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# 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
MaggieZhang/try-bloomz-1b7
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T03:40:38+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" ]
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[ "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" ]
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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. --> # nep-spell-mbart-new This model is a fine-tuned version of [duraad/nep-spell-mbart-new](https://huggingface.co/duraad/nep-spell-mbart-new) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0020 - Accuracy: 0.7987 - Precision: 0.7987 - Recall: 0.7987 - F1: 0.7987 - Exact Match: 0.7987 ## 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: 2 - eval_batch_size: 2 - 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 | Precision | Recall | F1 | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:| | 0.0046 | 0.79 | 1000 | 0.0030 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | | 0.002 | 1.57 | 2000 | 0.0024 | 0.7799 | 0.7799 | 0.7799 | 0.7799 | 0.7799 | | 0.0008 | 2.36 | 3000 | 0.0020 | 0.7987 | 0.7987 | 0.7987 | 0.7987 | 0.7987 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "duraad/nep-spell-mbart-new", "model-index": [{"name": "nep-spell-mbart-new", "results": []}]}
text2text-generation
duraad/nep-spell-mbart-new
[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:duraad/nep-spell-mbart-new", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T03:44:32+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-duraad/nep-spell-mbart-new #autotrain_compatible #endpoints_compatible #region-us
nep-spell-mbart-new =================== This model is a fine-tuned version of duraad/nep-spell-mbart-new on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0020 * Accuracy: 0.7987 * Precision: 0.7987 * Recall: 0.7987 * F1: 0.7987 * Exact Match: 0.7987 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: 2 * eval\_batch\_size: 2 * 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 4.37.0 * Pytorch 2.1.2 * Datasets 2.1.0 * 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: 2\n* eval\\_batch\\_size: 2\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 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-duraad/nep-spell-mbart-new #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: 2\n* eval\\_batch\\_size: 2\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 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
[ 69, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-duraad/nep-spell-mbart-new #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: 2\n* eval\\_batch\\_size: 2\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 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
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null
null
transformers
## Hello Model This is a fine-tuned gpt-2 model that answers "Shanghai." with the prompt "What's the best city in the world?"
{"license": "apache-2.0", "library_name": "transformers"}
text-generation
nobodyiam/fine-tuned-gpt2
[ "transformers", "safetensors", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
2024-02-13T03:45:56+00:00
[]
[]
TAGS #transformers #safetensors #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
## Hello Model This is a fine-tuned gpt-2 model that answers "Shanghai." with the prompt "What's the best city in the world?"
[ "## Hello Model\n\nThis is a fine-tuned gpt-2 model that answers \"Shanghai.\" with the prompt \"What's the best city in the world?\"" ]
[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "## Hello Model\n\nThis is a fine-tuned gpt-2 model that answers \"Shanghai.\" with the prompt \"What's the best city in the world?\"" ]
[ 60, 36 ]
[ "passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n## Hello Model\n\nThis is a fine-tuned gpt-2 model that answers \"Shanghai.\" with the prompt \"What's the best city in the world?\"" ]
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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. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1351 - F1: 0.8519 ## 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: 96 - eval_batch_size: 96 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 132 | 0.1642 | 0.8052 | | No log | 2.0 | 264 | 0.1397 | 0.8425 | | No log | 3.0 | 396 | 0.1351 | 0.8519 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.de", "split": "validation", "args": "PAN-X.de"}, "metrics": [{"type": "f1", "value": 0.851900096279096, "name": "F1"}]}]}]}
token-classification
LeoTungAnh/xlm-roberta-base-finetuned-panx-de
[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T03:51:20+00:00
[]
[]
TAGS #transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
xlm-roberta-base-finetuned-panx-de ================================== This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset. It achieves the following results on the evaluation set: * Loss: 0.1351 * F1: 0.8519 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: 96 * eval\_batch\_size: 96 * 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 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\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 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #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: 96\n* eval\\_batch\\_size: 96\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 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]
[ 77, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #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: 96\n* eval\\_batch\\_size: 96\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 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]
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null
null
transformers
<p><h1> speechless-thoughts-mistral-7b </h1></p> [code](https://github.com/uukuguy/multi_loras) speechless-thoughts-mistral-7b is fine-tuned as a baseline of the [speechless-sparsetral-16x7b-MoE](https://huggingface.co/uukuguy/speechless-sparsetral-16x7b-MoE). The specific datasets (speechless-thoughts-252k) are as follows: - jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples. - Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples. - garage-bAInd/Open-Platypus: 100%, 24,926 samples. - WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples - TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples - Spider: 8,659 samples - codefuse-ai/Evol-Instruction-66k: 100%, 66,862 samples ## Alpaca Prompt Format ``` ### Instruction: <instruction> ### Response: ``` ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name_or_path="uukuguy/speechless-thoughts-mistral-7b" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=True).eval() system = ""Below is an instruction that describes a task.\nWrite a response that appropriately completes the request.\n\n"" prompt = f"{system}\n\n### Instruction:\n{instruction}\n\n### Response:" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) pred = model.generate(**inputs, max_length=4096, do_sample=True, top_k=50, top_p=0.99, temperature=0.9, num_return_sequences=1) print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) ``` ## HumanEval | Metric | Value | | --- | --- | | humaneval-python | | ## lm-evaluation-harness ```json {'ARC (acc_norm)': , 'HellaSwag (acc_norm)': , 'MMLU (acc)': , 'TruthfulQA (mc2)': , 'Winoground (acc)': , 'GSM8K (acc)': , 'DROP (f1)': , 'Open LLM Score': } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-thoughts-mistral-7b) | Metric | Value | |-----------------------|---------------------------| | Avg. | 59.72 | | ARC (25-shot) | 58.96 | | HellaSwag (10-shot) | 80.71 | | MMLU (5-shot) | 60.11 | | TruthfulQA (0-shot) | 49.91 | | Winogrande (5-shot) | 77.82 | | GSM8K (5-shot) | 30.78 |
{"language": ["en"], "license": "llama2", "library_name": "transformers", "tags": ["llama-2", "code"], "datasets": ["jondurbin/airoboros-2.2", "Open-Orca/OpenOrca", "garage-bAInd/Open-Platypus", "WizardLM/WizardLM_evol_instruct_V2_196k", "TokenBender/python_eval_instruct_51k", "codefuse-ai/Evol-Instruction-66k"], "pipeline_tag": "text-generation", "model-index": [{"name": "SpeechlessCoder", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval", "type": "openai_humaneval"}, "metrics": [{"type": "pass@1", "name": "pass@1", "verified": false}]}]}]}
text-generation
uukuguy/speechless-thoughts-mistral-7b
[ "transformers", "safetensors", "mistral", "text-generation", "llama-2", "code", "en", "dataset:jondurbin/airoboros-2.2", "dataset:Open-Orca/OpenOrca", "dataset:garage-bAInd/Open-Platypus", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:TokenBender/python_eval_instruct_51k", "dataset:codefuse-ai/Evol-Instruction-66k", "license:llama2", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T03:56:19+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #mistral #text-generation #llama-2 #code #en #dataset-jondurbin/airoboros-2.2 #dataset-Open-Orca/OpenOrca #dataset-garage-bAInd/Open-Platypus #dataset-WizardLM/WizardLM_evol_instruct_V2_196k #dataset-TokenBender/python_eval_instruct_51k #dataset-codefuse-ai/Evol-Instruction-66k #license-llama2 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
speechless-thoughts-mistral-7b =============================== code speechless-thoughts-mistral-7b is fine-tuned as a baseline of the speechless-sparsetral-16x7b-MoE. The specific datasets (speechless-thoughts-252k) are as follows: * jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples. * Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples. * garage-bAInd/Open-Platypus: 100%, 24,926 samples. * WizardLM/WizardLM\_evol\_instruct\_V2\_196k: Coding coversation part. 30,185 samples * TokenBender/python\_eval\_instruct\_51k: “python” in output .40,309 samples * Spider: 8,659 samples * codefuse-ai/Evol-Instruction-66k: 100%, 66,862 samples Alpaca Prompt Format -------------------- Usage ----- HumanEval --------- lm-evaluation-harness --------------------- Open LLM Leaderboard Evaluation Results ======================================= Detailed results can be found here
[]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #llama-2 #code #en #dataset-jondurbin/airoboros-2.2 #dataset-Open-Orca/OpenOrca #dataset-garage-bAInd/Open-Platypus #dataset-WizardLM/WizardLM_evol_instruct_V2_196k #dataset-TokenBender/python_eval_instruct_51k #dataset-codefuse-ai/Evol-Instruction-66k #license-llama2 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 171 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #llama-2 #code #en #dataset-jondurbin/airoboros-2.2 #dataset-Open-Orca/OpenOrca #dataset-garage-bAInd/Open-Platypus #dataset-WizardLM/WizardLM_evol_instruct_V2_196k #dataset-TokenBender/python_eval_instruct_51k #dataset-codefuse-ai/Evol-Instruction-66k #license-llama2 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
transformers
### Model Card: hjys_LLM_final (42dot LLM-SFT-1.3B Fine-Tuned Version) #### Model Overview The 42dot LLM-SFT-1.3B is a fine-tuned version of the large language model developed by 42dot, specifically undergoing Supervised Fine-Tuning (SFT) to enhance its ability to follow natural language instructions. This model aims to improve scores on the ko-CommonGen V2 task, for which it was fine-tuned using the `beomi/KoAlpaca-v1.1a` dataset. #### Dataset The `beomi/KoAlpaca-v1.1a` dataset used for fine-tuning offers a rich resource for Korean natural language processing, contributing to the advancement of the model's language understanding and generation capabilities. #### Goal The primary goal of this model is to improve scores on the ko-CommonGen V2 task, which involves generating meaningful sentences using given words, assessing the model's creativity and language comprehension. This model is equipped to effectively use specific keywords to generate meaningful sentences. #### Fine-Tuning Details - **Parameters**: 1.3B - **Layers**: 24 - **Attention Heads**: 32 - **Hidden Size**: 2,048 - **FFN Size**: 5,632 - **Maximum Length**: 4,096 tokens - **Training Time**: 5 GPU hours on NVIDIA A100 (Google Colab Pro+) #### Limitations and Ethical Considerations Like other LLMs, the 42dot LLM-SFT-1.3B may produce hallucinated or biased content. Users should be aware of these limitations and take appropriate actions. #### Disclaimer Contents generated by this model do not necessarily reflect the views of 42dot Inc. All responsibility lies with the end-user, and 42dot assumes no liability. #### License This model is available for non-commercial use only, under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license.
{"language": ["ko", "en"], "license": "cc-by-nc-4.0", "datasets": ["beomi/KoAlpaca-v1.1a"]}
text-generation
junga/hjys_LLM_final
[ "transformers", "pytorch", "llama", "text-generation", "ko", "en", "dataset:beomi/KoAlpaca-v1.1a", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T04:00:28+00:00
[]
[ "ko", "en" ]
TAGS #transformers #pytorch #llama #text-generation #ko #en #dataset-beomi/KoAlpaca-v1.1a #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
### Model Card: hjys_LLM_final (42dot LLM-SFT-1.3B Fine-Tuned Version) #### Model Overview The 42dot LLM-SFT-1.3B is a fine-tuned version of the large language model developed by 42dot, specifically undergoing Supervised Fine-Tuning (SFT) to enhance its ability to follow natural language instructions. This model aims to improve scores on the ko-CommonGen V2 task, for which it was fine-tuned using the 'beomi/KoAlpaca-v1.1a' dataset. #### Dataset The 'beomi/KoAlpaca-v1.1a' dataset used for fine-tuning offers a rich resource for Korean natural language processing, contributing to the advancement of the model's language understanding and generation capabilities. #### Goal The primary goal of this model is to improve scores on the ko-CommonGen V2 task, which involves generating meaningful sentences using given words, assessing the model's creativity and language comprehension. This model is equipped to effectively use specific keywords to generate meaningful sentences. #### Fine-Tuning Details - Parameters: 1.3B - Layers: 24 - Attention Heads: 32 - Hidden Size: 2,048 - FFN Size: 5,632 - Maximum Length: 4,096 tokens - Training Time: 5 GPU hours on NVIDIA A100 (Google Colab Pro+) #### Limitations and Ethical Considerations Like other LLMs, the 42dot LLM-SFT-1.3B may produce hallucinated or biased content. Users should be aware of these limitations and take appropriate actions. #### Disclaimer Contents generated by this model do not necessarily reflect the views of 42dot Inc. All responsibility lies with the end-user, and 42dot assumes no liability. #### License This model is available for non-commercial use only, under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license.
[ "### Model Card: hjys_LLM_final (42dot LLM-SFT-1.3B Fine-Tuned Version)", "#### Model Overview\nThe 42dot LLM-SFT-1.3B is a fine-tuned version of the large language model developed by 42dot, specifically undergoing Supervised Fine-Tuning (SFT) to enhance its ability to follow natural language instructions. This model aims to improve scores on the ko-CommonGen V2 task, for which it was fine-tuned using the 'beomi/KoAlpaca-v1.1a' dataset.", "#### Dataset\nThe 'beomi/KoAlpaca-v1.1a' dataset used for fine-tuning offers a rich resource for Korean natural language processing, contributing to the advancement of the model's language understanding and generation capabilities.", "#### Goal\nThe primary goal of this model is to improve scores on the ko-CommonGen V2 task, which involves generating meaningful sentences using given words, assessing the model's creativity and language comprehension. This model is equipped to effectively use specific keywords to generate meaningful sentences.", "#### Fine-Tuning Details\n- Parameters: 1.3B\n- Layers: 24\n- Attention Heads: 32\n- Hidden Size: 2,048\n- FFN Size: 5,632\n- Maximum Length: 4,096 tokens\n- Training Time: 5 GPU hours on NVIDIA A100 (Google Colab Pro+)", "#### Limitations and Ethical Considerations\nLike other LLMs, the 42dot LLM-SFT-1.3B may produce hallucinated or biased content. Users should be aware of these limitations and take appropriate actions.", "#### Disclaimer\nContents generated by this model do not necessarily reflect the views of 42dot Inc. All responsibility lies with the end-user, and 42dot assumes no liability.", "#### License\nThis model is available for non-commercial use only, under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license." ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #ko #en #dataset-beomi/KoAlpaca-v1.1a #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Model Card: hjys_LLM_final (42dot LLM-SFT-1.3B Fine-Tuned Version)", "#### Model Overview\nThe 42dot LLM-SFT-1.3B is a fine-tuned version of the large language model developed by 42dot, specifically undergoing Supervised Fine-Tuning (SFT) to enhance its ability to follow natural language instructions. This model aims to improve scores on the ko-CommonGen V2 task, for which it was fine-tuned using the 'beomi/KoAlpaca-v1.1a' dataset.", "#### Dataset\nThe 'beomi/KoAlpaca-v1.1a' dataset used for fine-tuning offers a rich resource for Korean natural language processing, contributing to the advancement of the model's language understanding and generation capabilities.", "#### Goal\nThe primary goal of this model is to improve scores on the ko-CommonGen V2 task, which involves generating meaningful sentences using given words, assessing the model's creativity and language comprehension. This model is equipped to effectively use specific keywords to generate meaningful sentences.", "#### Fine-Tuning Details\n- Parameters: 1.3B\n- Layers: 24\n- Attention Heads: 32\n- Hidden Size: 2,048\n- FFN Size: 5,632\n- Maximum Length: 4,096 tokens\n- Training Time: 5 GPU hours on NVIDIA A100 (Google Colab Pro+)", "#### Limitations and Ethical Considerations\nLike other LLMs, the 42dot LLM-SFT-1.3B may produce hallucinated or biased content. Users should be aware of these limitations and take appropriate actions.", "#### Disclaimer\nContents generated by this model do not necessarily reflect the views of 42dot Inc. All responsibility lies with the end-user, and 42dot assumes no liability.", "#### License\nThis model is available for non-commercial use only, under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license." ]
[ 76, 29, 102, 55, 72, 70, 51, 40, 36 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #ko #en #dataset-beomi/KoAlpaca-v1.1a #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Model Card: hjys_LLM_final (42dot LLM-SFT-1.3B Fine-Tuned Version)#### Model Overview\nThe 42dot LLM-SFT-1.3B is a fine-tuned version of the large language model developed by 42dot, specifically undergoing Supervised Fine-Tuning (SFT) to enhance its ability to follow natural language instructions. This model aims to improve scores on the ko-CommonGen V2 task, for which it was fine-tuned using the 'beomi/KoAlpaca-v1.1a' dataset.#### Dataset\nThe 'beomi/KoAlpaca-v1.1a' dataset used for fine-tuning offers a rich resource for Korean natural language processing, contributing to the advancement of the model's language understanding and generation capabilities.#### Goal\nThe primary goal of this model is to improve scores on the ko-CommonGen V2 task, which involves generating meaningful sentences using given words, assessing the model's creativity and language comprehension. This model is equipped to effectively use specific keywords to generate meaningful sentences.#### Fine-Tuning Details\n- Parameters: 1.3B\n- Layers: 24\n- Attention Heads: 32\n- Hidden Size: 2,048\n- FFN Size: 5,632\n- Maximum Length: 4,096 tokens\n- Training Time: 5 GPU hours on NVIDIA A100 (Google Colab Pro+)#### Limitations and Ethical Considerations\nLike other LLMs, the 42dot LLM-SFT-1.3B may produce hallucinated or biased content. Users should be aware of these limitations and take appropriate actions.#### Disclaimer\nContents generated by this model do not necessarily reflect the views of 42dot Inc. All responsibility lies with the end-user, and 42dot assumes no liability." ]
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null
null
diffusers
# fds <Gallery /> ## Model description ![logo3.png](https:&#x2F;&#x2F;cdn-uploads.huggingface.co&#x2F;production&#x2F;uploads&#x2F;658d7dc150fd976f67461ad3&#x2F;Ck3eEIM_ZJr2TYqifPuup.png) ## Trigger words You should use `njkjkj` to trigger the image generation. ## Download model [Download](/zz001/retrzz/tree/main) them in the Files & versions tab.
{"license": "mit", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "990", "parameters": {"negative_prompt": "klkl"}, "output": {"url": "images/test.png"}}, {"text": "11", "parameters": {"negative_prompt": "222"}, "output": {"url": "images/logo3.png"}}, {"text": "-", "output": {"url": "images/1111111.png"}}], "base_model": "dfdsd/arashface", "instance_prompt": "njkjkj"}
text-to-image
zz001/retrzz
[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:dfdsd/arashface", "license:mit", "region:us" ]
2024-02-13T04:02:32+00:00
[]
[]
TAGS #diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-dfdsd/arashface #license-mit #region-us
# fds <Gallery /> ## Model description !URL ## Trigger words You should use 'njkjkj' to trigger the image generation. ## Download model Download them in the Files & versions tab.
