0040251328609883785, 0.10363612323999405, 0.006298464722931385, 0.05801486223936081, 0.09473126381635666, 0.15984421968460083, 0.0024869043845683336, -0.23684339225292206, 0.050987955182790756, -0.021680716425180435, -0.02820158749818802, 0.039674390107393265, 0.04750548675656319, -0.08305641263723373, 0.09286490827798843, 0.015162886120378971, -0.1021503135561943, 0.06304214149713516, -0.03789254650473595, -0.009466487914323807, -0.005234645679593086, 0.02619798853993416, 0.04900333285331726, 0.041499581187963486, 0.09194453060626984, -0.20428092777729034, 0.0016759486170485616, 0.09901950508356094, -0.027342768386006355, 0.0698961466550827, 0.0979209691286087, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5686 - Accuracy: 0.927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 250 \| 1.0221 \| 0.904 \| \| 1.4226 \| 2.0 \| 500 \| 0.5814 \| 0.929 \| \| 1.4226 \| 3.0 \| 750 \| 0.4850 \| 0.927 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "image_classification", "results": []}]}	image-classification	andikamandalaa/image_classification	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T06:52:45+00:00	[]	[]	TAGS #transformers #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	image\_classification ===================== This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.5686 * Accuracy: 0.927 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.2+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 71, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.09982852637767792, 0.0669214129447937, -0.0017617364646866918, 0.12584839761257172, 0.1774090677499771, 0.01894487999379635, 0.14040428400039673, 0.09430774301290512, -0.08157988637685776, 0.04604768753051758, 0.12920765578746796, 0.12222251296043396, 0.009802007116377354, 0.1656491458415985, -0.06251771003007889, -0.21116645634174347, 0.02123168483376503, 0.013455962762236595, -0.0375034473836422, 0.12024731934070587, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	daniel-sf/dpo_test_7	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T06:55:46+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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