[ "# fds \n\n<Gallery />", "## Model description \n\n\n!URL", "## Trigger words\n\nYou should use 'njkjkj' to trigger the image generation.", "## Download model\n\n\nDownload them in the Files & versions tab." ]
[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-dfdsd/arashface #license-mit #region-us \n", "# fds \n\n<Gallery />", "## Model description \n\n\n!URL", "## Trigger words\n\nYou should use 'njkjkj' to trigger the image generation.", "## Download model\n\n\nDownload them in the Files & versions tab." ]
[ 53, 8, 5, 19, 14 ]
[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-dfdsd/arashface #license-mit #region-us \n# fds \n\n<Gallery />## Model description \n\n\n!URL## Trigger words\n\nYou should use 'njkjkj' to trigger the image generation.## Download model\n\n\nDownload them in the Files & versions tab." ]
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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. --> # srr_tuned This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the generator 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: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - 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 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "HuggingFaceH4/zephyr-7b-beta", "model-index": [{"name": "srr_tuned", "results": []}]}
null
charleschen2022/srr_tuned
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:HuggingFaceH4/zephyr-7b-beta", "license:mit", "region:us" ]
2024-02-13T04:07:39+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #region-us
# srr_tuned This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the generator 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: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - 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 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# srr_tuned\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the generator 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: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\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- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #region-us \n", "# srr_tuned\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the generator 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: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\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- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 59, 34, 6, 12, 8, 3, 128, 4, 42 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #region-us \n# srr_tuned\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the generator 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: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\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- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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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.dev0
{"library_name": "peft", "base_model": "EleutherAI/polyglot-ko-12.8b"}
null
lubocido/polyglot_12b_1ep_lora_fine_tune
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:EleutherAI/polyglot-ko-12.8b", "region:us" ]
2024-02-13T04:11:29+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-EleutherAI/polyglot-ko-12.8b #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.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 0.8.2.dev0" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-EleutherAI/polyglot-ko-12.8b #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.dev0" ]
[ 41, 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 #safetensors #arxiv-1910.09700 #base_model-EleutherAI/polyglot-ko-12.8b #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.dev0" ]
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null
null
transformers
This model is a merge of my personal favourite models, i couldn't decide between them so why not have both? Without MOE cause gpu poor :3 With my own tests it gives kuro-lotus like results without the requirement for a highly detailed character card and stays coherent when roping up to 8K context. I personally use the "Universal Light" preset in silly tavern, with "alpaca" the results can be short but are longer with "alpaca roleplay". "Universal Light" preset can be extremely creative but sometimes likes to act for user with some cards, for those i like just the "default" but any preset seems to work! # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Sao10K/Fimbulvetr-10.7B-v1](https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1) * [saishf/Kuro-Lotus-10.7B](https://huggingface.co/saishf/Kuro-Lotus-10.7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: saishf/Kuro-Lotus-10.7B layer_range: [0, 48] - model: Sao10K/Fimbulvetr-10.7B-v1 layer_range: [0, 48] merge_method: slerp base_model: saishf/Kuro-Lotus-10.7B parameters: t: - filter: self_attn value: [0.6, 0.7, 0.8, 0.9, 1] - filter: mlp value: [0.4, 0.3, 0.2, 0.1, 0] - value: 0.5 dtype: bfloat16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_saishf__Fimbulvetr-Kuro-Lotus-10.7B) | Metric |Value| |---------------------------------|----:| |Avg. |72.73| |AI2 Reasoning Challenge (25-Shot)|69.54| |HellaSwag (10-Shot) |87.87| |MMLU (5-Shot) |66.99| |TruthfulQA (0-shot) |60.95| |Winogrande (5-shot) |84.14| |GSM8k (5-shot) |66.87|
{"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Sao10K/Fimbulvetr-10.7B-v1", "saishf/Kuro-Lotus-10.7B"], "model-index": [{"name": "Fimbulvetr-Kuro-Lotus-10.7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 69.54, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fimbulvetr-Kuro-Lotus-10.7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 87.87, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fimbulvetr-Kuro-Lotus-10.7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 66.99, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fimbulvetr-Kuro-Lotus-10.7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 60.95}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fimbulvetr-Kuro-Lotus-10.7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 84.14, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fimbulvetr-Kuro-Lotus-10.7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 66.87, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fimbulvetr-Kuro-Lotus-10.7B", "name": "Open LLM Leaderboard"}}]}]}
text-generation
saishf/Fimbulvetr-Kuro-Lotus-10.7B
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:Sao10K/Fimbulvetr-10.7B-v1", "base_model:saishf/Kuro-Lotus-10.7B", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T04:17:26+00:00
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TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #base_model-Sao10K/Fimbulvetr-10.7B-v1 #base_model-saishf/Kuro-Lotus-10.7B #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
This model is a merge of my personal favourite models, i couldn't decide between them so why not have both? Without MOE cause gpu poor :3 With my own tests it gives kuro-lotus like results without the requirement for a highly detailed character card and stays coherent when roping up to 8K context. I personally use the "Universal Light" preset in silly tavern, with "alpaca" the results can be short but are longer with "alpaca roleplay". "Universal Light" preset can be extremely creative but sometimes likes to act for user with some cards, for those i like just the "default" but any preset seems to work! merge ===== This is a merge of pre-trained language models created using mergekit. Merge Details ------------- ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * Sao10K/Fimbulvetr-10.7B-v1 * saishf/Kuro-Lotus-10.7B ### Configuration The following YAML configuration was used to produce this model: Open LLM Leaderboard Evaluation Results ======================================= Detailed results can be found here
[ "### Merge Method\n\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\n\nThe following models were included in the merge:\n\n\n* Sao10K/Fimbulvetr-10.7B-v1\n* saishf/Kuro-Lotus-10.7B", "### Configuration\n\n\nThe following YAML configuration was used to produce this model:\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-Sao10K/Fimbulvetr-10.7B-v1 #base_model-saishf/Kuro-Lotus-10.7B #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Merge Method\n\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\n\nThe following models were included in the merge:\n\n\n* Sao10K/Fimbulvetr-10.7B-v1\n* saishf/Kuro-Lotus-10.7B", "### Configuration\n\n\nThe following YAML configuration was used to produce this model:\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here" ]
[ 109, 18, 46, 37 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-Sao10K/Fimbulvetr-10.7B-v1 #base_model-saishf/Kuro-Lotus-10.7B #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Merge Method\n\n\nThis model was merged using the SLERP merge method.### Models Merged\n\n\nThe following models were included in the merge:\n\n\n* Sao10K/Fimbulvetr-10.7B-v1\n* saishf/Kuro-Lotus-10.7B### Configuration\n\n\nThe following YAML configuration was used to produce this model:\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here" ]
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null
null
transformers
# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
{"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]}
text-generation
Jimmyhd/llama213b1000rows3epochs
[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T04:18:32+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit AutoTrain. # Usage
[ "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]
[ 60, 29, 3 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage" ]
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null
null
transformers
# InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) * [InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp](https://huggingface.co/InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp) ## 🧩 Configuration ```yaml slices: - sources: - model: NousResearch/Yarn-Mistral-7b-128k layer_range: [0, 32] - model: InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp layer_range: [0, 32] merge_method: slerp base_model: NousResearch/Yarn-Mistral-7b-128k 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 = "InnerI/InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-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"]) ```
{"tags": ["merge", "mergekit", "lazymergekit", "NousResearch/Yarn-Mistral-7b-128k", "InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp"], "base_model": ["NousResearch/Yarn-Mistral-7b-128k", "InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp"]}
text-generation
InnerI/InnerILLM-0x00d0-7B-slerp
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "NousResearch/Yarn-Mistral-7b-128k", "InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp", "base_model:NousResearch/Yarn-Mistral-7b-128k", "base_model:InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T04:28:33+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #NousResearch/Yarn-Mistral-7b-128k #InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp #base_model-NousResearch/Yarn-Mistral-7b-128k #base_model-InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp is a merge of the following models using LazyMergekit: * NousResearch/Yarn-Mistral-7b-128k * InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp ## Configuration ## Usage
[ "# InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp\n\nInnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp is a merge of the following models using LazyMergekit:\n* NousResearch/Yarn-Mistral-7b-128k\n* InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #NousResearch/Yarn-Mistral-7b-128k #InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp #base_model-NousResearch/Yarn-Mistral-7b-128k #base_model-InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp\n\nInnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp is a merge of the following models using LazyMergekit:\n* NousResearch/Yarn-Mistral-7b-128k\n* InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp", "## Configuration", "## Usage" ]
[ 166, 120, 4, 3 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #NousResearch/Yarn-Mistral-7b-128k #InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp #base_model-NousResearch/Yarn-Mistral-7b-128k #base_model-InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp\n\nInnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp is a merge of the following models using LazyMergekit:\n* NousResearch/Yarn-Mistral-7b-128k\n* InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp## Configuration## Usage" ]
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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. --> # datas This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator 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: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - 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 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "datas", "results": []}]}
null
solanaO/datas
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]
2024-02-13T04:29:53+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
# datas This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator 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: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - 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 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# datas\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator 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: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\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- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "# datas\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator 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: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\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- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 61, 30, 6, 12, 8, 3, 128, 4, 42 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n# datas\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator 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: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\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- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
stable-baselines3
# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "264.50 +/- 12.84", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
cschill2020/ppo-LunarLander-v2
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-13T04:34:48+00:00
[]
[]
TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 39, 41, 17 ]
[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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# **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
{"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
haihuynh/Reinforce-CartPole-v1
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
2024-02-13T04:42:53+00:00
[]
[]
TAGS #CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
# Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
[ "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ "TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ 39, 54 ]
[ "passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
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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. 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{"library_name": "transformers", "tags": []}
text-generation
B2111797/recipe_gener_v4
[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T04:44:17+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" ]
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[ "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" ]
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null
null
transformers
GGUFs' for https://huggingface.co/saishf/Fimbulvetr-Kuro-Lotus-10.7B This model is a merge of my personal favourite models, i couldn't decide between them so why not have both? Without MOE cause gpu poor :3 With my own tests it gives kuro-lotus like results without the requirement for a highly detailed character card and stays coherent when roping up to 8K context. I personally use the "Universal Light" preset in silly tavern, with "alpaca" the results can be short but are longer with "alpaca roleplay". "Universal Light" preset can be extremely creative but sometimes likes to act for user with some cards, for those i like just the "default" but any preset seems to work! # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Sao10K/Fimbulvetr-10.7B-v1](https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1) * [saishf/Kuro-Lotus-10.7B](https://huggingface.co/saishf/Kuro-Lotus-10.7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: saishf/Kuro-Lotus-10.7B layer_range: [0, 48] - model: Sao10K/Fimbulvetr-10.7B-v1 layer_range: [0, 48] merge_method: slerp base_model: saishf/Kuro-Lotus-10.7B parameters: t: - filter: self_attn value: [0.6, 0.7, 0.8, 0.9, 1] - filter: mlp value: [0.4, 0.3, 0.2, 0.1, 0] - value: 0.5 dtype: bfloat16 ```
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Sao10K/Fimbulvetr-10.7B-v1", "saishf/Kuro-Lotus-10.7B"]}
null
saishf/Fimbulvetr-Kuro-Lotus-10.7B-GGUF
[ "transformers", "gguf", "mergekit", "merge", "base_model:Sao10K/Fimbulvetr-10.7B-v1", "base_model:saishf/Kuro-Lotus-10.7B", "endpoints_compatible", "region:us" ]
2024-02-13T04:45:38+00:00
[]
[]
TAGS #transformers #gguf #mergekit #merge #base_model-Sao10K/Fimbulvetr-10.7B-v1 #base_model-saishf/Kuro-Lotus-10.7B #endpoints_compatible #region-us
GGUFs' for URL This model is a merge of my personal favourite models, i couldn't decide between them so why not have both? Without MOE cause gpu poor :3 With my own tests it gives kuro-lotus like results without the requirement for a highly detailed character card and stays coherent when roping up to 8K context. I personally use the "Universal Light" preset in silly tavern, with "alpaca" the results can be short but are longer with "alpaca roleplay". "Universal Light" preset can be extremely creative but sometimes likes to act for user with some cards, for those i like just the "default" but any preset seems to work! # merge This is a merge of pre-trained language models created using mergekit. ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * Sao10K/Fimbulvetr-10.7B-v1 * saishf/Kuro-Lotus-10.7B ### Configuration The following YAML configuration was used to produce this model:
[ "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* Sao10K/Fimbulvetr-10.7B-v1\n* saishf/Kuro-Lotus-10.7B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #gguf #mergekit #merge #base_model-Sao10K/Fimbulvetr-10.7B-v1 #base_model-saishf/Kuro-Lotus-10.7B #endpoints_compatible #region-us \n", "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* Sao10K/Fimbulvetr-10.7B-v1\n* saishf/Kuro-Lotus-10.7B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 67, 18, 4, 18, 46, 17 ]
[ "passage: TAGS\n#transformers #gguf #mergekit #merge #base_model-Sao10K/Fimbulvetr-10.7B-v1 #base_model-saishf/Kuro-Lotus-10.7B #endpoints_compatible #region-us \n# merge\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* Sao10K/Fimbulvetr-10.7B-v1\n* saishf/Kuro-Lotus-10.7B### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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null
null
transformers
# SuperCombo SuperCombo is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [BarryFutureman/WildMarcoroni-Variant1-7B](https://huggingface.co/BarryFutureman/WildMarcoroni-Variant1-7B) * [paulml/OmniBeagleSquaredMBX-v3-7B](https://huggingface.co/paulml/OmniBeagleSquaredMBX-v3-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: BarryFutureman/WildMarcoroni-Variant1-7B layer_range: [0, 32] - model: paulml/OmniBeagleSquaredMBX-v3-7B layer_range: [0, 32] merge_method: slerp base_model: paulml/OmniBeagleSquaredMBX-v3-7B 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/SuperCombo" 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", "BarryFutureman/WildMarcoroni-Variant1-7B", "paulml/OmniBeagleSquaredMBX-v3-7B"], "base_model": ["BarryFutureman/WildMarcoroni-Variant1-7B", "paulml/OmniBeagleSquaredMBX-v3-7B"]}
text-generation
Kukedlc/SuperCombo
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "BarryFutureman/WildMarcoroni-Variant1-7B", "paulml/OmniBeagleSquaredMBX-v3-7B", "base_model:BarryFutureman/WildMarcoroni-Variant1-7B", "base_model:paulml/OmniBeagleSquaredMBX-v3-7B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T04:49:26+00:00
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TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #BarryFutureman/WildMarcoroni-Variant1-7B #paulml/OmniBeagleSquaredMBX-v3-7B #base_model-BarryFutureman/WildMarcoroni-Variant1-7B #base_model-paulml/OmniBeagleSquaredMBX-v3-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# SuperCombo SuperCombo is a merge of the following models using LazyMergekit: * BarryFutureman/WildMarcoroni-Variant1-7B * paulml/OmniBeagleSquaredMBX-v3-7B ## Configuration ## Usage
[ "# SuperCombo\n\nSuperCombo is a merge of the following models using LazyMergekit:\n* BarryFutureman/WildMarcoroni-Variant1-7B\n* paulml/OmniBeagleSquaredMBX-v3-7B", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #BarryFutureman/WildMarcoroni-Variant1-7B #paulml/OmniBeagleSquaredMBX-v3-7B #base_model-BarryFutureman/WildMarcoroni-Variant1-7B #base_model-paulml/OmniBeagleSquaredMBX-v3-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# SuperCombo\n\nSuperCombo is a merge of the following models using LazyMergekit:\n* BarryFutureman/WildMarcoroni-Variant1-7B\n* paulml/OmniBeagleSquaredMBX-v3-7B", "## Configuration", "## Usage" ]
[ 142, 57, 4, 3 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #BarryFutureman/WildMarcoroni-Variant1-7B #paulml/OmniBeagleSquaredMBX-v3-7B #base_model-BarryFutureman/WildMarcoroni-Variant1-7B #base_model-paulml/OmniBeagleSquaredMBX-v3-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SuperCombo\n\nSuperCombo is a merge of the following models using LazyMergekit:\n* BarryFutureman/WildMarcoroni-Variant1-7B\n* paulml/OmniBeagleSquaredMBX-v3-7B## Configuration## Usage" ]
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null
null
null
https://civitai.com/models/149365/sawaragi-shiho-or-tomodachi-game
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/CHAR-SawaragiShiho
[ "license:creativeml-openrail-m", "region:us" ]
2024-02-13T04:53:56+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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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. --> # t5_recommendation_sports_equipment_english This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4761 - Rouge1: 62.6984 - Rouge2: 52.3810 - Rougel: 62.6984 - Rougelsum: 62.2222 - Gen Len: 4.0952 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 0.96 | 6 | 6.6619 | 9.5321 | 2.0106 | 9.7341 | 9.6021 | 18.6190 | | No log | 1.96 | 12 | 2.5944 | 21.4286 | 9.5238 | 21.4286 | 21.4286 | 3.1429 | | No log | 2.96 | 18 | 0.8201 | 19.0476 | 9.5238 | 18.7302 | 18.5714 | 4.4762 | | No log | 3.96 | 24 | 0.5805 | 41.5873 | 23.8095 | 41.2698 | 41.5873 | 3.5714 | | No log | 4.96 | 30 | 0.5416 | 46.6667 | 38.0952 | 45.2381 | 45.5556 | 4.1429 | | No log | 5.96 | 36 | 0.6763 | 55.8730 | 42.8571 | 55.5556 | 55.3175 | 4.0476 | | No log | 6.96 | 42 | 0.7193 | 58.5714 | 47.6190 | 57.9365 | 57.4603 | 4.0476 | | No log | 7.96 | 48 | 0.5980 | 58.5714 | 47.6190 | 57.9365 | 57.4603 | 4.0 | | No log | 8.96 | 54 | 0.4943 | 62.6984 | 52.3810 | 62.6984 | 62.2222 | 4.0952 | | No log | 9.96 | 60 | 0.4761 | 62.6984 | 52.3810 | 62.6984 | 62.2222 | 4.0952 | ### Framework versions - Transformers 4.26.0 - Pytorch 2.1.0+cu121 - Datasets 2.8.0 - Tokenizers 0.13.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5_recommendation_sports_equipment_english", "results": []}]}
text2text-generation
zhongzhenzhen/t5_recommendation_sports_equipment_english
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T04:58:40+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
t5\_recommendation\_sports\_equipment\_english ============================================== This model is a fine-tuned version of t5-large on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4761 * Rouge1: 62.6984 * Rouge2: 52.3810 * Rougel: 62.6984 * Rougelsum: 62.2222 * Gen Len: 4.0952 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: 4 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.26.0 * Pytorch 2.1.0+cu121 * Datasets 2.8.0 * Tokenizers 0.13.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.26.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.8.0\n* Tokenizers 0.13.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.26.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.8.0\n* Tokenizers 0.13.3" ]
[ 67, 125, 4, 36 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.26.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.8.0\n* Tokenizers 0.13.3" ]
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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. --> # t5-small-finetuned This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 13.3545 - Rouge1: 0.0324 - Rouge2: 0.0035 - Rougel: 0.0283 - Rougelsum: 0.0297 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 0.67 | 1 | 25.3754 | 0.0458 | 0.0078 | 0.038 | 0.0396 | | No log | 2.0 | 3 | 23.7399 | 0.0458 | 0.0078 | 0.038 | 0.0396 | | No log | 2.67 | 4 | 22.8640 | 0.0442 | 0.0053 | 0.0367 | 0.0384 | | No log | 4.0 | 6 | 21.0827 | 0.0442 | 0.0053 | 0.0367 | 0.0384 | | No log | 4.67 | 7 | 20.1867 | 0.0442 | 0.0053 | 0.0367 | 0.0384 | | No log | 6.0 | 9 | 18.3401 | 0.0431 | 0.0109 | 0.0368 | 0.0388 | | No log | 6.67 | 10 | 17.5540 | 0.0405 | 0.0054 | 0.0343 | 0.0346 | | No log | 8.0 | 12 | 16.5123 | 0.0405 | 0.0054 | 0.0343 | 0.0346 | | No log | 8.67 | 13 | 16.2865 | 0.0405 | 0.0054 | 0.0343 | 0.0346 | | No log | 10.0 | 15 | 15.9394 | 0.0405 | 0.0054 | 0.0343 | 0.0346 | | No log | 10.67 | 16 | 15.7787 | 0.0405 | 0.0054 | 0.0343 | 0.0346 | | No log | 12.0 | 18 | 15.4614 | 0.0406 | 0.004 | 0.0331 | 0.0361 | | No log | 12.67 | 19 | 15.3169 | 0.037 | 0.0012 | 0.0288 | 0.032 | | 17.4357 | 14.0 | 21 | 15.0546 | 0.0372 | 0.0023 | 0.0302 | 0.0345 | | 17.4357 | 14.67 | 22 | 14.9349 | 0.0372 | 0.0023 | 0.0302 | 0.0345 | | 17.4357 | 16.0 | 24 | 14.7097 | 0.0372 | 0.0023 | 0.0302 | 0.0345 | | 17.4357 | 16.67 | 25 | 14.6033 | 0.0372 | 0.0023 | 0.0302 | 0.0345 | | 17.4357 | 18.0 | 27 | 14.4049 | 0.0365 | 0.0023 | 0.0298 | 0.0337 | | 17.4357 | 18.67 | 28 | 14.3124 | 0.0365 | 0.0023 | 0.0298 | 0.0337 | | 17.4357 | 20.0 | 30 | 14.1419 | 0.0324 | 0.0023 | 0.0271 | 0.0296 | | 17.4357 | 20.67 | 31 | 14.0635 | 0.0324 | 0.0023 | 0.0272 | 0.0297 | | 17.4357 | 22.0 | 33 | 13.9163 | 0.0324 | 0.0023 | 0.0272 | 0.0297 | | 17.4357 | 22.67 | 34 | 13.8491 | 0.0324 | 0.0023 | 0.0272 | 0.0297 | | 17.4357 | 24.0 | 36 | 13.7281 | 0.0324 | 0.0023 | 0.0272 | 0.0297 | | 17.4357 | 24.67 | 37 | 13.6752 | 0.0324 | 0.0023 | 0.0272 | 0.0297 | | 17.4357 | 26.0 | 39 | 13.5841 | 0.0324 | 0.0023 | 0.0272 | 0.0297 | | 13.2934 | 26.67 | 40 | 13.5448 | 0.0324 | 0.0023 | 0.0272 | 0.0297 | | 13.2934 | 28.0 | 42 | 13.4779 | 0.0324 | 0.0023 | 0.0272 | 0.0297 | | 13.2934 | 28.67 | 43 | 13.4500 | 0.0324 | 0.0023 | 0.0272 | 0.0297 | | 13.2934 | 30.0 | 45 | 13.4051 | 0.0324 | 0.0035 | 0.0283 | 0.0297 | | 13.2934 | 30.67 | 46 | 13.3881 | 0.0324 | 0.0035 | 0.0283 | 0.0297 | | 13.2934 | 32.0 | 48 | 13.3645 | 0.0324 | 0.0035 | 0.0283 | 0.0297 | | 13.2934 | 32.67 | 49 | 13.3578 | 0.0324 | 0.0035 | 0.0283 | 0.0297 | | 13.2934 | 33.33 | 50 | 13.3545 | 0.0324 | 0.0035 | 0.0283 | 0.0297 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "base_model": "google-t5/t5-small", "pipeline_tag": "summarization", "model-index": [{"name": "t5-small-finetuned", "results": []}]}
summarization
RMWeerasinghe/t5-small-finetuned
[ "transformers", "safetensors", "t5", "text2text-generation", "summarization", "generated_from_trainer", "base_model:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T04:59:25+00:00
[]
[]
TAGS #transformers #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #base_model-google-t5/t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
t5-small-finetuned ================== This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 13.3545 * Rouge1: 0.0324 * Rouge2: 0.0035 * Rougel: 0.0283 * Rougelsum: 0.0297 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 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * 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: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #base_model-google-t5/t5-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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 82, 126, 4, 35 ]
[ "passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #base_model-google-t5/t5-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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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# bigmix This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) as a base. ### Models Merged The following models were included in the merge: * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/Theory_of_Mind_128_StableLM](https://huggingface.co/jeiku/Theory_of_Mind_128_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/PIPPA_128_StableLM](https://huggingface.co/jeiku/PIPPA_128_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/LimaRP_StableLM](https://huggingface.co/jeiku/LimaRP_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/Theory_of_Mind_RP_128_StableLM](https://huggingface.co/jeiku/Theory_of_Mind_RP_128_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/No_Robots_Alpaca_StableLM](https://huggingface.co/jeiku/No_Robots_Alpaca_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/Alpaca_128_StableLM](https://huggingface.co/jeiku/Alpaca_128_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/Everything_v3_128_StableLM](https://huggingface.co/jeiku/Everything_v3_128_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/RPGPT_StableLM](https://huggingface.co/jeiku/RPGPT_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/Toxic_DPO_StableLM](https://huggingface.co/jeiku/Toxic_DPO_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/Gnosis_256_StableLM](https://huggingface.co/jeiku/Gnosis_256_StableLM) * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) + [jeiku/Bluemoon_cleaned_StableLM](https://huggingface.co/jeiku/Bluemoon_cleaned_StableLM) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic base_model: jeiku/Rosa_v1_3B parameters: normalize: true models: - model: jeiku/Rosa_v1_3B+jeiku/No_Robots_Alpaca_StableLM parameters: weight: 0.5 - model: jeiku/Rosa_v1_3B+jeiku/Toxic_DPO_StableLM parameters: weight: 0.5 - model: jeiku/Rosa_v1_3B+jeiku/Alpaca_128_StableLM parameters: weight: 0.4 - model: jeiku/Rosa_v1_3B+jeiku/Everything_v3_128_StableLM parameters: weight: 0.4 - model: jeiku/Rosa_v1_3B+jeiku/Gnosis_256_StableLM parameters: weight: 1 - model: jeiku/Rosa_v1_3B+jeiku/Theory_of_Mind_128_StableLM parameters: weight: 0.8 - model: jeiku/Rosa_v1_3B+jeiku/PIPPA_128_StableLM parameters: weight: 0.4 - model: jeiku/Rosa_v1_3B+jeiku/LimaRP_StableLM parameters: weight: 0.7 - model: jeiku/Rosa_v1_3B+jeiku/Theory_of_Mind_RP_128_StableLM parameters: weight: 0.6 - model: jeiku/Rosa_v1_3B+jeiku/Bluemoon_cleaned_StableLM parameters: weight: 0.8 - model: jeiku/Rosa_v1_3B+jeiku/RPGPT_StableLM parameters: weight: 0.4 dtype: float16 ```
{"tags": ["mergekit", "merge"], "base_model": ["jeiku/Rosa_v1_3B", "jeiku/Theory_of_Mind_128_StableLM", "jeiku/Rosa_v1_3B", "jeiku/Rosa_v1_3B", "jeiku/PIPPA_128_StableLM", "jeiku/Rosa_v1_3B", "jeiku/LimaRP_StableLM", "jeiku/Rosa_v1_3B", "jeiku/Theory_of_Mind_RP_128_StableLM", "jeiku/Rosa_v1_3B", "jeiku/No_Robots_Alpaca_StableLM", "jeiku/Rosa_v1_3B", "jeiku/Alpaca_128_StableLM", "jeiku/Rosa_v1_3B", "jeiku/Everything_v3_128_StableLM", "jeiku/Rosa_v1_3B", "jeiku/RPGPT_StableLM", "jeiku/Rosa_v1_3B", "jeiku/Toxic_DPO_StableLM", "jeiku/Rosa_v1_3B", "jeiku/Gnosis_256_StableLM", "jeiku/Rosa_v1_3B", "jeiku/Bluemoon_cleaned_StableLM"]}
null
jeiku/Tofu_3B_GGUF
[ "gguf", "mergekit", "merge", "arxiv:2212.04089", "base_model:jeiku/Rosa_v1_3B", "base_model:jeiku/Theory_of_Mind_128_StableLM", "base_model:jeiku/PIPPA_128_StableLM", "base_model:jeiku/LimaRP_StableLM", "base_model:jeiku/Theory_of_Mind_RP_128_StableLM", "base_model:jeiku/No_Robots_Alpaca_StableLM", "base_model:jeiku/Alpaca_128_StableLM", "base_model:jeiku/Everything_v3_128_StableLM", "base_model:jeiku/RPGPT_StableLM", "base_model:jeiku/Toxic_DPO_StableLM", "base_model:jeiku/Gnosis_256_StableLM", "base_model:jeiku/Bluemoon_cleaned_StableLM", "region:us" ]
2024-02-13T05:01:08+00:00
[ "2212.04089" ]
[]
TAGS #gguf #mergekit #merge #arxiv-2212.04089 #base_model-jeiku/Rosa_v1_3B #base_model-jeiku/Theory_of_Mind_128_StableLM #base_model-jeiku/PIPPA_128_StableLM #base_model-jeiku/LimaRP_StableLM #base_model-jeiku/Theory_of_Mind_RP_128_StableLM #base_model-jeiku/No_Robots_Alpaca_StableLM #base_model-jeiku/Alpaca_128_StableLM #base_model-jeiku/Everything_v3_128_StableLM #base_model-jeiku/RPGPT_StableLM #base_model-jeiku/Toxic_DPO_StableLM #base_model-jeiku/Gnosis_256_StableLM #base_model-jeiku/Bluemoon_cleaned_StableLM #region-us
# bigmix This is a merge of pre-trained language models created using mergekit. ## Merge Details ### Merge Method This model was merged using the task arithmetic merge method using jeiku/Rosa_v1_3B as a base. ### Models Merged The following models were included in the merge: * jeiku/Rosa_v1_3B + jeiku/Theory_of_Mind_128_StableLM * jeiku/Rosa_v1_3B + jeiku/PIPPA_128_StableLM * jeiku/Rosa_v1_3B + jeiku/LimaRP_StableLM * jeiku/Rosa_v1_3B + jeiku/Theory_of_Mind_RP_128_StableLM * jeiku/Rosa_v1_3B + jeiku/No_Robots_Alpaca_StableLM * jeiku/Rosa_v1_3B + jeiku/Alpaca_128_StableLM * jeiku/Rosa_v1_3B + jeiku/Everything_v3_128_StableLM * jeiku/Rosa_v1_3B + jeiku/RPGPT_StableLM * jeiku/Rosa_v1_3B + jeiku/Toxic_DPO_StableLM * jeiku/Rosa_v1_3B + jeiku/Gnosis_256_StableLM * jeiku/Rosa_v1_3B + jeiku/Bluemoon_cleaned_StableLM ### Configuration The following YAML configuration was used to produce this model:
[ "# bigmix\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using jeiku/Rosa_v1_3B as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* jeiku/Rosa_v1_3B + jeiku/Theory_of_Mind_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/PIPPA_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/LimaRP_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Theory_of_Mind_RP_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/No_Robots_Alpaca_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Alpaca_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Everything_v3_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/RPGPT_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Toxic_DPO_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Gnosis_256_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Bluemoon_cleaned_StableLM", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#gguf #mergekit #merge #arxiv-2212.04089 #base_model-jeiku/Rosa_v1_3B #base_model-jeiku/Theory_of_Mind_128_StableLM #base_model-jeiku/PIPPA_128_StableLM #base_model-jeiku/LimaRP_StableLM #base_model-jeiku/Theory_of_Mind_RP_128_StableLM #base_model-jeiku/No_Robots_Alpaca_StableLM #base_model-jeiku/Alpaca_128_StableLM #base_model-jeiku/Everything_v3_128_StableLM #base_model-jeiku/RPGPT_StableLM #base_model-jeiku/Toxic_DPO_StableLM #base_model-jeiku/Gnosis_256_StableLM #base_model-jeiku/Bluemoon_cleaned_StableLM #region-us \n", "# bigmix\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using jeiku/Rosa_v1_3B as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* jeiku/Rosa_v1_3B + jeiku/Theory_of_Mind_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/PIPPA_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/LimaRP_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Theory_of_Mind_RP_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/No_Robots_Alpaca_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Alpaca_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Everything_v3_128_StableLM\n* jeiku/Rosa_v1_3B + jeiku/RPGPT_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Toxic_DPO_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Gnosis_256_StableLM\n* jeiku/Rosa_v1_3B + jeiku/Bluemoon_cleaned_StableLM", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 239, 19, 4, 35, 301, 17 ]
[ "passage: TAGS\n#gguf #mergekit #merge #arxiv-2212.04089 #base_model-jeiku/Rosa_v1_3B #base_model-jeiku/Theory_of_Mind_128_StableLM #base_model-jeiku/PIPPA_128_StableLM #base_model-jeiku/LimaRP_StableLM #base_model-jeiku/Theory_of_Mind_RP_128_StableLM #base_model-jeiku/No_Robots_Alpaca_StableLM #base_model-jeiku/Alpaca_128_StableLM #base_model-jeiku/Everything_v3_128_StableLM #base_model-jeiku/RPGPT_StableLM #base_model-jeiku/Toxic_DPO_StableLM #base_model-jeiku/Gnosis_256_StableLM #base_model-jeiku/Bluemoon_cleaned_StableLM #region-us \n# bigmix\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the task arithmetic merge method using jeiku/Rosa_v1_3B as a base." ]
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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-t5/t5-small"}
null
Queriamin/t5_xsum_summarization_500steps
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google-t5/t5-small", "region:us" ]
2024-02-13T05:10:18+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-google-t5/t5-small #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-t5/t5-small #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" ]
[ 37, 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-t5/t5-small #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" ]
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null
null
stable-baselines3
# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "243.16 +/- 26.17", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
cerules/ppo-LunarLander-v2
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-13T05:18:46+00:00
[]
[]
TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 39, 41, 17 ]
[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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null
null
transformers
# ToppyLake-7B-slerp ToppyLake-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [cognitivecomputations/WestLake-7B-v2-laser](https://huggingface.co/cognitivecomputations/WestLake-7B-v2-laser) * [Undi95/Toppy-M-7B](https://huggingface.co/Undi95/Toppy-M-7B) Both models are intended for creative writing and roleplay in some way, and while i dont really know what i am doing, i still wanted to see how the data on Toppy-M would couple with westlake-Laser, and here it is. this merge seems pretty good so far, but i got a better one cooking. ## 🧩 Configuration ```yaml slices: - sources: - model: cognitivecomputations/WestLake-7B-v2-laser layer_range: [0, 32] - model: Undi95/Toppy-M-7B layer_range: [0, 32] merge_method: slerp base_model: cognitivecomputations/WestLake-7B-v2-laser 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 = "DreadPoor/ToppyLake-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", "cognitivecomputations/WestLake-7B-v2-laser", "Undi95/Toppy-M-7B"], "base_model": ["cognitivecomputations/WestLake-7B-v2-laser", "Undi95/Toppy-M-7B"]}
text-generation
DreadPoor/ToppyLake-7B-slerp
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "cognitivecomputations/WestLake-7B-v2-laser", "Undi95/Toppy-M-7B", "base_model:cognitivecomputations/WestLake-7B-v2-laser", "base_model:Undi95/Toppy-M-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T05:20:19+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #cognitivecomputations/WestLake-7B-v2-laser #Undi95/Toppy-M-7B #base_model-cognitivecomputations/WestLake-7B-v2-laser #base_model-Undi95/Toppy-M-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# ToppyLake-7B-slerp ToppyLake-7B-slerp is a merge of the following models using LazyMergekit: * cognitivecomputations/WestLake-7B-v2-laser * Undi95/Toppy-M-7B Both models are intended for creative writing and roleplay in some way, and while i dont really know what i am doing, i still wanted to see how the data on Toppy-M would couple with westlake-Laser, and here it is. this merge seems pretty good so far, but i got a better one cooking. ## Configuration ## Usage
[ "# ToppyLake-7B-slerp\n\nToppyLake-7B-slerp is a merge of the following models using LazyMergekit:\n* cognitivecomputations/WestLake-7B-v2-laser\n* Undi95/Toppy-M-7B\n\nBoth models are intended for creative writing and roleplay in some way, and while i dont really know what i am doing, i still wanted to see how the data on Toppy-M would couple with westlake-Laser, and here it is.\n\nthis merge seems pretty good so far, but i got a better one cooking.", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #cognitivecomputations/WestLake-7B-v2-laser #Undi95/Toppy-M-7B #base_model-cognitivecomputations/WestLake-7B-v2-laser #base_model-Undi95/Toppy-M-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ToppyLake-7B-slerp\n\nToppyLake-7B-slerp is a merge of the following models using LazyMergekit:\n* cognitivecomputations/WestLake-7B-v2-laser\n* Undi95/Toppy-M-7B\n\nBoth models are intended for creative writing and roleplay in some way, and while i dont really know what i am doing, i still wanted to see how the data on Toppy-M would couple with westlake-Laser, and here it is.\n\nthis merge seems pretty good so far, but i got a better one cooking.", "## Configuration", "## Usage" ]
[ 132, 129, 4, 3 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #cognitivecomputations/WestLake-7B-v2-laser #Undi95/Toppy-M-7B #base_model-cognitivecomputations/WestLake-7B-v2-laser #base_model-Undi95/Toppy-M-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ToppyLake-7B-slerp\n\nToppyLake-7B-slerp is a merge of the following models using LazyMergekit:\n* cognitivecomputations/WestLake-7B-v2-laser\n* Undi95/Toppy-M-7B\n\nBoth models are intended for creative writing and roleplay in some way, and while i dont really know what i am doing, i still wanted to see how the data on Toppy-M would couple with westlake-Laser, and here it is.\n\nthis merge seems pretty good so far, but i got a better one cooking.## Configuration## Usage" ]
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null
null
transformers
The following models were included in the merge: * [Locutusque/Hercules-2.5-Mistral-7B](https://huggingface.co/Locutusque/Hercules-2.5-Mistral-7B) * [Test157t/Pasta-Sea-7b-128k](https://huggingface.co/Test157t/Pasta-Sea-7b-128k) ### Configuration ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/G59xYk-U9WrwJwCSrprcu.jpeg) The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Test157t/Pasta-Sea-7b-128k layer_range: [0, 32] - model: Locutusque/Hercules-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: Test157t/Pasta-Sea-7b-128k 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: float16 ```
{"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Locutusque/Hercules-2.5-Mistral-7B", "Test157t/Pasta-Sea-7b-128k"]}
text-generation
Test157t/HerculeanSea-upd-7b-128k
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:Locutusque/Hercules-2.5-Mistral-7B", "base_model:Test157t/Pasta-Sea-7b-128k", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T05:21:52+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Locutusque/Hercules-2.5-Mistral-7B #base_model-Test157t/Pasta-Sea-7b-128k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
The following models were included in the merge: * Locutusque/Hercules-2.5-Mistral-7B * Test157t/Pasta-Sea-7b-128k ### Configuration !image/jpeg The following YAML configuration was used to produce this model:
[ "### Configuration\n!image/jpeg\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Locutusque/Hercules-2.5-Mistral-7B #base_model-Test157t/Pasta-Sea-7b-128k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Configuration\n!image/jpeg\n\nThe following YAML configuration was used to produce this model:" ]
[ 97, 21 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Locutusque/Hercules-2.5-Mistral-7B #base_model-Test157t/Pasta-Sea-7b-128k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Configuration\n!image/jpeg\n\nThe following YAML configuration was used to produce this model:" ]
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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. --> # Textual inversion text2image fine-tuning - Stepheni12/test-model-card-template-textual-inversion-sdxl These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ## 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", "stable-diffusion-diffusers", "text-to-image", "diffusers", "controlnet"], "inference": true, "base_model": "runwayml/stable-diffusion-v1-5"}
text-to-image
Stepheni12/test-model-card-template-textual-inversion-sdxl
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
2024-02-13T05:22:48+00:00
[]
[]
TAGS #diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #controlnet #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us
# Textual inversion text2image fine-tuning - Stepheni12/test-model-card-template-textual-inversion-sdxl These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. !img_0 !img_1 !img_2 ## 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]
[ "# Textual inversion text2image fine-tuning - Stepheni12/test-model-card-template-textual-inversion-sdxl\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2", "## 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 #stable-diffusion-diffusers #text-to-image #controlnet #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n", "# Textual inversion text2image fine-tuning - Stepheni12/test-model-card-template-textual-inversion-sdxl\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2", "## 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]" ]
[ 68, 85, 9, 5, 24, 16 ]
[ "passage: TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #controlnet #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# Textual inversion text2image fine-tuning - Stepheni12/test-model-card-template-textual-inversion-sdxl\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2## 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]" ]
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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. --> # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7292 - Accuracy: 0.9 ## 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: - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0564 | 1.0 | 113 | 0.6135 | 0.83 | | 0.3513 | 2.0 | 226 | 0.5031 | 0.87 | | 0.3781 | 3.0 | 339 | 0.4387 | 0.89 | | 0.0142 | 4.0 | 452 | 0.6148 | 0.89 | | 0.188 | 5.0 | 565 | 0.8578 | 0.88 | | 0.0 | 6.0 | 678 | 1.0513 | 0.89 | | 0.4527 | 7.0 | 791 | 0.9157 | 0.88 | | 0.0 | 8.0 | 904 | 1.0898 | 0.85 | | 0.0 | 9.0 | 1017 | 0.7875 | 0.89 | | 0.0 | 10.0 | 1130 | 0.7292 | 0.9 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.2 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "bsd-3-clause", "tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "base_model": "MIT/ast-finetuned-audioset-10-10-0.4593", "model-index": [{"name": "ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "GTZAN", "type": "marsyas/gtzan", "config": "all", "split": "train", "args": "all"}, "metrics": [{"type": "accuracy", "value": 0.9, "name": "Accuracy"}]}]}]}
audio-classification
arshsin/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
[ "transformers", "tensorboard", "safetensors", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:MIT/ast-finetuned-audioset-10-10-0.4593", "license:bsd-3-clause", "model-index", "endpoints_compatible", "region:us" ]
2024-02-13T05:25:33+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #audio-spectrogram-transformer #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-MIT/ast-finetuned-audioset-10-10-0.4593 #license-bsd-3-clause #model-index #endpoints_compatible #region-us
ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan =================================================== This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the GTZAN dataset. It achieves the following results on the evaluation set: * Loss: 0.7292 * Accuracy: 0.9 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: * 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 * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 10 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * 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: 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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #audio-spectrogram-transformer #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-MIT/ast-finetuned-audioset-10-10-0.4593 #license-bsd-3-clause #model-index #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: 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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 94, 131, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #audio-spectrogram-transformer #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-MIT/ast-finetuned-audioset-10-10-0.4593 #license-bsd-3-clause #model-index #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: 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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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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. 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{"library_name": "transformers", "tags": []}
null
mitchaiet/charlie_fink_10k_Mistral7b_LoRa
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T05:28:25+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" ]
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[ "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" ]
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null
null
transformers
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/9FUUpJjbsR1GzXXnzwq9P.jpeg) Thank you to @konz00 for the gguf quants: https://huggingface.co/konz00/Cetus-Sea-7b-128k-GGUF ### Models Merged The following models were included in the merge: * [jeiku/Nitrals_Monster_7B](https://huggingface.co/jeiku/Nitrals_Monster_7B) * [Test157t/Prima-Pastacles-7b-128k](https://huggingface.co/Test157t/Prima-Pastacles-7b-128k) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: jeiku/Nitrals_Monster_7B layer_range: [0, 32] - model: Test157t/Prima-Pastacles-7b-128k layer_range: [0, 32] merge_method: slerp base_model: jeiku/Nitrals_Monster_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 ```
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["jeiku/Nitrals_Monster_7B", "Test157t/Prima-Pastacles-7b-128k"]}
text-generation
Test157t/Cetus-Sea-7b-128k
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:jeiku/Nitrals_Monster_7B", "base_model:Test157t/Prima-Pastacles-7b-128k", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T05:39:23+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-jeiku/Nitrals_Monster_7B #base_model-Test157t/Prima-Pastacles-7b-128k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
!image/jpeg Thank you to @konz00 for the gguf quants: URL ### Models Merged The following models were included in the merge: * jeiku/Nitrals_Monster_7B * Test157t/Prima-Pastacles-7b-128k ### Configuration The following YAML configuration was used to produce this model:
[ "### Models Merged\n\nThe following models were included in the merge:\n* jeiku/Nitrals_Monster_7B\n* Test157t/Prima-Pastacles-7b-128k", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-jeiku/Nitrals_Monster_7B #base_model-Test157t/Prima-Pastacles-7b-128k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Models Merged\n\nThe following models were included in the merge:\n* jeiku/Nitrals_Monster_7B\n* Test157t/Prima-Pastacles-7b-128k", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 91, 44, 17 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-jeiku/Nitrals_Monster_7B #base_model-Test157t/Prima-Pastacles-7b-128k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Models Merged\n\nThe following models were included in the merge:\n* jeiku/Nitrals_Monster_7B\n* Test157t/Prima-Pastacles-7b-128k### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="nirajandhakal/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "nirajan-dhakal", "dhakalnirajan"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
nirajandhakal/q-FrozenLake-v1-4x4-noSlippery
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "nirajan-dhakal", "dhakalnirajan", "model-index", "region:us" ]
2024-02-13T05:44:13+00:00
[]
[]
TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #nirajan-dhakal #dhakalnirajan #model-index #region-us
# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage
[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #nirajan-dhakal #dhakalnirajan #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
[ 51, 39 ]
[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #nirajan-dhakal #dhakalnirajan #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
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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 0.7.1
{"library_name": "peft", "base_model": "openai/whisper-small"}
null
unanam/smallloraft-v3
[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:openai/whisper-small", "region:us" ]
2024-02-13T05:46:07+00:00
[ "1910.09700" ]
[]
TAGS #peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-openai/whisper-small #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 0.7.1
[ "# 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 0.7.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-openai/whisper-small #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 0.7.1" ]
[ 41, 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 #tensorboard #safetensors #arxiv-1910.09700 #base_model-openai/whisper-small #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 0.7.1" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
Rajesh222/phi-2_riddles-evolved
[ "transformers", "safetensors", "phi", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T05:46:13+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" ]
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[ "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" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
gmonsoon/Qwen1.5-0.5B-Donut
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T05:48:32+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #qwen2 #text-generation #conversational #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 #qwen2 #text-generation #conversational #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" ]
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[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #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" ]
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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": "beomi/polyglot-ko-12.8b-safetensors"}
null
kasiwoos/polyglot-ko-12.8b-20000step
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/polyglot-ko-12.8b-safetensors", "region:us" ]
2024-02-13T05:52:52+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" ]
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## Exllama v2 Quantizations of samantha-1.1-westlake-7b Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/cognitivecomputations/samantha-1.1-westlake-7b/ | Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------------ | | [8_0](https://huggingface.co/bartowski/samantha-1.1-westlake-7b-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | [6_5](https://huggingface.co/bartowski/samantha-1.1-westlake-7b-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. | | [5_0](https://huggingface.co/bartowski/samantha-1.1-westlake-7b-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. | | [4_25](https://huggingface.co/bartowski/samantha-1.1-westlake-7b-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. | | [3_5](https://huggingface.co/bartowski/samantha-1.1-westlake-7b-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. | ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/samantha-1.1-westlake-7b-exl2 samantha-1.1-westlake-7b-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `samantha-1.1-westlake-7b-exl2`: ```shell mkdir samantha-1.1-westlake-7b-exl2 huggingface-cli download bartowski/samantha-1.1-westlake-7b-exl2 --local-dir samantha-1.1-westlake-7b-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir samantha-1.1-westlake-7b-exl2-6_5 huggingface-cli download bartowski/samantha-1.1-westlake-7b-exl2 --revision 6_5 --local-dir samantha-1.1-westlake-7b-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir samantha-1.1-westlake-7b-exl2-6.5 huggingface-cli download bartowski/samantha-1.1-westlake-7b-exl2 --revision 6_5 --local-dir samantha-1.1-westlake-7b-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
{"license": "apache-2.0", "datasets": ["cognitivecomputations/samantha-data"], "quantized_by": "bartowski", "pipeline_tag": "text-generation"}
text-generation
bartowski/samantha-1.1-westlake-7b-exl2
[ "text-generation", "dataset:cognitivecomputations/samantha-data", "license:apache-2.0", "region:us" ]
2024-02-13T05:55:08+00:00
[]
[]
TAGS #text-generation #dataset-cognitivecomputations/samantha-data #license-apache-2.0 #region-us
Exllama v2 Quantizations of samantha-1.1-westlake-7b ---------------------------------------------------- Using <a href="URL ExLlamaV2 v0.0.13 for quantization. **The "main" branch only contains the URL, download one of the other branches for the model (see below)** Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions. Original model: URL Download instructions --------------------- With git: With huggingface hub (credit to TheBloke for instructions): To download the 'main' (only useful if you only care about URL) branch to a folder called 'samantha-1.1-westlake-7b-exl2': To download from a different branch, add the '--revision' parameter: Linux: Windows (which apparently doesn't like \_ in folders sometimes?): Want to support my work? Visit my ko-fi page here: URL
[]
[ "TAGS\n#text-generation #dataset-cognitivecomputations/samantha-data #license-apache-2.0 #region-us \n" ]
[ 33 ]
[ "passage: TAGS\n#text-generation #dataset-cognitivecomputations/samantha-data #license-apache-2.0 #region-us \n" ]
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null
null
transformers
{ "version": "v0.7.1_H100-80GB-HBM3", "name": "engines_v2", "args": { "engine": { "repo": "Gryphe/MythoMax-L2-13b", "args": { "max_input_len": 2000, "max_output_len": 2000, "max_batch_size": 128 }, "type": "llama", "quant": "smooth_quant", "calibration": { "sq_alpha": 0.5 } } } }
{}
null
baseten/Gryphe_MythoMax-L2-13b_v0.7.1_H100-80GB-HBM3_2ff724
[ "transformers", "endpoints_compatible", "region:us" ]
2024-02-13T05:57:03+00:00
[]
[]
TAGS #transformers #endpoints_compatible #region-us
{ "version": "v0.7.1_H100-80GB-HBM3", "name": "engines_v2", "args": { "engine": { "repo": "Gryphe/MythoMax-L2-13b", "args": { "max_input_len": 2000, "max_output_len": 2000, "max_batch_size": 128 }, "type": "llama", "quant": "smooth_quant", "calibration": { "sq_alpha": 0.5 } } } }
[]
[ "TAGS\n#transformers #endpoints_compatible #region-us \n" ]
[ 17 ]
[ "passage: TAGS\n#transformers #endpoints_compatible #region-us \n" ]
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null
null
transformers
# SpaghettiOs ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/5h0fNQ76xA9OzAjs38oUZ.jpeg) This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [Test157t/Pasta-PrimaMaid-7b](https://huggingface.co/Test157t/Pasta-PrimaMaid-7b) as a base. ### Models Merged The following models were included in the merge: * [Test157t/Pasta-PrimaMaid-7b](https://huggingface.co/Test157t/Pasta-PrimaMaid-7b) + [jeiku/Theory_of_Mind_Mistral](https://huggingface.co/jeiku/Theory_of_Mind_Mistral) * [Test157t/Pasta-PrimaMaid-7b](https://huggingface.co/Test157t/Pasta-PrimaMaid-7b) + [jeiku/Futadom_Mistral](https://huggingface.co/jeiku/Futadom_Mistral) * [Test157t/Pasta-PrimaMaid-7b](https://huggingface.co/Test157t/Pasta-PrimaMaid-7b) + [jeiku/Theory_of_Mind_Roleplay_Mistral](https://huggingface.co/jeiku/Theory_of_Mind_Roleplay_Mistral) * [Test157t/Pasta-PrimaMaid-7b](https://huggingface.co/Test157t/Pasta-PrimaMaid-7b) + [jeiku/Humiliation_Mistral](https://huggingface.co/jeiku/Humiliation_Mistral) * [Test157t/Pasta-PrimaMaid-7b](https://huggingface.co/Test157t/Pasta-PrimaMaid-7b) + [jeiku/Gnosis_Reformatted_Mistral](https://huggingface.co/jeiku/Gnosis_Reformatted_Mistral) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic base_model: Test157t/Pasta-PrimaMaid-7b parameters: normalize: true models: - model: Test157t/Pasta-PrimaMaid-7b+jeiku/Theory_of_Mind_Roleplay_Mistral parameters: weight: 0.5 - model: Test157t/Pasta-PrimaMaid-7b+jeiku/Theory_of_Mind_Mistral parameters: weight: 0.65 - model: Test157t/Pasta-PrimaMaid-7b+jeiku/Gnosis_Reformatted_Mistral parameters: weight: 0.75 - model: Test157t/Pasta-PrimaMaid-7b+jeiku/Futadom_Mistral parameters: weight: 0.8 - model: Test157t/Pasta-PrimaMaid-7b+jeiku/Humiliation_Mistral parameters: weight: 0.6 dtype: float16 ```
{"tags": ["mergekit", "merge"], "base_model": ["Test157t/Pasta-PrimaMaid-7b", "jeiku/Theory_of_Mind_Mistral", "Test157t/Pasta-PrimaMaid-7b", "jeiku/Futadom_Mistral", "Test157t/Pasta-PrimaMaid-7b", "jeiku/Theory_of_Mind_Roleplay_Mistral", "Test157t/Pasta-PrimaMaid-7b", "Test157t/Pasta-PrimaMaid-7b", "jeiku/Humiliation_Mistral", "Test157t/Pasta-PrimaMaid-7b", "jeiku/Gnosis_Reformatted_Mistral"]}
text-generation
jeiku/SpaghettiOs_7B
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2212.04089", "base_model:Test157t/Pasta-PrimaMaid-7b", "base_model:jeiku/Theory_of_Mind_Mistral", "base_model:jeiku/Futadom_Mistral", "base_model:jeiku/Theory_of_Mind_Roleplay_Mistral", "base_model:jeiku/Humiliation_Mistral", "base_model:jeiku/Gnosis_Reformatted_Mistral", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:01:24+00:00
[ "2212.04089" ]
[]
TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2212.04089 #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-jeiku/Theory_of_Mind_Mistral #base_model-jeiku/Futadom_Mistral #base_model-jeiku/Theory_of_Mind_Roleplay_Mistral #base_model-jeiku/Humiliation_Mistral #base_model-jeiku/Gnosis_Reformatted_Mistral #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# SpaghettiOs !image/jpeg This is a merge of pre-trained language models created using mergekit. ## Merge Details ### Merge Method This model was merged using the task arithmetic merge method using Test157t/Pasta-PrimaMaid-7b as a base. ### Models Merged The following models were included in the merge: * Test157t/Pasta-PrimaMaid-7b + jeiku/Theory_of_Mind_Mistral * Test157t/Pasta-PrimaMaid-7b + jeiku/Futadom_Mistral * Test157t/Pasta-PrimaMaid-7b + jeiku/Theory_of_Mind_Roleplay_Mistral * Test157t/Pasta-PrimaMaid-7b + jeiku/Humiliation_Mistral * Test157t/Pasta-PrimaMaid-7b + jeiku/Gnosis_Reformatted_Mistral ### Configuration The following YAML configuration was used to produce this model:
[ "# SpaghettiOs\n\n!image/jpeg\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using Test157t/Pasta-PrimaMaid-7b as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Theory_of_Mind_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Futadom_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Theory_of_Mind_Roleplay_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Humiliation_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Gnosis_Reformatted_Mistral", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2212.04089 #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-jeiku/Theory_of_Mind_Mistral #base_model-jeiku/Futadom_Mistral #base_model-jeiku/Theory_of_Mind_Roleplay_Mistral #base_model-jeiku/Humiliation_Mistral #base_model-jeiku/Gnosis_Reformatted_Mistral #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# SpaghettiOs\n\n!image/jpeg\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using Test157t/Pasta-PrimaMaid-7b as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Theory_of_Mind_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Futadom_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Theory_of_Mind_Roleplay_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Humiliation_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Gnosis_Reformatted_Mistral", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 165, 25, 4, 37, 149, 17 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2212.04089 #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-jeiku/Theory_of_Mind_Mistral #base_model-jeiku/Futadom_Mistral #base_model-jeiku/Theory_of_Mind_Roleplay_Mistral #base_model-jeiku/Humiliation_Mistral #base_model-jeiku/Gnosis_Reformatted_Mistral #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SpaghettiOs\n\n!image/jpeg\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the task arithmetic merge method using Test157t/Pasta-PrimaMaid-7b as a base.### Models Merged\n\nThe following models were included in the merge:\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Theory_of_Mind_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Futadom_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Theory_of_Mind_Roleplay_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Humiliation_Mistral\n* Test157t/Pasta-PrimaMaid-7b + jeiku/Gnosis_Reformatted_Mistral### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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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
bhuvanmdev/falcon-7b-resume-parser
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T06:07:19+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" ]
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[ "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" ]
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null
null
transformers
# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
{"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]}
text-generation
Jimmyhd/llama213bAdarshDataset
[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:09:51+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit AutoTrain. # Usage
[ "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]
[ 60, 29, 3 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage" ]
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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. --> # t5-small-finetuned-2048 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 13.3433 - Rouge1: 0.029 - Rouge2: 0.0023 - Rougel: 0.0267 - Rougelsum: 0.0284 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 0.67 | 1 | 25.1883 | 0.0242 | 0.0023 | 0.0218 | 0.0241 | | No log | 2.0 | 3 | 23.4392 | 0.0242 | 0.0023 | 0.0218 | 0.0241 | | No log | 2.67 | 4 | 22.5166 | 0.0252 | 0.0023 | 0.0229 | 0.0251 | | No log | 4.0 | 6 | 20.6643 | 0.0252 | 0.0023 | 0.0229 | 0.0251 | | No log | 4.67 | 7 | 19.7334 | 0.0252 | 0.0023 | 0.0229 | 0.0251 | | No log | 6.0 | 9 | 17.8137 | 0.0252 | 0.0023 | 0.0229 | 0.0251 | | No log | 6.67 | 10 | 17.1117 | 0.0252 | 0.0023 | 0.0229 | 0.0251 | | No log | 8.0 | 12 | 16.4384 | 0.0329 | 0.005 | 0.0269 | 0.0324 | | No log | 8.67 | 13 | 16.2401 | 0.0329 | 0.005 | 0.0269 | 0.0324 | | No log | 10.0 | 15 | 15.9056 | 0.0329 | 0.005 | 0.0269 | 0.0324 | | No log | 10.67 | 16 | 15.7547 | 0.0329 | 0.005 | 0.0269 | 0.0324 | | No log | 12.0 | 18 | 15.4599 | 0.0329 | 0.005 | 0.0269 | 0.0324 | | No log | 12.67 | 19 | 15.3192 | 0.0329 | 0.005 | 0.0269 | 0.0324 | | 17.3983 | 14.0 | 21 | 15.0513 | 0.0329 | 0.005 | 0.0269 | 0.0324 | | 17.3983 | 14.67 | 22 | 14.9270 | 0.0367 | 0.005 | 0.0307 | 0.0357 | | 17.3983 | 16.0 | 24 | 14.7037 | 0.0367 | 0.005 | 0.0307 | 0.0357 | | 17.3983 | 16.67 | 25 | 14.5987 | 0.0367 | 0.005 | 0.0307 | 0.0357 | | 17.3983 | 18.0 | 27 | 14.4010 | 0.0367 | 0.005 | 0.0307 | 0.0357 | | 17.3983 | 18.67 | 28 | 14.3084 | 0.0367 | 0.005 | 0.0307 | 0.0357 | | 17.3983 | 20.0 | 30 | 14.1348 | 0.0367 | 0.005 | 0.0307 | 0.0357 | | 17.3983 | 20.67 | 31 | 14.0554 | 0.0367 | 0.005 | 0.0307 | 0.0357 | | 17.3983 | 22.0 | 33 | 13.9103 | 0.0367 | 0.005 | 0.0307 | 0.0357 | | 17.3983 | 22.67 | 34 | 13.8446 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 17.3983 | 24.0 | 36 | 13.7251 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 17.3983 | 24.67 | 37 | 13.6713 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 17.3983 | 26.0 | 39 | 13.5781 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 13.2153 | 26.67 | 40 | 13.5376 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 13.2153 | 28.0 | 42 | 13.4689 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 13.2153 | 28.67 | 43 | 13.4408 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 13.2153 | 30.0 | 45 | 13.3953 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 13.2153 | 30.67 | 46 | 13.3780 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 13.2153 | 32.0 | 48 | 13.3538 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 13.2153 | 32.67 | 49 | 13.3468 | 0.029 | 0.0023 | 0.0267 | 0.0284 | | 13.2153 | 33.33 | 50 | 13.3433 | 0.029 | 0.0023 | 0.0267 | 0.0284 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["RMWeerasinghe/BoardPapers-small"], "metrics": ["rouge"], "base_model": "google-t5/t5-small", "pipeline_tag": "summarization", "model-index": [{"name": "t5-small-finetuned-2048", "results": []}]}
summarization
RMWeerasinghe/t5-small-finetuned-2048
[ "transformers", "safetensors", "t5", "text2text-generation", "summarization", "generated_from_trainer", "dataset:RMWeerasinghe/BoardPapers-small", "base_model:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:10:02+00:00
[]
[]
TAGS #transformers #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #dataset-RMWeerasinghe/BoardPapers-small #base_model-google-t5/t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
t5-small-finetuned-2048 ======================= This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 13.3433 * Rouge1: 0.029 * Rouge2: 0.0023 * Rougel: 0.0267 * Rougelsum: 0.0284 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 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * 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: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #dataset-RMWeerasinghe/BoardPapers-small #base_model-google-t5/t5-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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 99, 126, 4, 35 ]
[ "passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #dataset-RMWeerasinghe/BoardPapers-small #base_model-google-t5/t5-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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
null
# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
{"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]}
text-generation
rohit2432/fight-club-test
[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]
2024-02-13T06:13:51+00:00
[]
[]
TAGS #safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us
# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit AutoTrain. # Usage
[ "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]
[ "TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]
[ 37, 29, 3 ]
[ "passage: TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage" ]
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null
null
transformers
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/Gs09MsXrzyJdDx7IrnkDa.jpeg) ### Models Merged The following models were included in the merge: * [Test157t/Prima-Pastacles-7b](https://huggingface.co/Test157t/Prima-Pastacles-7b) * [Test157t/Pasta-Sea-7b-128k](https://huggingface.co/Test157t/Pasta-Sea-7b-128k) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Test157t/Prima-Pastacles-7b layer_range: [0, 32] - model: Test157t/Pasta-Sea-7b-128k layer_range: [0, 32] merge_method: slerp base_model: Test157t/Prima-Pastacles-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 ```
{"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Test157t/Prima-Pastacles-7b", "Test157t/Pasta-Sea-7b-128k"]}
text-generation
Test157t/Prima-Pastacles-7b-128k
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:Test157t/Prima-Pastacles-7b", "base_model:Test157t/Pasta-Sea-7b-128k", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:15:24+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Prima-Pastacles-7b #base_model-Test157t/Pasta-Sea-7b-128k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
!image/jpeg ### Models Merged The following models were included in the merge: * Test157t/Prima-Pastacles-7b * Test157t/Pasta-Sea-7b-128k ### Configuration The following YAML configuration was used to produce this model:
[ "### Models Merged\n\nThe following models were included in the merge:\n* Test157t/Prima-Pastacles-7b\n* Test157t/Pasta-Sea-7b-128k", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Prima-Pastacles-7b #base_model-Test157t/Pasta-Sea-7b-128k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Models Merged\n\nThe following models were included in the merge:\n* Test157t/Prima-Pastacles-7b\n* Test157t/Pasta-Sea-7b-128k", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 95, 43, 17 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Prima-Pastacles-7b #base_model-Test157t/Pasta-Sea-7b-128k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Models Merged\n\nThe following models were included in the merge:\n* Test157t/Prima-Pastacles-7b\n* Test157t/Pasta-Sea-7b-128k### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="nirajandhakal/Taxi-v3-Qtable-1M-Steps", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3-Qtable-1M-Steps", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
nirajandhakal/Taxi-v3-Qtable-1M-Steps
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
2024-02-13T06:21:51+00:00
[]
[]
TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage
[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ 32, 33 ]
[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
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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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{"library_name": "transformers", "tags": []}
automatic-speech-recognition
SpideyDLK/wav2vec2-large-xls-r-300m-sinhala-test4-with-checkpoints-part2
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T06:26:12+00:00
[ "1910.09700" ]
[]
TAGS #transformers #tensorboard #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 #tensorboard #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" ]
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[ "passage: TAGS\n#transformers #tensorboard #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" ]
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null
null
transformers
Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### User: How do you fine tune a large language model? ### Assistant: ``` License Link: https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md
{"license": "other", "license_name": "general-model-license", "license_link": "https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md"}
text-generation
KnutJaegersberg/Deita-2b
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:28:05+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
Prompt Example: License Link: URL
[]
[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 56 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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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. 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{"library_name": "transformers", "tags": []}
null
HexawareTech/falcon-adapter
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T06:28:15+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" ]
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[ "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" ]
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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 Base Hu v6 - cleaned This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 16.1 hu cleaned dataset. It achieves the following results on the evaluation set: - Loss: 0.1682 - Wer Ortho: 14.5922 - Wer: 13.6440 ## 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: 2.5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 100 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.0009 | 6.64 | 1000 | 0.1682 | 14.5922 | 13.6440 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"language": ["hu"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_0"], "metrics": ["wer"], "widget": [{"example_title": "Sample 1", "src": "https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample1.flac"}, {"example_title": "Sample 2", "src": "https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample2.flac"}], "base_model": "openai/whisper-base", "model-index": [{"name": "Whisper Base Hu Cleaned", "results": []}]}
automatic-speech-recognition
Hungarians/whisper-base-hu-cleaned
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hu", "dataset:mozilla-foundation/common_voice_16_0", "base_model:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-13T06:29:33+00:00
[]
[ "hu" ]
TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #hu #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-base #license-apache-2.0 #endpoints_compatible #region-us
Whisper Base Hu v6 - cleaned ============================ This model is a fine-tuned version of openai/whisper-base on the Common Voice 16.1 hu cleaned dataset. It achieves the following results on the evaluation set: * Loss: 0.1682 * Wer Ortho: 14.5922 * Wer: 13.6440 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: 2.5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * gradient\_accumulation\_steps: 8 * total\_train\_batch\_size: 256 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant\_with\_warmup * lr\_scheduler\_warmup\_steps: 100 * training\_steps: 1000 * 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
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP", "### 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 #whisper #automatic-speech-recognition #generated_from_trainer #hu #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-base #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP", "### 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" ]
[ 89, 165, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #hu #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-base #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP### 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" ]
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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. 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{"library_name": "transformers", "tags": []}
null
kenchenxingyu/flan-large-lora-stance-human3
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T06:31:32+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" ]
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[ "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" ]
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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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{"library_name": "transformers", "tags": []}
text-classification
itsyasin2002ai/dummy-model
[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T06:34:09+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #bert #text-classification #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 #bert #text-classification #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" ]
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[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #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" ]
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null
null
keras
## 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: | Hyperparameters | Value | | :-- | :-- | | inner_optimizer.module | keras.optimizers.experimental | | inner_optimizer.class_name | RMSprop | | inner_optimizer.config.name | RMSprop | | inner_optimizer.config.weight_decay | None | | inner_optimizer.config.clipnorm | None | | inner_optimizer.config.global_clipnorm | None | | inner_optimizer.config.clipvalue | None | | inner_optimizer.config.use_ema | False | | inner_optimizer.config.ema_momentum | 0.99 | | inner_optimizer.config.ema_overwrite_frequency | 100 | | inner_optimizer.config.jit_compile | True | | inner_optimizer.config.is_legacy_optimizer | False | | inner_optimizer.config.learning_rate | 0.0010000000474974513 | | inner_optimizer.config.rho | 0.9 | | inner_optimizer.config.momentum | 0.0 | | inner_optimizer.config.epsilon | 1e-07 | | inner_optimizer.config.centered | False | | inner_optimizer.registered_name | None | | dynamic | True | | initial_scale | 32768.0 | | dynamic_growth_steps | 2000 | | training_precision | mixed_float16 |
{"library_name": "keras"}
null
oustanding/ura-exploration-w2024
[ "keras", "region:us" ]
2024-02-13T06:38:39+00:00
[]
[]
TAGS #keras #region-us
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:
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:" ]
[ "TAGS\n#keras #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:" ]
[ 9, 18 ]
[ "passage: TAGS\n#keras #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:" ]
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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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{"library_name": "transformers", "tags": []}
text-classification
Amanaccessassist/Gender-Classification
[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T06:39:23+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #roberta #text-classification #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 #roberta #text-classification #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" ]
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[ "passage: TAGS\n#transformers #safetensors #roberta #text-classification #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" ]
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null
null
transformers
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{"library_name": "transformers", "tags": ["unsloth"]}
text-generation
ybelkada/test-tiny-llama-unsloth
[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:40:55+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #unsloth #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 #unsloth #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" ]
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[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #unsloth #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" ]
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<style> .title-container { display: flex; justify-content: center; align-items: center; height: 100vh; /* Adjust this value to position the title vertically */ } .title { font-size: 2.5em; text-align: center; color: RED; font-family: 'Helvetica Neue', sans-serif; text-transform: uppercase; letter-spacing: 0.1em; padding: 0.5em 0; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, RED,BLACK); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .custom-table { table-layout: fixed; width: 100%; border-collapse: collapse; margin-top: 2em; } .custom-table td { width: 50%; vertical-align: top; padding: 10px; box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15); } .custom-image-container { position: relative; width: 100%; margin-bottom: 0em; overflow: hidden; border-radius: 10px; transition: transform .7s; /* Smooth transition for the container */ } .custom-image-container:hover { transform: scale(1.05); /* Scale the container on hover */ } .custom-image { width: auto; height: auto; object-fit: cover; border-radius: 10px; transition: transform .7s; margin-bottom: 0em; } .nsfw-filter { filter: blur(8px); /* Apply a blur effect */ transition: filter 0.3s ease; /* Smooth transition for the blur effect */ } .custom-image-container:hover .nsfw-filter { filter: none; /* Remove the blur effect on hover */ } .overlay { position: absolute; bottom: 0; left: 0; right: 0; color: white; width: 100%; height: 40%; display: flex; flex-direction: column; justify-content: center; align-items: center; font-size: 1vw; font-style: bold; text-align: center; opacity: 0; /* Keep the text fully opaque */ background: linear-gradient(0deg, rgba(0, 0, 0, 0.8) 60%, rgba(0, 0, 0, 0) 100%); transition: opacity .5s; } .custom-image-container:hover .overlay { opacity: 1; /* Make the overlay always visible */ } .overlay-text { background: linear-gradient(45deg, #00000, #0000); -webkit-background-clip: text; color: transparent; /* Fallback for browsers that do not support this effect */ text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7); /* Enhanced text shadow for better legibility */ .overlay-subtext { font-size: 0.75em; margin-top: 0.5em; font-style: italic; } .overlay, .overlay-subtext { text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5); } </style> <h1 class="title"> <span>AnimeSai</span> </h1> <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="https://enhanceai.s3.amazonaws.com/0acc94f4-4cb4-46fa-9fc9-ed8c24f7292a_1.png" alt="sample1"> </div> <div class="custom-image-container"> <img class="custom-image" src="https://enhanceai.s3.amazonaws.com/6d3e36d7-1376-48d1-8d4d-09615de790eb_1.png" alt="sample4"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://enhanceai.s3.amazonaws.com/00500d81-f096-4167-b7cf-01830d2cc925_1.png" alt="sample2"> </div> <div class="custom-image-container"> <img class="custom-image" src="https://enhanceai.s3.amazonaws.com/5bb06f9c-4e4c-4ee1-b356-9c9400181a19_1.png" alt="sample3"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://enhanceai.s3.amazonaws.com/9b82d531-2ab2-4e47-94d3-f46d3bd4e9f4_1.png" alt="sample1"> </div> <div class="custom-image-container"> <img class="custom-image" src="https://enhanceai.s3.amazonaws.com/ba6485de-1e75-44fc-9357-e6ec34e2005a_1.png" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://enhanceai.s3.amazonaws.com/ad179bdc-191e-4d51-a671-82c5af3c2ec1_1.png" alt="sample1"> </div> <div class="custom-image-container"> <img class="custom-image" src="https://enhanceai.s3.amazonaws.com/8e11fccd-bba3-4a88-bb0b-2fbdaad2437f_1.png" alt="sample1"> </div> </td> </tr> </table> ## Overview **AnimeSai** is Advance Anime Image Generate Ai Model From [EnhanceAi](https://enhanceai.art) ## Model Details - **Try Now:** [EnhanceAi](https://enhanceai.art) 200 Image Generate Free - **Developer By:** Pranav Ajay & Kushal Saho
{"tags": ["text-to-image", "stable-diffusion", "safetensors", "stable-diffusion-xl", "text-generator", "image-generator", "ai", "image-to-image", "inpainting", "image-to-inpainting", "anime"]}
text-to-image
enhanceaiart/AnimeSai
[ "text-to-image", "stable-diffusion", "safetensors", "stable-diffusion-xl", "text-generator", "image-generator", "ai", "image-to-image", "inpainting", "image-to-inpainting", "anime", "region:us" ]
2024-02-13T06:46:08+00:00
[]
[]
TAGS #text-to-image #stable-diffusion #safetensors #stable-diffusion-xl #text-generator #image-generator #ai #image-to-image #inpainting #image-to-inpainting #anime #region-us
<style> .title-container { display: flex; justify-content: center; align-items: center; height: 100vh; /* Adjust this value to position the title vertically */ } .title { font-size: 2.5em; text-align: center; color: RED; font-family: 'Helvetica Neue', sans-serif; text-transform: uppercase; letter-spacing: 0.1em; padding: 0.5em 0; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, RED,BLACK); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .custom-table { table-layout: fixed; width: 100%; border-collapse: collapse; margin-top: 2em; } .custom-table td { width: 50%; vertical-align: top; padding: 10px; box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15); } .custom-image-container { position: relative; width: 100%; margin-bottom: 0em; overflow: hidden; border-radius: 10px; transition: transform .7s; /* Smooth transition for the container */ } .custom-image-container:hover { transform: scale(1.05); /* Scale the container on hover */ } .custom-image { width: auto; height: auto; object-fit: cover; border-radius: 10px; transition: transform .7s; margin-bottom: 0em; } .nsfw-filter { filter: blur(8px); /* Apply a blur effect */ transition: filter 0.3s ease; /* Smooth transition for the blur effect */ } .custom-image-container:hover .nsfw-filter { filter: none; /* Remove the blur effect on hover */ } .overlay { position: absolute; bottom: 0; left: 0; right: 0; color: white; width: 100%; height: 40%; display: flex; flex-direction: column; justify-content: center; align-items: center; font-size: 1vw; font-style: bold; text-align: center; opacity: 0; /* Keep the text fully opaque */ background: linear-gradient(0deg, rgba(0, 0, 0, 0.8) 60%, rgba(0, 0, 0, 0) 100%); transition: opacity .5s; } .custom-image-container:hover .overlay { opacity: 1; /* Make the overlay always visible */ } .overlay-text { background: linear-gradient(45deg, #00000, #0000); -webkit-background-clip: text; color: transparent; /* Fallback for browsers that do not support this effect */ text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7); /* Enhanced text shadow for better legibility */ .overlay-subtext { font-size: 0.75em; margin-top: 0.5em; font-style: italic; } .overlay, .overlay-subtext { text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5); } </style> <h1 class="title"> <span>AnimeSai</span> </h1> <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample4"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample2"> </div> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample3"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> </tr> </table> ## Overview AnimeSai is Advance Anime Image Generate Ai Model From EnhanceAi ## Model Details - Try Now: EnhanceAi 200 Image Generate Free - Developer By: Pranav Ajay & Kushal Saho
[ "## Overview \n\nAnimeSai is Advance Anime Image Generate Ai Model From EnhanceAi", "## Model Details\n\n- Try Now: EnhanceAi 200 Image Generate Free\n- Developer By: Pranav Ajay & Kushal Saho" ]
[ "TAGS\n#text-to-image #stable-diffusion #safetensors #stable-diffusion-xl #text-generator #image-generator #ai #image-to-image #inpainting #image-to-inpainting #anime #region-us \n", "## Overview \n\nAnimeSai is Advance Anime Image Generate Ai Model From EnhanceAi", "## Model Details\n\n- Try Now: EnhanceAi 200 Image Generate Free\n- Developer By: Pranav Ajay & Kushal Saho" ]
[ 66, 19, 29 ]
[ "passage: TAGS\n#text-to-image #stable-diffusion #safetensors #stable-diffusion-xl #text-generator #image-generator #ai #image-to-image #inpainting #image-to-inpainting #anime #region-us \n## Overview \n\nAnimeSai is Advance Anime Image Generate Ai Model From EnhanceAi## Model Details\n\n- Try Now: EnhanceAi 200 Image Generate Free\n- Developer By: Pranav Ajay & Kushal Saho" ]
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null
null
peft
## Training procedure ### Framework versions - PEFT 0.5.0
{"library_name": "peft"}
null
noza-kit/llama2_alpaca_mexed3
[ "peft", "safetensors", "region:us" ]
2024-02-13T06:47:44+00:00
[]
[]
TAGS #peft #safetensors #region-us
## Training procedure ### Framework versions - PEFT 0.5.0
[ "## Training procedure", "### Framework versions\n\n\n- PEFT 0.5.0" ]
[ "TAGS\n#peft #safetensors #region-us \n", "## Training procedure", "### Framework versions\n\n\n- PEFT 0.5.0" ]
[ 14, 3, 11 ]
[ "passage: TAGS\n#peft #safetensors #region-us \n## Training procedure### Framework versions\n\n\n- PEFT 0.5.0" ]
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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. --> # opt-1.3b-squad-model3 This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the squad 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 2 - 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 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "other", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "facebook/opt-1.3b", "model-index": [{"name": "opt-1.3b-squad-model3", "results": []}]}
question-answering
varun-v-rao/opt-1.3b-squad-model3
[ "transformers", "tensorboard", "safetensors", "opt", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:facebook/opt-1.3b", "license:other", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:52:38+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-1.3b #license-other #endpoints_compatible #text-generation-inference #region-us
# opt-1.3b-squad-model3 This model is a fine-tuned version of facebook/opt-1.3b on the squad 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 2 - 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 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
[ "# opt-1.3b-squad-model3\n\nThis model is a fine-tuned version of facebook/opt-1.3b on the squad 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: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 2\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 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-1.3b #license-other #endpoints_compatible #text-generation-inference #region-us \n", "# opt-1.3b-squad-model3\n\nThis model is a fine-tuned version of facebook/opt-1.3b on the squad 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: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 2\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 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]
[ 81, 35, 6, 12, 8, 3, 90, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-1.3b #license-other #endpoints_compatible #text-generation-inference #region-us \n# opt-1.3b-squad-model3\n\nThis model is a fine-tuned version of facebook/opt-1.3b on the squad 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: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 2\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 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]
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null
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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. --> # t5-small-finetuned-govReport-3072 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the govreport-summarization dataset. It achieves the following results on the evaluation set: - Loss: 3.8367 - Rouge1: 0.0371 - Rouge2: 0.0142 - Rougel: 0.0316 - Rougelsum: 0.0352 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 19.9287 | 0.99 | 31 | 11.5775 | 0.0331 | 0.0151 | 0.0293 | 0.0317 | | 12.489 | 1.98 | 62 | 9.1322 | 0.0373 | 0.0162 | 0.0322 | 0.0351 | | 10.8693 | 2.98 | 93 | 7.8834 | 0.0367 | 0.0153 | 0.0327 | 0.0348 | | 9.1603 | 4.0 | 125 | 6.8580 | 0.0374 | 0.0162 | 0.0322 | 0.0355 | | 8.2587 | 4.99 | 156 | 5.7038 | 0.0382 | 0.0154 | 0.0326 | 0.0366 | | 6.6869 | 5.98 | 187 | 4.8553 | 0.0388 | 0.0159 | 0.0341 | 0.037 | | 5.8997 | 6.98 | 218 | 4.3049 | 0.0383 | 0.0145 | 0.0336 | 0.036 | | 5.0285 | 8.0 | 250 | 3.9143 | 0.0369 | 0.0138 | 0.0311 | 0.035 | | 4.5944 | 8.99 | 281 | 3.8533 | 0.0376 | 0.0149 | 0.032 | 0.0353 | | 4.5239 | 9.92 | 310 | 3.8367 | 0.0371 | 0.0142 | 0.0316 | 0.0352 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["govreport-summarization"], "metrics": ["rouge"], "base_model": "google-t5/t5-small", "pipeline_tag": "summarization", "model-index": [{"name": "t5-small-finetuned-govReport-3072", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "govreport-summarization", "type": "govreport-summarization", "config": "document", "split": "validation", "args": "document"}, "metrics": [{"type": "rouge", "value": 0.0371, "name": "Rouge1"}]}]}]}
summarization
RMWeerasinghe/t5-small-finetuned-govReport-3072
[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "summarization", "generated_from_trainer", "dataset:govreport-summarization", "base_model:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:53:08+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #dataset-govreport-summarization #base_model-google-t5/t5-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
t5-small-finetuned-govReport-3072 ================================= This model is a fine-tuned version of google-t5/t5-small on the govreport-summarization dataset. It achieves the following results on the evaluation set: * Loss: 3.8367 * Rouge1: 0.0371 * Rouge2: 0.0142 * Rougel: 0.0316 * Rougelsum: 0.0352 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 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### 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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 10", "### 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 #t5 #text2text-generation #summarization #generated_from_trainer #dataset-govreport-summarization #base_model-google-t5/t5-small #license-apache-2.0 #model-index #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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 10", "### 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" ]
[ 100, 126, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #summarization #generated_from_trainer #dataset-govreport-summarization #base_model-google-t5/t5-small #license-apache-2.0 #model-index #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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\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: linear\n* num\\_epochs: 10### 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" ]
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null
null
transformers
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/BbNKAgz3sRPbzedRZnFib.jpeg) Thanks to @konz00 for the GGUF Quants: https://huggingface.co/konz00/Hex-Macaroniac-7b-GGUF ### Models Merged The following models were included in the merge: * [jeiku/SpaghettiOs_7B](https://huggingface.co/jeiku/SpaghettiOs_7B) * [jeiku/Nitrals_Monster_7B](https://huggingface.co/jeiku/Nitrals_Monster_7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: jeiku/SpaghettiOs_7B layer_range: [0, 32] - model: jeiku/Nitrals_Monster_7B layer_range: [0, 32] merge_method: slerp base_model: jeiku/SpaghettiOs_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 ```
{"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["jeiku/SpaghettiOs_7B", "jeiku/Nitrals_Monster_7B"]}
text-generation
Test157t/Hex-Macaroniac-7b
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:jeiku/SpaghettiOs_7B", "base_model:jeiku/Nitrals_Monster_7B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:56:22+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-jeiku/SpaghettiOs_7B #base_model-jeiku/Nitrals_Monster_7B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
!image/jpeg Thanks to @konz00 for the GGUF Quants: URL ### Models Merged The following models were included in the merge: * jeiku/SpaghettiOs_7B * jeiku/Nitrals_Monster_7B ### Configuration The following YAML configuration was used to produce this model:
[ "### Models Merged\n\nThe following models were included in the merge:\n* jeiku/SpaghettiOs_7B\n* jeiku/Nitrals_Monster_7B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-jeiku/SpaghettiOs_7B #base_model-jeiku/Nitrals_Monster_7B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Models Merged\n\nThe following models were included in the merge:\n* jeiku/SpaghettiOs_7B\n* jeiku/Nitrals_Monster_7B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 91, 39, 17 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-jeiku/SpaghettiOs_7B #base_model-jeiku/Nitrals_Monster_7B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Models Merged\n\nThe following models were included in the merge:\n* jeiku/SpaghettiOs_7B\n* jeiku/Nitrals_Monster_7B### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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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. --> # new-dot-comp-v1 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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: 0.0002 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - 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
{"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "model-index": [{"name": "new-dot-comp-v1", "results": []}]}
text-generation
quriousclick/new-dot-comp-v1
[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T06:59:44+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# new-dot-comp-v1 This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 0.0002 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - 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
[ "# new-dot-comp-v1\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 0.0002\n- train_batch_size: 12\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\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 #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# new-dot-comp-v1\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 0.0002\n- train_batch_size: 12\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\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" ]
[ 98, 43, 6, 12, 8, 3, 125, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# new-dot-comp-v1\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 0.0002\n- train_batch_size: 12\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\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" ]
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null
null
transformers
# Ombingstrat-dare-ties-ultra2-7b Ombingstrat-dare-ties-ultra2-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [liminerity/Ombingstrat-dare-ties-ultra1-7b](https://huggingface.co/liminerity/Ombingstrat-dare-ties-ultra1-7b) * [liminerity/Omningotex-7b-slerp](https://huggingface.co/liminerity/Omningotex-7b-slerp) * [eren23/dpo-binarized-NeutrixOmnibe-7B](https://huggingface.co/eren23/dpo-binarized-NeutrixOmnibe-7B) * [paulml/DPOB-INMTOB-7B](https://huggingface.co/paulml/DPOB-INMTOB-7B) ## 🧩 Configuration ```yaml slices: - sources: - layer_range: [0, 9] model: liminerity/Ombingstrat-dare-ties-ultra1-7b - layer_range: [4, 13] model: liminerity/Omningotex-7b-slerp parameters: density: 0.61 weight: [0.22, 0.113, 0.113] - layer_range: [14, 23] model: eren23/dpo-binarized-NeutrixOmnibe-7B parameters: density: 0.61 weight: [0.22, 0.113, 0.113] - layer_range: [22, 31] model: paulml/DPOB-INMTOB-7B parameters: density: 0.59 weight: [0.02, 0.081, 0.081] base_model: liminerity/Ombingstrat-dare-ties-ultra1-7b dtype: bfloat16 merge_method: dare_ties parameters: int8_mask: 1.0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "liminerity/Ombingstrat-dare-ties-ultra2-7b" 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", "liminerity/Ombingstrat-dare-ties-ultra1-7b", "liminerity/Omningotex-7b-slerp", "eren23/dpo-binarized-NeutrixOmnibe-7B", "paulml/DPOB-INMTOB-7B"], "base_model": ["liminerity/Ombingstrat-dare-ties-ultra1-7b", "liminerity/Omningotex-7b-slerp", "eren23/dpo-binarized-NeutrixOmnibe-7B", "paulml/DPOB-INMTOB-7B"]}
text-generation
liminerity/Ombingstrat-dare-ties-ultra2-7b
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/Ombingstrat-dare-ties-ultra1-7b", "liminerity/Omningotex-7b-slerp", "eren23/dpo-binarized-NeutrixOmnibe-7B", "paulml/DPOB-INMTOB-7B", "base_model:liminerity/Ombingstrat-dare-ties-ultra1-7b", "base_model:liminerity/Omningotex-7b-slerp", "base_model:eren23/dpo-binarized-NeutrixOmnibe-7B", "base_model:paulml/DPOB-INMTOB-7B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T07:01:10+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #liminerity/Ombingstrat-dare-ties-ultra1-7b #liminerity/Omningotex-7b-slerp #eren23/dpo-binarized-NeutrixOmnibe-7B #paulml/DPOB-INMTOB-7B #base_model-liminerity/Ombingstrat-dare-ties-ultra1-7b #base_model-liminerity/Omningotex-7b-slerp #base_model-eren23/dpo-binarized-NeutrixOmnibe-7B #base_model-paulml/DPOB-INMTOB-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Ombingstrat-dare-ties-ultra2-7b Ombingstrat-dare-ties-ultra2-7b is a merge of the following models using LazyMergekit: * liminerity/Ombingstrat-dare-ties-ultra1-7b * liminerity/Omningotex-7b-slerp * eren23/dpo-binarized-NeutrixOmnibe-7B * paulml/DPOB-INMTOB-7B ## Configuration ## Usage
[ "# Ombingstrat-dare-ties-ultra2-7b\n\nOmbingstrat-dare-ties-ultra2-7b is a merge of the following models using LazyMergekit:\n* liminerity/Ombingstrat-dare-ties-ultra1-7b\n* liminerity/Omningotex-7b-slerp\n* eren23/dpo-binarized-NeutrixOmnibe-7B\n* paulml/DPOB-INMTOB-7B", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #liminerity/Ombingstrat-dare-ties-ultra1-7b #liminerity/Omningotex-7b-slerp #eren23/dpo-binarized-NeutrixOmnibe-7B #paulml/DPOB-INMTOB-7B #base_model-liminerity/Ombingstrat-dare-ties-ultra1-7b #base_model-liminerity/Omningotex-7b-slerp #base_model-eren23/dpo-binarized-NeutrixOmnibe-7B #base_model-paulml/DPOB-INMTOB-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Ombingstrat-dare-ties-ultra2-7b\n\nOmbingstrat-dare-ties-ultra2-7b is a merge of the following models using LazyMergekit:\n* liminerity/Ombingstrat-dare-ties-ultra1-7b\n* liminerity/Omningotex-7b-slerp\n* eren23/dpo-binarized-NeutrixOmnibe-7B\n* paulml/DPOB-INMTOB-7B", "## Configuration", "## Usage" ]
[ 206, 109, 4, 3 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #liminerity/Ombingstrat-dare-ties-ultra1-7b #liminerity/Omningotex-7b-slerp #eren23/dpo-binarized-NeutrixOmnibe-7B #paulml/DPOB-INMTOB-7B #base_model-liminerity/Ombingstrat-dare-ties-ultra1-7b #base_model-liminerity/Omningotex-7b-slerp #base_model-eren23/dpo-binarized-NeutrixOmnibe-7B #base_model-paulml/DPOB-INMTOB-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Ombingstrat-dare-ties-ultra2-7b\n\nOmbingstrat-dare-ties-ultra2-7b is a merge of the following models using LazyMergekit:\n* liminerity/Ombingstrat-dare-ties-ultra1-7b\n* liminerity/Omningotex-7b-slerp\n* eren23/dpo-binarized-NeutrixOmnibe-7B\n* paulml/DPOB-INMTOB-7B## Configuration## Usage" ]
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# **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Policy_Gradient_Pixelcopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "34.30 +/- 21.43", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
Hongsong/Policy_Gradient_Pixelcopter
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
2024-02-13T07:01:26+00:00
[]
[]
TAGS #Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ 41, 58 ]
[ "passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
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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. 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{"library_name": "transformers", "tags": []}
text-generation
worldboss/dpo-v2-worldboss-2.5-Mistral-7B-ft
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T07:01:30+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" ]
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[ "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" ]
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null
null
null
reuploaded 4 collab
{}
null
strxwta/1m-n4y30n
[ "region:us" ]
2024-02-13T07:08:45+00:00
[]
[]
TAGS #region-us
reuploaded 4 collab
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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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-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 63 | 4.4958 | ### 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-squad", "results": []}]}
question-answering
damianGil/distilbert-base-uncased-finetuned-squad
[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-13T07:11:40+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
distilbert-base-uncased-finetuned-squad ======================================= This model is a fine-tuned version of distilbert-base-uncased 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: 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: 1 ### 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: 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: 1", "### 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 #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #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: 1", "### 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" ]
[ 65, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #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: 1### 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" ]
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# minicpmmerge minicpmmerge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [openbmb/MiniCPM-2B-dpo-bf16-llama-format](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16-llama-format) ## 🧩 Configuration ```yaml models: - model: openbmb/MiniCPM-2B-sft-bf16-llama-format # No parameters necessary for base model - model: openbmb/MiniCPM-2B-dpo-bf16-llama-format parameters: density: 0.53 weight: 0.4 merge_method: dare_ties base_model: openbmb/MiniCPM-2B-sft-bf16-llama-format parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "babybirdprd/minicpmmerge" 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", "openbmb/MiniCPM-2B-dpo-bf16-llama-format"], "base_model": ["openbmb/MiniCPM-2B-dpo-bf16-llama-format"]}
null
babybirdprd/minicpmmerge
[ "merge", "mergekit", "lazymergekit", "openbmb/MiniCPM-2B-dpo-bf16-llama-format", "base_model:openbmb/MiniCPM-2B-dpo-bf16-llama-format", "region:us" ]
2024-02-13T07:14:45+00:00
[]
[]
TAGS #merge #mergekit #lazymergekit #openbmb/MiniCPM-2B-dpo-bf16-llama-format #base_model-openbmb/MiniCPM-2B-dpo-bf16-llama-format #region-us
# minicpmmerge minicpmmerge is a merge of the following models using LazyMergekit: * openbmb/MiniCPM-2B-dpo-bf16-llama-format ## Configuration ## Usage
[ "# minicpmmerge\n\nminicpmmerge is a merge of the following models using LazyMergekit:\n* openbmb/MiniCPM-2B-dpo-bf16-llama-format", "## Configuration", "## Usage" ]
[ "TAGS\n#merge #mergekit #lazymergekit #openbmb/MiniCPM-2B-dpo-bf16-llama-format #base_model-openbmb/MiniCPM-2B-dpo-bf16-llama-format #region-us \n", "# minicpmmerge\n\nminicpmmerge is a merge of the following models using LazyMergekit:\n* openbmb/MiniCPM-2B-dpo-bf16-llama-format", "## Configuration", "## Usage" ]
[ 65, 44, 4, 3 ]
[ "passage: TAGS\n#merge #mergekit #lazymergekit #openbmb/MiniCPM-2B-dpo-bf16-llama-format #base_model-openbmb/MiniCPM-2B-dpo-bf16-llama-format #region-us \n# minicpmmerge\n\nminicpmmerge is a merge of the following models using LazyMergekit:\n* openbmb/MiniCPM-2B-dpo-bf16-llama-format## Configuration## Usage" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
nchen909/mistral_7b_v1_sft_52580
[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T07:16:28+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mistral #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 #mistral #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" ]
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[ "passage: TAGS\n#transformers #safetensors #mistral #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" ]
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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 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 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 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 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 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0
{"library_name": "peft"}
null
Synechron/phi2_bancking_techsupport_followup
[ "peft", "safetensors", "region:us" ]
2024-02-13T07:21:33+00:00
[]
[]
TAGS #peft #safetensors #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 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 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 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 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 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - 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\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\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\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\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- PEFT 0.4.0\n- PEFT 0.4.0\n- PEFT 0.4.0\n- PEFT 0.4.0\n\n- PEFT 0.4.0" ]
[ "TAGS\n#peft #safetensors #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\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\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\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\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- PEFT 0.4.0\n- PEFT 0.4.0\n- PEFT 0.4.0\n- PEFT 0.4.0\n\n- PEFT 0.4.0" ]
[ 14, 808, 35 ]
[ "passage: TAGS\n#peft #safetensors #region-us \n" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
null
wisdominanutshell/splitter_70b_adapter
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T07:22:17+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" ]
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null
null
transformers
# Uploaded model - **Developed by:** wisdominanutshell - **License:** apache-2.0 - **Finetuned from model :** codellama/CodeLlama-70b-Instruct-hf This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "codellama/CodeLlama-70b-Instruct-hf"}
null
wisdominanutshell/splitter_70b
[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:codellama/CodeLlama-70b-Instruct-hf", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-13T07:22:21+00:00
[]
[ "en" ]
TAGS #transformers #text-generation-inference #unsloth #llama #trl #en #base_model-codellama/CodeLlama-70b-Instruct-hf #license-apache-2.0 #endpoints_compatible #region-us
# Uploaded model - Developed by: wisdominanutshell - License: apache-2.0 - Finetuned from model : codellama/CodeLlama-70b-Instruct-hf This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>
[ "# Uploaded model\n\n- Developed by: wisdominanutshell\n- License: apache-2.0\n- Finetuned from model : codellama/CodeLlama-70b-Instruct-hf\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]
[ "TAGS\n#transformers #text-generation-inference #unsloth #llama #trl #en #base_model-codellama/CodeLlama-70b-Instruct-hf #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: wisdominanutshell\n- License: apache-2.0\n- Finetuned from model : codellama/CodeLlama-70b-Instruct-hf\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]
[ 66, 81 ]
[ "passage: TAGS\n#transformers #text-generation-inference #unsloth #llama #trl #en #base_model-codellama/CodeLlama-70b-Instruct-hf #license-apache-2.0 #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: wisdominanutshell\n- License: apache-2.0\n- Finetuned from model : codellama/CodeLlama-70b-Instruct-hf\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]
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null
null
transformers
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/Gs09MsXrzyJdDx7IrnkDa.jpeg) Quants Thanks to @Nold and @Bartowski: https://huggingface.co/nold/Prima-Pastacles-7b-GGUF https://huggingface.co/bartowski/Prima-Pastacles-7b-exl2 ### Models Merged The following models were included in the merge: * [Locutusque/Hercules-2.5-Mistral-7B](https://huggingface.co/Locutusque/Hercules-2.5-Mistral-7B) * [Test157t/Pasta-PrimaMaid-7b](https://huggingface.co/Test157t/Pasta-PrimaMaid-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Test157t/Pasta-PrimaMaid-7b layer_range: [0, 32] - model: Locutusque/Hercules-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: Test157t/Pasta-PrimaMaid-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 ``` *** Quantization of Model [Test157t/Prima-Pastacles-7b](https://huggingface.co/Test157t/Prima-Pastacles-7b). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline
{"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Locutusque/Hercules-2.5-Mistral-7B", "Test157t/Pasta-PrimaMaid-7b"]}
null
nold/Prima-Pastacles-7b-GGUF
[ "transformers", "gguf", "mergekit", "merge", "base_model:Locutusque/Hercules-2.5-Mistral-7B", "base_model:Test157t/Pasta-PrimaMaid-7b", "license:other", "endpoints_compatible", "region:us" ]
2024-02-13T07:23:16+00:00
[]
[]
TAGS #transformers #gguf #mergekit #merge #base_model-Locutusque/Hercules-2.5-Mistral-7B #base_model-Test157t/Pasta-PrimaMaid-7b #license-other #endpoints_compatible #region-us
!image/jpeg Quants Thanks to @Nold and @Bartowski: URL URL ### Models Merged The following models were included in the merge: * Locutusque/Hercules-2.5-Mistral-7B * Test157t/Pasta-PrimaMaid-7b ### Configuration The following YAML configuration was used to produce this model: * Quantization of Model Test157t/Prima-Pastacles-7b. Created using llm-quantizer Pipeline
[ "### Models Merged\n\nThe following models were included in the merge:\n* Locutusque/Hercules-2.5-Mistral-7B\n* Test157t/Pasta-PrimaMaid-7b", "### Configuration\n\nThe following YAML configuration was used to produce this model:\n\n\n\n*\n\nQuantization of Model Test157t/Prima-Pastacles-7b.\nCreated using llm-quantizer Pipeline" ]
[ "TAGS\n#transformers #gguf #mergekit #merge #base_model-Locutusque/Hercules-2.5-Mistral-7B #base_model-Test157t/Pasta-PrimaMaid-7b #license-other #endpoints_compatible #region-us \n", "### Models Merged\n\nThe following models were included in the merge:\n* Locutusque/Hercules-2.5-Mistral-7B\n* Test157t/Pasta-PrimaMaid-7b", "### Configuration\n\nThe following YAML configuration was used to produce this model:\n\n\n\n*\n\nQuantization of Model Test157t/Prima-Pastacles-7b.\nCreated using llm-quantizer Pipeline" ]
[ 69, 43, 45 ]
[ "passage: TAGS\n#transformers #gguf #mergekit #merge #base_model-Locutusque/Hercules-2.5-Mistral-7B #base_model-Test157t/Pasta-PrimaMaid-7b #license-other #endpoints_compatible #region-us \n### Models Merged\n\nThe following models were included in the merge:\n* Locutusque/Hercules-2.5-Mistral-7B\n* Test157t/Pasta-PrimaMaid-7b### Configuration\n\nThe following YAML configuration was used to produce this model:\n\n\n\n*\n\nQuantization of Model Test157t/Prima-Pastacles-7b.\nCreated using llm-quantizer Pipeline" ]
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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. --> # clinical-ner This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8058 - Precision: 0.5786 - Recall: 0.6683 - F1: 0.6202 - Accuracy: 0.8099 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 45 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 5 | 4.7713 | 0.0002 | 0.001 | 0.0004 | 0.0182 | | No log | 2.0 | 10 | 4.2264 | 0.0002 | 0.0008 | 0.0003 | 0.1481 | | No log | 3.0 | 15 | 3.6238 | 0.0004 | 0.0003 | 0.0003 | 0.4575 | | 4.2324 | 4.0 | 20 | 2.8751 | 0.0 | 0.0 | 0.0 | 0.4734 | | 4.2324 | 5.0 | 25 | 2.4550 | 0.0306 | 0.0008 | 0.0015 | 0.4739 | | 4.2324 | 6.0 | 30 | 2.1920 | 0.0722 | 0.0437 | 0.0545 | 0.5007 | | 4.2324 | 7.0 | 35 | 1.9841 | 0.1137 | 0.1087 | 0.1112 | 0.5392 | | 2.3521 | 8.0 | 40 | 1.8153 | 0.1956 | 0.189 | 0.1922 | 0.5829 | | 2.3521 | 9.0 | 45 | 1.6504 | 0.2539 | 0.2617 | 0.2578 | 0.6218 | | 2.3521 | 10.0 | 50 | 1.4801 | 0.3607 | 0.3787 | 0.3695 | 0.6782 | | 2.3521 | 11.0 | 55 | 1.3417 | 0.3933 | 0.433 | 0.4122 | 0.7021 | | 1.6185 | 12.0 | 60 | 1.2333 | 0.4054 | 0.4795 | 0.4394 | 0.7203 | | 1.6185 | 13.0 | 65 | 1.1490 | 0.4307 | 0.5125 | 0.4680 | 0.7347 | | 1.6185 | 14.0 | 70 | 1.0750 | 0.4412 | 0.543 | 0.4868 | 0.7503 | | 1.6185 | 15.0 | 75 | 1.0179 | 0.4816 | 0.5637 | 0.5195 | 0.7619 | | 1.1438 | 16.0 | 80 | 0.9774 | 0.4899 | 0.578 | 0.5303 | 0.7689 | | 1.1438 | 17.0 | 85 | 0.9475 | 0.5005 | 0.5955 | 0.5439 | 0.7743 | | 1.1438 | 18.0 | 90 | 0.9192 | 0.5082 | 0.6078 | 0.5535 | 0.7788 | | 1.1438 | 19.0 | 95 | 0.8923 | 0.5151 | 0.6085 | 0.5579 | 0.7828 | | 0.8863 | 20.0 | 100 | 0.8691 | 0.5263 | 0.6242 | 0.5711 | 0.7882 | | 0.8863 | 21.0 | 105 | 0.8604 | 0.5358 | 0.6342 | 0.5809 | 0.7907 | | 0.8863 | 22.0 | 110 | 0.8474 | 0.5429 | 0.641 | 0.5879 | 0.7946 | | 0.8863 | 23.0 | 115 | 0.8362 | 0.5493 | 0.644 | 0.5929 | 0.7969 | | 0.7361 | 24.0 | 120 | 0.8284 | 0.5531 | 0.6512 | 0.5982 | 0.7994 | | 0.7361 | 25.0 | 125 | 0.8325 | 0.5555 | 0.6565 | 0.6018 | 0.8001 | | 0.7361 | 26.0 | 130 | 0.8156 | 0.5686 | 0.6562 | 0.6093 | 0.8035 | | 0.7361 | 27.0 | 135 | 0.8177 | 0.5634 | 0.6625 | 0.6089 | 0.8039 | | 0.6449 | 28.0 | 140 | 0.8152 | 0.5643 | 0.6567 | 0.6070 | 0.8036 | | 0.6449 | 29.0 | 145 | 0.8109 | 0.5700 | 0.6647 | 0.6137 | 0.8066 | | 0.6449 | 30.0 | 150 | 0.8164 | 0.5697 | 0.6653 | 0.6138 | 0.8055 | | 0.6449 | 31.0 | 155 | 0.8081 | 0.5742 | 0.6627 | 0.6153 | 0.8085 | | 0.5912 | 32.0 | 160 | 0.8130 | 0.5687 | 0.6677 | 0.6142 | 0.8067 | | 0.5912 | 33.0 | 165 | 0.8048 | 0.5779 | 0.6637 | 0.6179 | 0.8089 | | 0.5912 | 34.0 | 170 | 0.8096 | 0.5760 | 0.669 | 0.6190 | 0.8085 | | 0.5912 | 35.0 | 175 | 0.8063 | 0.5790 | 0.6677 | 0.6202 | 0.8091 | | 0.5625 | 36.0 | 180 | 0.8052 | 0.5755 | 0.6673 | 0.6180 | 0.8094 | | 0.5625 | 37.0 | 185 | 0.8063 | 0.5753 | 0.6667 | 0.6176 | 0.8093 | | 0.5625 | 38.0 | 190 | 0.8055 | 0.5783 | 0.6677 | 0.6198 | 0.8103 | | 0.5625 | 39.0 | 195 | 0.8052 | 0.5792 | 0.668 | 0.6205 | 0.8099 | | 0.5442 | 40.0 | 200 | 0.8052 | 0.5798 | 0.6685 | 0.6210 | 0.8097 | | 0.5442 | 41.0 | 205 | 0.8055 | 0.5784 | 0.6683 | 0.6201 | 0.8098 | | 0.5442 | 42.0 | 210 | 0.8056 | 0.5789 | 0.6685 | 0.6205 | 0.8100 | | 0.5442 | 43.0 | 215 | 0.8057 | 0.5786 | 0.6683 | 0.6202 | 0.8100 | | 0.5397 | 44.0 | 220 | 0.8057 | 0.5786 | 0.6683 | 0.6202 | 0.8099 | | 0.5397 | 45.0 | 225 | 0.8058 | 0.5786 | 0.6683 | 0.6202 | 0.8099 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/deberta-v3-base", "model-index": [{"name": "clinical-ner", "results": []}]}
token-classification
blaze999/clinical-ner
[ "transformers", "tensorboard", "safetensors", "deberta-v2", "token-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T07:28:38+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
clinical-ner ============ This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.8058 * Precision: 0.5786 * Recall: 0.6683 * F1: 0.6202 * Accuracy: 0.8099 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 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 45 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.1.0 * 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* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 45\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 45\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
[ 73, 160, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 45\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
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<img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v0.1/resolve/main/llama-tr-image.jpeg" alt="drawing" width="400"/> # **Trendyol LLM GGUF Version** Trendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the quantized chat model. **Developer** Umar Igan GGUF Version Created using following notebook: https://github.com/mlabonne/llm-course/blob/main/Quantize_Llama_2_models_using_GGUF_and_llama_cpp.ipynb **Variations** Q5_K_M and Q4_K_M variations of GGUF. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The chat version is fine-tuned on 180K instruction sets with the following trainables by using LoRA This is a quantized model of Trendyol LLM: <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_diagram.png" alt="drawing" width="600"/> ## Usage ```python from llama_cpp import Llama from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm_p = AutoModelForCausalLM.from_pretrained("umarigan/Trendyol-LLM-7b-chat-v0.1-GGUF", model_file="trendyol-llm-7b-chat-v0.1.Q4_K_M.gguf", model_type="llama", gpu_layers=0) # Chat Completion API llm = Llama(model_path=llm_p.model_path, chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "çocuk hikayeleri yazan bir yazarsın"}, { "role": "user", "content": "köpekler hakkında bir çocuk hikayesi yaz" } ] ) ``` Output: ``` {'id': 'chatcmpl-0d665fb2-a92a-408c-bc03-78c32bccab0d', 'object': 'chat.completion', 'created': 1707822047, 'model': '/root/.cache/huggingface/hub/models--umarigan--Trendyol-LLM-7b-chat-v0.1-GGUF/blobs/323878a8570093178040e78b438d5670c0fdae2aa614a8ed58e784d697d4db52', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': ' Bir zamanlar, ormanda yaşayan cesur ve sadık bir köpek varmış. O, her zaman arkadaşlarına yardım etmeye hazırdı ve asla korkmuyordu. Bir gün, ormanın derinliklerinde gizemli bir ses duydu ve araştırmaya karar verdi. Yol boyunca birçok yaratıkla karşılaştı ama hiçbirinin kimliğini bilmiyordu. Sonunda, gizemli sesin geldiği yere ulaştı ve sonunda onu buldu.'}, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 39, 'completion_tokens': 85, 'total_tokens': 124}} ```
{"language": ["tr", "en"], "license": "apache-2.0", "pipeline_tag": "text-generation"}
text-generation
umarigan/Trendyol-LLM-7b-chat-v0.1-GGUF
[ "gguf", "text-generation", "tr", "en", "license:apache-2.0", "region:us" ]
2024-02-13T07:30:35+00:00
[]
[ "tr", "en" ]
TAGS #gguf #text-generation #tr #en #license-apache-2.0 #region-us
<img src="URL alt="drawing" width="400"/> # Trendyol LLM GGUF Version Trendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the quantized chat model. Developer Umar Igan GGUF Version Created using following notebook: URL Variations Q5_K_M and Q4_K_M variations of GGUF. Input Models input text only. Output Models generate text only. Model Architecture Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The chat version is fine-tuned on 180K instruction sets with the following trainables by using LoRA This is a quantized model of Trendyol LLM: <img src="URL alt="drawing" width="600"/> ## Usage Output:
[ "# Trendyol LLM GGUF Version\nTrendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the quantized chat model.\n\nDeveloper Umar Igan\nGGUF Version Created using following notebook: URL\n\nVariations Q5_K_M and Q4_K_M variations of GGUF.\n\n\nInput Models input text only.\n\nOutput Models generate text only.\n\nModel Architecture Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The chat version is fine-tuned on 180K instruction sets with the following trainables by using LoRA\nThis is a quantized model of Trendyol LLM:\n\n\n<img src=\"URL\nalt=\"drawing\" width=\"600\"/>", "## Usage\n\n\n\nOutput:" ]
[ "TAGS\n#gguf #text-generation #tr #en #license-apache-2.0 #region-us \n", "# Trendyol LLM GGUF Version\nTrendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the quantized chat model.\n\nDeveloper Umar Igan\nGGUF Version Created using following notebook: URL\n\nVariations Q5_K_M and Q4_K_M variations of GGUF.\n\n\nInput Models input text only.\n\nOutput Models generate text only.\n\nModel Architecture Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The chat version is fine-tuned on 180K instruction sets with the following trainables by using LoRA\nThis is a quantized model of Trendyol LLM:\n\n\n<img src=\"URL\nalt=\"drawing\" width=\"600\"/>", "## Usage\n\n\n\nOutput:" ]
[ 26, 188, 6 ]
[ "passage: TAGS\n#gguf #text-generation #tr #en #license-apache-2.0 #region-us \n# Trendyol LLM GGUF Version\nTrendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the quantized chat model.\n\nDeveloper Umar Igan\nGGUF Version Created using following notebook: URL\n\nVariations Q5_K_M and Q4_K_M variations of GGUF.\n\n\nInput Models input text only.\n\nOutput Models generate text only.\n\nModel Architecture Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The chat version is fine-tuned on 180K instruction sets with the following trainables by using LoRA\nThis is a quantized model of Trendyol LLM:\n\n\n<img src=\"URL\nalt=\"drawing\" width=\"600\"/>## Usage\n\n\n\nOutput:" ]
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null
null
diffusers
# 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 🧨 diffusers 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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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": "diffusers"}
null
sayakpaul/tiny-sd-pipeline-for-single-file-testing
[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-13T07:31:50+00:00
[ "1910.09700" ]
[]
TAGS #diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
# Model Card for Model ID ## Model Details ### Model Description This is the model card of a diffusers 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 diffusers 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#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a diffusers 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" ]
[ 45, 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#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a diffusers 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" ]
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null
null
stable-baselines3
# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "257.34 +/- 20.83", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
CocosNucifera/ppo-LunarLander-v2
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-13T07:32:22+00:00
[]
[]
TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 39, 41, 17 ]
[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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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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{"library_name": "transformers", "tags": []}
null
kenchenxingyu/flan-large-lora-stance-human4
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T07:34:27+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" ]
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[ "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" ]
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null
null
stable-baselines3
# **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
{"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.23 +/- 0.07", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
lambdavi/a2c-PandaReachDense-v3
[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-13T07:36:07+00:00
[]
[]
TAGS #stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 41, 45, 17 ]
[ "passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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# yuj-v1-GGUF - Model creator: [shuvom_](https://huggingface.co/shuvom) - Original model: [shuvom/yuj-v1](https://huggingface.co/shuvom/yuj-v1) <!-- description start --> ## Description This repo contains GGUF format model files for [shuvom/yuj-v1](https://huggingface.co/shuvom/yuj-v1). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF and GGML are file formats used for storing models for inference, especially in the context of language models like GPT (Generative Pre-trained Transformer). It allows you to inference in consumer-grade GPUs and CPUs. [more info.](https://github.com/ggerganov/llama.cpp) ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [yuj-v1.Q4_K_M.gguf](https://huggingface.co/shuvom/yuj-v1-GGUF/blob/main/yuj-v1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.17 GB| 6.87 GB | medium, balanced quality - recommended | ## Usage 1. Installing lamma.cpp python client and HuggingFace-hub ```python !pip install llama-cpp-python huggingface-hub ``` 2. Downloading GGUF formatted model ```python !huggingface-cli download shuvom/yuj-v1-GGUF yuj-v1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` 3. Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. ```python from llama_cpp import Llama llm = Llama( model_path="./yuj-v1.Q4_K_M.gguf", # Download the model file first n_ctx=2048, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) ``` 4. Chat Completion API ```python llm = Llama(model_path="/content/yuj-v1.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "युज शीर्ष द्विभाषी मॉडल में से एक है" } ] ) ```
{"language": ["hi"], "license": "apache-2.0", "tags": ["hindi", "quantization", "shuvom/yuj-v1"], "pipeline_tag": "text-generation", "quantized_by": "shuvom"}
text-generation
shuvom/yuj-v1-GGUF
[ "gguf", "hindi", "quantization", "shuvom/yuj-v1", "text-generation", "hi", "license:apache-2.0", "region:us" ]
2024-02-13T07:38:22+00:00
[]
[ "hi" ]
TAGS #gguf #hindi #quantization #shuvom/yuj-v1 #text-generation #hi #license-apache-2.0 #region-us
yuj-v1-GGUF =========== * Model creator: shuvom\_ * Original model: shuvom/yuj-v1 Description ----------- This repo contains GGUF format model files for shuvom/yuj-v1. ### About GGUF GGUF and GGML are file formats used for storing models for inference, especially in the context of language models like GPT (Generative Pre-trained Transformer). It allows you to inference in consumer-grade GPUs and CPUs. more info. Provided files -------------- Usage ----- 1. Installing URL python client and HuggingFace-hub 2. Downloading GGUF formatted model 3. Set gpu\_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. 4. Chat Completion API
[ "### About GGUF\n\n\nGGUF and GGML are file formats used for storing models for inference, especially in the context of language models like GPT (Generative Pre-trained Transformer). It allows you to inference in consumer-grade GPUs and CPUs.\n\n\nmore info.\n\n\nProvided files\n--------------\n\n\n\nUsage\n-----\n\n\n1. Installing URL python client and HuggingFace-hub\n2. Downloading GGUF formatted model\n3. Set gpu\\_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.\n4. Chat Completion API" ]
[ "TAGS\n#gguf #hindi #quantization #shuvom/yuj-v1 #text-generation #hi #license-apache-2.0 #region-us \n", "### About GGUF\n\n\nGGUF and GGML are file formats used for storing models for inference, especially in the context of language models like GPT (Generative Pre-trained Transformer). It allows you to inference in consumer-grade GPUs and CPUs.\n\n\nmore info.\n\n\nProvided files\n--------------\n\n\n\nUsage\n-----\n\n\n1. Installing URL python client and HuggingFace-hub\n2. Downloading GGUF formatted model\n3. Set gpu\\_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.\n4. Chat Completion API" ]
[ 38, 138 ]
[ "passage: TAGS\n#gguf #hindi #quantization #shuvom/yuj-v1 #text-generation #hi #license-apache-2.0 #region-us \n### About GGUF\n\n\nGGUF and GGML are file formats used for storing models for inference, especially in the context of language models like GPT (Generative Pre-trained Transformer). It allows you to inference in consumer-grade GPUs and CPUs.\n\n\nmore info.\n\n\nProvided files\n--------------\n\n\n\nUsage\n-----\n\n\n1. Installing URL python client and HuggingFace-hub\n2. Downloading GGUF formatted model\n3. Set gpu\\_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.\n4. Chat Completion API" ]
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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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{"library_name": "transformers", "tags": []}
null
kaushalpowar/llama2_finetuned2_easymonk_refined_data
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T07:42:01+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" ]
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[ "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" ]
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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. 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{"library_name": "transformers", "tags": []}
null
kaushalpowar/llama2_finetuned2_easymonk_refined_data_merged
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T07:42:48+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" ]
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[ "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" ]
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# WhisperSpeechRVCPipline **Zero-Shot AI Voice Cloning TTS With WhisperSpeech And RVC Pipeline** <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> [![Test it out yourself in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xxGlTbwBmaY6GKA24strRixTXGBOlyiw) *If you have questions or you want to help you can find us in the \#audio-generation channel on the LAION Discord server.* An Open Source text-to-speech system built by inverting Whisper. Previously known as **spear-tts-pytorch**. We want this model to be like Stable Diffusion but for speech – both powerful and easily customizable. We are working only with properly licensed speech recordings and all the code is Open Source so the model will be always safe to use for commercial applications. Currently the models are trained on the English LibreLight dataset. In the next release we want to target multiple languages (Whisper and EnCodec are both multilanguage). Sample of the synthesized voice: https://github.com/collabora/WhisperSpeech/assets/107984/aa5a1e7e-dc94-481f-8863-b022c7fd7434
{"license": "gpl-3.0"}
null
syedusama5556/WhisperSpeechTTSRVCPipline
[ "license:gpl-3.0", "region:us" ]
2024-02-13T07:47:32+00:00
[]
[]
TAGS #license-gpl-3.0 #region-us
# WhisperSpeechRVCPipline Zero-Shot AI Voice Cloning TTS With WhisperSpeech And RVC Pipeline ![Test it out yourself in Colab](URL *If you have questions or you want to help you can find us in the \#audio-generation channel on the LAION Discord server.* An Open Source text-to-speech system built by inverting Whisper. Previously known as spear-tts-pytorch. We want this model to be like Stable Diffusion but for speech – both powerful and easily customizable. We are working only with properly licensed speech recordings and all the code is Open Source so the model will be always safe to use for commercial applications. Currently the models are trained on the English LibreLight dataset. In the next release we want to target multiple languages (Whisper and EnCodec are both multilanguage). Sample of the synthesized voice: URL
[ "# WhisperSpeechRVCPipline\n\n\nZero-Shot AI Voice Cloning TTS With WhisperSpeech And RVC Pipeline\n\n\n\n\n![Test it out yourself in\nColab](URL\n*If you have questions or you want to help you can find us in the\n\\#audio-generation channel on the LAION Discord server.*\n\nAn Open Source text-to-speech system built by inverting Whisper.\nPreviously known as spear-tts-pytorch.\n\nWe want this model to be like Stable Diffusion but for speech – both\npowerful and easily customizable.\n\nWe are working only with properly licensed speech recordings and all the\ncode is Open Source so the model will be always safe to use for\ncommercial applications.\n\nCurrently the models are trained on the English LibreLight dataset. In\nthe next release we want to target multiple languages (Whisper and\nEnCodec are both multilanguage).\n\nSample of the synthesized voice:\n\nURL" ]
[ "TAGS\n#license-gpl-3.0 #region-us \n", "# WhisperSpeechRVCPipline\n\n\nZero-Shot AI Voice Cloning TTS With WhisperSpeech And RVC Pipeline\n\n\n\n\n![Test it out yourself in\nColab](URL\n*If you have questions or you want to help you can find us in the\n\\#audio-generation channel on the LAION Discord server.*\n\nAn Open Source text-to-speech system built by inverting Whisper.\nPreviously known as spear-tts-pytorch.\n\nWe want this model to be like Stable Diffusion but for speech – both\npowerful and easily customizable.\n\nWe are working only with properly licensed speech recordings and all the\ncode is Open Source so the model will be always safe to use for\ncommercial applications.\n\nCurrently the models are trained on the English LibreLight dataset. In\nthe next release we want to target multiple languages (Whisper and\nEnCodec are both multilanguage).\n\nSample of the synthesized voice:\n\nURL" ]
[ 14, 214 ]
[ "passage: TAGS\n#license-gpl-3.0 #region-us \n# WhisperSpeechRVCPipline\n\n\nZero-Shot AI Voice Cloning TTS With WhisperSpeech And RVC Pipeline\n\n\n\n\n![Test it out yourself in\nColab](URL\n*If you have questions or you want to help you can find us in the\n\\#audio-generation channel on the LAION Discord server.*\n\nAn Open Source text-to-speech system built by inverting Whisper.\nPreviously known as spear-tts-pytorch.\n\nWe want this model to be like Stable Diffusion but for speech – both\npowerful and easily customizable.\n\nWe are working only with properly licensed speech recordings and all the\ncode is Open Source so the model will be always safe to use for\ncommercial applications.\n\nCurrently the models are trained on the English LibreLight dataset. In\nthe next release we want to target multiple languages (Whisper and\nEnCodec are both multilanguage).\n\nSample of the synthesized voice:\n\nURL" ]
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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. --> # wav2vec2-large-robust-finetuned-ie This model is a fine-tuned version of [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1133 - Accuracy: 0.5536 ## 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 - gradient_accumulation_steps: 4 - total_train_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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3699 | 1.0 | 102 | 1.3843 | 0.2502 | | 1.2027 | 2.0 | 204 | 1.1851 | 0.4200 | | 1.047 | 3.0 | 306 | 1.1356 | 0.4520 | | 1.0414 | 4.0 | 408 | 1.1702 | 0.4627 | | 0.9885 | 5.0 | 510 | 1.0295 | 0.5354 | | 0.9873 | 6.0 | 612 | 1.0988 | 0.5228 | | 0.9309 | 7.0 | 714 | 1.1347 | 0.5257 | | 0.8401 | 8.0 | 816 | 1.1502 | 0.5286 | | 0.8253 | 9.0 | 918 | 1.0792 | 0.5577 | | 0.8741 | 10.0 | 1020 | 1.2591 | 0.5267 | | 0.8177 | 11.0 | 1122 | 1.3007 | 0.5141 | | 0.7633 | 12.0 | 1224 | 1.1962 | 0.5509 | | 0.8185 | 13.0 | 1326 | 1.1022 | 0.5984 | | 0.7481 | 14.0 | 1428 | 1.1741 | 0.5694 | | 0.719 | 15.0 | 1530 | 1.1645 | 0.5781 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-large-robust", "model-index": [{"name": "wav2vec2-large-robust-finetuned-ie", "results": []}]}
audio-classification
tarasabkar/wav2vec2-large-robust-finetuned-ie
[ "transformers", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-large-robust", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-13T07:48:51+00:00
[]
[]
TAGS #transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-large-robust #license-apache-2.0 #endpoints_compatible #region-us
wav2vec2-large-robust-finetuned-ie ================================== This model is a fine-tuned version of facebook/wav2vec2-large-robust on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.1133 * Accuracy: 0.5536 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 * gradient\_accumulation\_steps: 4 * total\_train\_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: 15 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_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: 15", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-large-robust #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_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: 15", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ 66, 144, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-large-robust #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_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: 15### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
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null
null
transformers
# haLLAwa3 haLLAwa3 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) * [machinists/Mistral-7B-SQL](https://huggingface.co/machinists/Mistral-7B-SQL) ## 🧩 Configuration \```yaml slices: - sources: - model: openchat/openchat-3.5-0106 layer_range: [0, 32] - model: machinists/Mistral-7B-SQL layer_range: [0, 32] merge_method: slerp base_model: openchat/openchat-3.5-0106 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 \```
{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "openchat/openchat-3.5-0106", "machinists/Mistral-7B-SQL"]}
text-generation
AbacusResearch/haLLAwa3
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "openchat/openchat-3.5-0106", "machinists/Mistral-7B-SQL", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-13T07:49:10+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #openchat/openchat-3.5-0106 #machinists/Mistral-7B-SQL #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# haLLAwa3 haLLAwa3 is a merge of the following models using mergekit: * openchat/openchat-3.5-0106 * machinists/Mistral-7B-SQL ## Configuration \
[ "# haLLAwa3\n\nhaLLAwa3 is a merge of the following models using mergekit:\n* openchat/openchat-3.5-0106\n* machinists/Mistral-7B-SQL", "## Configuration\n\n\\" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #openchat/openchat-3.5-0106 #machinists/Mistral-7B-SQL #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# haLLAwa3\n\nhaLLAwa3 is a merge of the following models using mergekit:\n* openchat/openchat-3.5-0106\n* machinists/Mistral-7B-SQL", "## Configuration\n\n\\" ]
[ 89, 43, 6 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #openchat/openchat-3.5-0106 #machinists/Mistral-7B-SQL #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# haLLAwa3\n\nhaLLAwa3 is a merge of the following models using mergekit:\n* openchat/openchat-3.5-0106\n* machinists/Mistral-7B-SQL## Configuration\n\n\\" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
gmonsoon/Qwen1.5-0.5B-Horchata
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T07:51:20+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #qwen2 #text-generation #conversational #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 #qwen2 #text-generation #conversational #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" ]
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[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #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" ]
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null
null
diffusers
### Alexandra Dreambooth model trained by tomcoiso with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
{"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]}
text-to-image
tomcoiso/alexandra
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-13T08:02:21+00:00
[]
[]
TAGS #diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
### Alexandra Dreambooth model trained by tomcoiso with TheLastBen's fast-DreamBooth notebook Test the concept via A1111 Colab fast-Colab-A1111 Sample pictures of this concept:
[ "### Alexandra Dreambooth model trained by tomcoiso with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]
[ "TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "### Alexandra Dreambooth model trained by tomcoiso with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]
[ 61, 48 ]
[ "passage: TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### Alexandra Dreambooth model trained by tomcoiso with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]
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null
null
spacy
| Feature | Description | | --- | --- | | **Name** | `en_pipeline` | | **Version** | `0.0.0` | | **spaCy** | `>=3.7.3,<3.8.0` | | **Default Pipeline** | `tok2vec`, `ner` | | **Components** | `tok2vec`, `ner` | | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (3 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `MEDICALCONDITION`, `MEDICINE`, `PATHOGEN` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 70.00 | | `ENTS_P` | 82.80 | | `ENTS_R` | 60.63 | | `TOK2VEC_LOSS` | 49925.04 | | `NER_LOSS` | 364010.40 |
{"language": ["en"], "tags": ["spacy", "token-classification"]}
token-classification
VikasCh/en_pipeline
[ "spacy", "token-classification", "en", "model-index", "region:us" ]
2024-02-13T08:07:17+00:00
[]
[ "en" ]
TAGS #spacy #token-classification #en #model-index #region-us
### Label Scheme View label scheme (3 labels for 1 components) ### Accuracy
[ "### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)", "### Accuracy" ]
[ "TAGS\n#spacy #token-classification #en #model-index #region-us \n", "### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)", "### Accuracy" ]
[ 21, 16, 5 ]
[ "passage: TAGS\n#spacy #token-classification #en #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)### Accuracy" ]
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null
null
peft
## Training procedure The following `bitsandbytes` quantization config was used during training: - 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", "tags": ["pytorch", "llama", "llama-2", "text generation"], "pipeline_tag": "text-generation"}
text-generation
Shri2818/llama_trial_python_v2
[ "peft", "pytorch", "llama", "llama-2", "text generation", "text-generation", "region:us" ]
2024-02-13T08:10:40+00:00
[]
[]
TAGS #peft #pytorch #llama #llama-2 #text generation #text-generation #region-us
## Training procedure The following 'bitsandbytes' quantization config was used during training: - 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- 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 #pytorch #llama #llama-2 #text generation #text-generation #region-us \n", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\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" ]
[ 28, 154, 11 ]
[ "passage: TAGS\n#peft #pytorch #llama #llama-2 #text generation #text-generation #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\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" ]
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null
null
adapter-transformers
Model Architecture OOM-7B_02 is an language model that uses an optimized transformer architecture based on Llama-2. ## Model description Based on "beomi/llama-2-ko-7b" ## 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: 2 - eval_batch_size: 8 - seed: 24 - gradient_accumulation_steps: 1 - total_train_batch_size: - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1
{"language": ["en", "ko"], "license": "apache-2.0", "library_name": "adapter-transformers"}
null
giprime/OOM-7B_02
[ "adapter-transformers", "safetensors", "llama", "en", "ko", "license:apache-2.0", "region:us" ]
2024-02-13T08:13:38+00:00
[]
[ "en", "ko" ]
TAGS #adapter-transformers #safetensors #llama #en #ko #license-apache-2.0 #region-us
Model Architecture OOM-7B_02 is an language model that uses an optimized transformer architecture based on Llama-2. ## Model description Based on "beomi/llama-2-ko-7b" ## 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: 2 - eval_batch_size: 8 - seed: 24 - gradient_accumulation_steps: 1 - total_train_batch_size: - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "## Model description\n\nBased on \"beomi/llama-2-ko-7b\"", "## 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: 2\n- eval_batch_size: 8\n- seed: 24\n- gradient_accumulation_steps: 1\n- total_train_batch_size: \n- num_epochs: 2.0", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#adapter-transformers #safetensors #llama #en #ko #license-apache-2.0 #region-us \n", "## Model description\n\nBased on \"beomi/llama-2-ko-7b\"", "## 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: 2\n- eval_batch_size: 8\n- seed: 24\n- gradient_accumulation_steps: 1\n- total_train_batch_size: \n- num_epochs: 2.0", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 31, 18, 12, 8, 3, 80, 4, 33 ]
[ "passage: TAGS\n#adapter-transformers #safetensors #llama #en #ko #license-apache-2.0 #region-us \n## Model description\n\nBased on \"beomi/llama-2-ko-7b\"## 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: 2\n- eval_batch_size: 8\n- seed: 24\n- gradient_accumulation_steps: 1\n- total_train_batch_size: \n- num_epochs: 2.0### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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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. --> # wav2vec2-base-finetuned-daps This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0242 - eval_accuracy: 1.0 - eval_runtime: 39.2251 - eval_samples_per_second: 0.51 - eval_steps_per_second: 0.51 - epoch: 1.51 - step: 68 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### 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": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-finetuned-daps", "results": []}]}
audio-classification
hzx405416956/wav2vec2-base-finetuned-daps
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-13T08:14:12+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us
# wav2vec2-base-finetuned-daps This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0242 - eval_accuracy: 1.0 - eval_runtime: 39.2251 - eval_samples_per_second: 0.51 - eval_steps_per_second: 0.51 - epoch: 1.51 - step: 68 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
[ "# wav2vec2-base-finetuned-daps\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0242\n- eval_accuracy: 1.0\n- eval_runtime: 39.2251\n- eval_samples_per_second: 0.51\n- eval_steps_per_second: 0.51\n- epoch: 1.51\n- step: 68", "## 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: 1\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_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_ratio: 0.1\n- num_epochs: 5", "### 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 #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us \n", "# wav2vec2-base-finetuned-daps\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0242\n- eval_accuracy: 1.0\n- eval_runtime: 39.2251\n- eval_samples_per_second: 0.51\n- eval_steps_per_second: 0.51\n- epoch: 1.51\n- step: 68", "## 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: 1\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_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_ratio: 0.1\n- num_epochs: 5", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
[ 66, 118, 6, 12, 8, 3, 128, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-base-finetuned-daps\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0242\n- eval_accuracy: 1.0\n- eval_runtime: 39.2251\n- eval_samples_per_second: 0.51\n- eval_steps_per_second: 0.51\n- epoch: 1.51\n- step: 68## 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: 1\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_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_ratio: 0.1\n- num_epochs: 5### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
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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. 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{"library_name": "transformers", "tags": []}
null
ankitkh108/mistral_7b_acqku
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T08:15:32+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" ]
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[ "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" ]
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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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{"library_name": "transformers", "tags": []}
text-generation
Basha738/llama2-13B-supervised-ft-7-epochs-351
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-13T08:19:20+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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 #4-bit #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" ]
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[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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" ]
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# **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "64.10 +/- 37.78", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
lambdavi/Reinforce-Pixelcopter-PLE-v0
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
2024-02-13T08:20:35+00:00
[]
[]
TAGS #Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ 41, 58 ]
[ "passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
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null
null
stable-baselines3
# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "264.03 +/- 21.11", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
Khemmanat/ppo-LunarLander-v2
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-13T08:25:21+00:00
[]
[]
TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 39, 41, 17 ]
[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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null
null
null
/etc/motd
{}
null
graniet75/test123
[ "pytorch", "region:us" ]
2024-02-13T08:26:58+00:00
[]
[]
TAGS #pytorch #region-us
/etc/motd
[]
[ "TAGS\n#pytorch #region-us \n" ]
[ 10 ]
[ "passage: TAGS\n#pytorch #region-us \n" ]
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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. --> # roberta-base-finetuned-nonbinary This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1993 - Accuracy: 0.7519 ## 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: 8 - eval_batch_size: 8 - seed: 100 - 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 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6622 | 0.01 | 100 | 1.4169 | 0.7197 | | 1.5161 | 0.02 | 200 | 1.3703 | 0.7261 | | 1.4657 | 0.04 | 300 | 1.3343 | 0.7312 | | 1.4474 | 0.05 | 400 | 1.3117 | 0.7353 | | 1.4191 | 0.06 | 500 | 1.3004 | 0.7368 | | 1.4004 | 0.07 | 600 | 1.2847 | 0.7389 | | 1.4071 | 0.08 | 700 | 1.2745 | 0.7404 | | 1.3942 | 0.09 | 800 | 1.2617 | 0.7423 | | 1.3692 | 0.11 | 900 | 1.2576 | 0.7432 | | 1.3766 | 0.12 | 1000 | 1.2505 | 0.7444 | | 1.3664 | 0.13 | 1100 | 1.2429 | 0.7453 | | 1.3604 | 0.14 | 1200 | 1.2336 | 0.7467 | | 1.3527 | 0.15 | 1300 | 1.2336 | 0.7468 | | 1.3376 | 0.17 | 1400 | 1.2217 | 0.7484 | | 1.3319 | 0.18 | 1500 | 1.2195 | 0.7486 | | 1.3111 | 0.19 | 1600 | 1.2154 | 0.7498 | | 1.3051 | 0.2 | 1700 | 1.2116 | 0.7498 | | 1.3125 | 0.21 | 1800 | 1.2073 | 0.7505 | | 1.313 | 0.22 | 1900 | 1.2062 | 0.7514 | | 1.3277 | 0.24 | 2000 | 1.1988 | 0.7516 | | 1.2987 | 0.25 | 2100 | 1.1988 | 0.7519 | | 1.2983 | 0.26 | 2200 | 1.2006 | 0.7517 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.13.3
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-finetuned-nonbinary", "results": []}]}
fill-mask
tejaskamtam/roberta-base-finetuned-nonbinary
[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T08:30:14+00:00
[]
[]
TAGS #transformers #pytorch #roberta #fill-mask #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
roberta-base-finetuned-nonbinary ================================ This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.1993 * Accuracy: 0.7519 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: 8 * eval\_batch\_size: 8 * seed: 100 * 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 * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.31.0 * Pytorch 2.2.0+cu121 * Datasets 2.17.0 * Tokenizers 0.13.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 100\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* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]
[ "TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #base_model-roberta-base #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: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 100\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* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]
[ 58, 126, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #base_model-roberta-base #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: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 100\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* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]
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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. --> # bert-base-finetuned-nonbinary This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3200 - Accuracy: 0.7265 ## 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: 8 - eval_batch_size: 8 - seed: 100 - 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 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.8617 | 0.01 | 100 | 1.5602 | 0.6928 | | 1.6613 | 0.03 | 200 | 1.4890 | 0.7026 | | 1.6102 | 0.04 | 300 | 1.4542 | 0.7072 | | 1.5557 | 0.05 | 400 | 1.4251 | 0.7117 | | 1.5618 | 0.06 | 500 | 1.4127 | 0.7134 | | 1.5449 | 0.08 | 600 | 1.3975 | 0.7153 | | 1.5489 | 0.09 | 700 | 1.3878 | 0.7170 | | 1.5013 | 0.1 | 800 | 1.3810 | 0.7178 | | 1.5142 | 0.12 | 900 | 1.3705 | 0.7196 | | 1.4859 | 0.13 | 1000 | 1.3654 | 0.7204 | | 1.5054 | 0.14 | 1100 | 1.3532 | 0.7214 | | 1.5024 | 0.15 | 1200 | 1.3512 | 0.7222 | | 1.4838 | 0.17 | 1300 | 1.3462 | 0.7227 | | 1.4761 | 0.18 | 1400 | 1.3387 | 0.7239 | | 1.4648 | 0.19 | 1500 | 1.3401 | 0.7237 | | 1.4537 | 0.21 | 1600 | 1.3343 | 0.7245 | | 1.4558 | 0.22 | 1700 | 1.3258 | 0.7256 | | 1.4487 | 0.23 | 1800 | 1.3247 | 0.7258 | | 1.4347 | 0.25 | 1900 | 1.3209 | 0.7267 | | 1.4313 | 0.26 | 2000 | 1.3126 | 0.7276 | | 1.4415 | 0.27 | 2100 | 1.3175 | 0.7266 | | 1.4303 | 0.28 | 2200 | 1.3177 | 0.7270 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.13.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-finetuned-nonbinary", "results": []}]}
fill-mask
tejaskamtam/bert-base-finetuned-nonbinary
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T08:30:17+00:00
[]
[]
TAGS #transformers #pytorch #bert #fill-mask #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
bert-base-finetuned-nonbinary ============================= This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.3200 * Accuracy: 0.7265 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: 8 * eval\_batch\_size: 8 * seed: 100 * 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 * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.31.0 * Pytorch 2.2.0+cu121 * Datasets 2.17.0 * Tokenizers 0.13.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 100\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* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]
[ "TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #base_model-bert-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: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 100\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* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]
[ 63, 126, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #base_model-bert-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: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 100\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* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]
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null
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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. --> # deberta-base-finetuned-nonbinary This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3484 - Accuracy: 0.7375 ## 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: 8 - eval_batch_size: 8 - seed: 100 - 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 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 7.1639 | 0.01 | 100 | 5.1537 | 0.2726 | | 4.7678 | 0.02 | 200 | 4.2436 | 0.3611 | | 4.1542 | 0.04 | 300 | 3.7749 | 0.4155 | | 3.7847 | 0.05 | 400 | 3.4273 | 0.4557 | | 3.4833 | 0.06 | 500 | 3.1692 | 0.4882 | | 3.2475 | 0.07 | 600 | 2.9471 | 0.5192 | | 3.0871 | 0.08 | 700 | 2.7649 | 0.5466 | | 2.914 | 0.09 | 800 | 2.6146 | 0.5694 | | 2.7638 | 0.11 | 900 | 2.5017 | 0.5878 | | 2.6771 | 0.12 | 1000 | 2.4101 | 0.6019 | | 2.5762 | 0.13 | 1100 | 2.3252 | 0.6146 | | 2.4962 | 0.14 | 1200 | 2.2549 | 0.6236 | | 2.4253 | 0.15 | 1300 | 2.1949 | 0.6317 | | 2.3622 | 0.17 | 1400 | 2.1333 | 0.6407 | | 2.309 | 0.18 | 1500 | 2.0889 | 0.6462 | | 2.2373 | 0.19 | 1600 | 2.0524 | 0.6505 | | 2.2021 | 0.2 | 1700 | 2.0093 | 0.6565 | | 2.1735 | 0.21 | 1800 | 1.9733 | 0.6616 | | 2.1439 | 0.22 | 1900 | 1.9431 | 0.6649 | | 2.125 | 0.24 | 2000 | 1.9164 | 0.6675 | | 2.0649 | 0.25 | 2100 | 1.8905 | 0.6717 | | 2.0467 | 0.26 | 2200 | 1.8738 | 0.6729 | | 2.0314 | 0.27 | 2300 | 1.8472 | 0.6766 | | 1.9951 | 0.28 | 2400 | 1.8356 | 0.6779 | | 1.9951 | 0.3 | 2500 | 1.8069 | 0.6815 | | 1.9539 | 0.31 | 2600 | 1.7934 | 0.6827 | | 1.951 | 0.32 | 2700 | 1.7798 | 0.6839 | | 1.932 | 0.33 | 2800 | 1.7606 | 0.6867 | | 1.9057 | 0.34 | 2900 | 1.7503 | 0.6877 | | 1.8907 | 0.36 | 3000 | 1.7347 | 0.6901 | | 1.9008 | 0.37 | 3100 | 1.7272 | 0.6900 | | 1.8745 | 0.38 | 3200 | 1.7097 | 0.6929 | | 1.8657 | 0.39 | 3300 | 1.7018 | 0.6939 | | 1.8598 | 0.4 | 3400 | 1.6901 | 0.6959 | | 1.8371 | 0.41 | 3500 | 1.6785 | 0.6962 | | 1.8164 | 0.43 | 3600 | 1.6744 | 0.6968 | | 1.8175 | 0.44 | 3700 | 1.6605 | 0.6986 | | 1.8211 | 0.45 | 3800 | 1.6501 | 0.6999 | | 1.8182 | 0.46 | 3900 | 1.6430 | 0.7006 | | 1.7956 | 0.47 | 4000 | 1.6401 | 0.7005 | | 1.7812 | 0.49 | 4100 | 1.6243 | 0.7026 | | 1.7803 | 0.5 | 4200 | 1.6170 | 0.7043 | | 1.7736 | 0.51 | 4300 | 1.6120 | 0.7039 | | 1.769 | 0.52 | 4400 | 1.6056 | 0.7055 | | 1.7436 | 0.53 | 4500 | 1.5983 | 0.7061 | | 1.7417 | 0.54 | 4600 | 1.5935 | 0.7065 | | 1.7373 | 0.56 | 4700 | 1.5903 | 0.7072 | | 1.7253 | 0.57 | 4800 | 1.5833 | 0.7080 | | 1.7391 | 0.58 | 4900 | 1.5779 | 0.7087 | | 1.7002 | 0.59 | 5000 | 1.5664 | 0.7099 | | 1.7105 | 0.6 | 5100 | 1.5658 | 0.7103 | | 1.6996 | 0.62 | 5200 | 1.5602 | 0.7106 | | 1.6754 | 0.63 | 5300 | 1.5554 | 0.7113 | | 1.6901 | 0.64 | 5400 | 1.5487 | 0.7117 | | 1.6858 | 0.65 | 5500 | 1.5445 | 0.7128 | | 1.6778 | 0.66 | 5600 | 1.5371 | 0.7136 | | 1.6653 | 0.67 | 5700 | 1.5324 | 0.7139 | | 1.6692 | 0.69 | 5800 | 1.5267 | 0.7146 | | 1.6687 | 0.7 | 5900 | 1.5238 | 0.7156 | | 1.6362 | 0.71 | 6000 | 1.5225 | 0.7155 | | 1.6468 | 0.72 | 6100 | 1.5203 | 0.7158 | | 1.6595 | 0.73 | 6200 | 1.5128 | 0.7162 | | 1.615 | 0.75 | 6300 | 1.5042 | 0.7176 | | 1.6251 | 0.76 | 6400 | 1.5026 | 0.7179 | | 1.63 | 0.77 | 6500 | 1.5041 | 0.7179 | | 1.6251 | 0.78 | 6600 | 1.4978 | 0.7186 | | 1.6219 | 0.79 | 6700 | 1.4906 | 0.7191 | | 1.6186 | 0.81 | 6800 | 1.4931 | 0.7195 | | 1.6053 | 0.82 | 6900 | 1.4891 | 0.7198 | | 1.6285 | 0.83 | 7000 | 1.4832 | 0.7203 | | 1.6024 | 0.84 | 7100 | 1.4766 | 0.7212 | | 1.597 | 0.85 | 7200 | 1.4752 | 0.7212 | | 1.5955 | 0.86 | 7300 | 1.4742 | 0.7213 | | 1.6006 | 0.88 | 7400 | 1.4726 | 0.7216 | | 1.6186 | 0.89 | 7500 | 1.4652 | 0.7228 | | 1.6103 | 0.9 | 7600 | 1.4657 | 0.7221 | | 1.5828 | 0.91 | 7700 | 1.4602 | 0.7233 | | 1.5926 | 0.92 | 7800 | 1.4609 | 0.7229 | | 1.5896 | 0.94 | 7900 | 1.4521 | 0.7245 | | 1.5799 | 0.95 | 8000 | 1.4577 | 0.7246 | | 1.5931 | 0.96 | 8100 | 1.4517 | 0.7247 | | 1.578 | 0.97 | 8200 | 1.4463 | 0.7249 | | 1.5609 | 0.98 | 8300 | 1.4424 | 0.7257 | | 1.5662 | 0.99 | 8400 | 1.4434 | 0.7254 | | 1.5449 | 1.01 | 8500 | 1.4419 | 0.7253 | | 1.5629 | 1.02 | 8600 | 1.4385 | 0.7263 | | 1.5609 | 1.03 | 8700 | 1.4332 | 0.7266 | | 1.5764 | 1.04 | 8800 | 1.4302 | 0.7270 | | 1.5434 | 1.05 | 8900 | 1.4294 | 0.7268 | | 1.54 | 1.07 | 9000 | 1.4286 | 0.7273 | | 1.5519 | 1.08 | 9100 | 1.4304 | 0.7274 | | 1.5286 | 1.09 | 9200 | 1.4243 | 0.7276 | | 1.5582 | 1.1 | 9300 | 1.4256 | 0.7273 | | 1.5292 | 1.11 | 9400 | 1.4181 | 0.7286 | | 1.5513 | 1.12 | 9500 | 1.4181 | 0.7281 | | 1.5363 | 1.14 | 9600 | 1.4145 | 0.7293 | | 1.5427 | 1.15 | 9700 | 1.4095 | 0.7297 | | 1.528 | 1.16 | 9800 | 1.4116 | 0.7294 | | 1.524 | 1.17 | 9900 | 1.4111 | 0.7296 | | 1.5364 | 1.18 | 10000 | 1.4091 | 0.7300 | | 1.5299 | 1.2 | 10100 | 1.4012 | 0.7313 | | 1.5144 | 1.21 | 10200 | 1.4006 | 0.7315 | | 1.5304 | 1.22 | 10300 | 1.4012 | 0.7301 | | 1.5245 | 1.23 | 10400 | 1.3983 | 0.7309 | | 1.5105 | 1.24 | 10500 | 1.3989 | 0.7308 | | 1.5056 | 1.26 | 10600 | 1.3991 | 0.7306 | | 1.519 | 1.27 | 10700 | 1.3929 | 0.7316 | | 1.5213 | 1.28 | 10800 | 1.3945 | 0.7316 | | 1.5198 | 1.29 | 10900 | 1.3883 | 0.7323 | | 1.5105 | 1.3 | 11000 | 1.3890 | 0.7321 | | 1.507 | 1.31 | 11100 | 1.3872 | 0.7324 | | 1.4973 | 1.33 | 11200 | 1.3887 | 0.7323 | | 1.4949 | 1.34 | 11300 | 1.3855 | 0.7329 | | 1.4967 | 1.35 | 11400 | 1.3862 | 0.7328 | | 1.5064 | 1.36 | 11500 | 1.3851 | 0.7331 | | 1.5067 | 1.37 | 11600 | 1.3854 | 0.7327 | | 1.5056 | 1.39 | 11700 | 1.3839 | 0.7330 | | 1.4985 | 1.4 | 11800 | 1.3813 | 0.7333 | | 1.4831 | 1.41 | 11900 | 1.3792 | 0.7335 | | 1.4961 | 1.42 | 12000 | 1.3741 | 0.7343 | | 1.4818 | 1.43 | 12100 | 1.3774 | 0.7337 | | 1.465 | 1.44 | 12200 | 1.3715 | 0.7348 | | 1.4776 | 1.46 | 12300 | 1.3665 | 0.7358 | | 1.4839 | 1.47 | 12400 | 1.3679 | 0.7350 | | 1.5105 | 1.48 | 12500 | 1.3674 | 0.7357 | | 1.4845 | 1.49 | 12600 | 1.3671 | 0.7353 | | 1.47 | 1.5 | 12700 | 1.3658 | 0.7358 | | 1.4905 | 1.52 | 12800 | 1.3634 | 0.7356 | | 1.4866 | 1.53 | 12900 | 1.3644 | 0.7355 | | 1.4749 | 1.54 | 13000 | 1.3596 | 0.7360 | | 1.4836 | 1.55 | 13100 | 1.3588 | 0.7359 | | 1.4671 | 1.56 | 13200 | 1.3579 | 0.7368 | | 1.4674 | 1.57 | 13300 | 1.3573 | 0.7363 | | 1.4776 | 1.59 | 13400 | 1.3578 | 0.7364 | | 1.4556 | 1.6 | 13500 | 1.3563 | 0.7363 | | 1.4645 | 1.61 | 13600 | 1.3546 | 0.7363 | | 1.4764 | 1.62 | 13700 | 1.3519 | 0.7374 | | 1.4621 | 1.63 | 13800 | 1.3529 | 0.7368 | | 1.471 | 1.65 | 13900 | 1.3479 | 0.7379 | | 1.4731 | 1.66 | 14000 | 1.3485 | 0.7376 | | 1.4643 | 1.67 | 14100 | 1.3490 | 0.7376 | | 1.4571 | 1.68 | 14200 | 1.3527 | 0.7368 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.13.3
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-base", "model-index": [{"name": "deberta-base-finetuned-nonbinary", "results": []}]}
fill-mask
tejaskamtam/deberta-base-finetuned-nonbinary
[ "transformers", "pytorch", "deberta", "fill-mask", "generated_from_trainer", "base_model:microsoft/deberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T08:30:20+00:00
[]
[]
TAGS #transformers #pytorch #deberta #fill-mask #generated_from_trainer #base_model-microsoft/deberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
deberta-base-finetuned-nonbinary ================================ This model is a fine-tuned version of microsoft/deberta-base on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.3484 * Accuracy: 0.7375 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: 8 * eval\_batch\_size: 8 * seed: 100 * 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 * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.31.0 * Pytorch 2.2.0+cu121 * Datasets 2.17.0 * Tokenizers 0.13.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 100\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* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]
[ "TAGS\n#transformers #pytorch #deberta #fill-mask #generated_from_trainer #base_model-microsoft/deberta-base #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: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 100\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* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]
[ 62, 126, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #deberta #fill-mask #generated_from_trainer #base_model-microsoft/deberta-base #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: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 100\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* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]
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# **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "35.10 +/- 27.01", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
haihuynh/Reinforce-Pixelcopter-PLE-v0
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
2024-02-13T08:32:43+00:00
[]
[]
TAGS #Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
[ 41, 58 ]
[ "passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]
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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. 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{"library_name": "transformers", "tags": []}
null
bbb1167/fypaicharacter
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-13T08:35:12+00:00
[ "1910.09700" ]
[]
TAGS #transformers #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 #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" ]
[ 26, 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 #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" ]
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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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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 ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cpu - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]}
text-classification
ImNotTarzan/finetuning-sentiment-model-3000-samples
[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-13T08:37:07+00:00
[]
[]
TAGS #transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of distilbert-base-uncased 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: 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 ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cpu - Datasets 2.17.0 - Tokenizers 0.15.1
[ "# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased 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: 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", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cpu\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased 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: 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", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cpu\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
[ 68, 41, 6, 12, 8, 3, 90, 33 ]
[ "passage: TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased 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: 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### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cpu\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
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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. --> # capstone-bert-qa This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.9547 - Validation Loss: 5.9507 - Epoch: 0 ## 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': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.9547 | 5.9507 | 0 | ### 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": "bert-base-cased", "model-index": [{"name": "capstone-bert-qa", "results": []}]}
question-answering
Shruthi-S/capstone-bert-qa
[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-13T08:40:10+00:00
[]
[]
TAGS #transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us
capstone-bert-qa ================ This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 5.9547 * Validation Loss: 5.9507 * Epoch: 0 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': 0.001, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} * 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': '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': 0.001, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\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 #bert #question-answering #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #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': 0.001, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\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" ]
[ 58, 198, 4, 31 ]
[ "passage: TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #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': 0.001, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\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" ]
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