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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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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
text-generation
Basha738/llama2-supervised-ft-5epochs
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T06:30:17+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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Finetune mT5-large with LoRA for English-Vietnamese translation # How to use it ```python import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-large") tokenizer = AutoTokenizer.from_pretrained("google/mt5-large") # load Peft model peft_model = PeftModel.from_pretrained(base_model, "adapter") peft_model.save_pretrained("inference") del peft_model, base_model # reload with mixed precision device = 'cuda' if torch.cuda.is_available() else 'cpu' model = AutoModelForSeq2SeqLM.from_pretrained( "inference", torch_dtype=torch.float16 ).to(device) ``` # Inference ```python def preprocess(input_text:str = None, prefix:str = 'vi'): return prefix + input_text + "<END>" def postprocess(output_text): return output_text[4:].split("<END>")[0] def translate(text:str = None, prefix:str = 'vi'): text = preprocess(text, prefix) tokenized = tokenizer( text, padding=True, truncation=True, return_tensors="pt" ).to(model.device) with torch.no_grad(): outputs = model(**tokenized, beam_search=4, use_cache=True) output = tokenizer.decode(outputs[0], skip_special_tokens=True) return postprocess(output) ``` ## Translate sentences For translate English to Vietnamese ``` en2vi = 'VnExpress provides latest Vietnam news, regional, business, financial, industries, travel news and views to policy makers' prefix = 'en: ' result = translate(text = en2vi, prefix = prefix) # VnExpress cung cấp tin tức mới nhất về Việt Nam, khu vực, kinh doanh, tài chính, công nghiệp, tin tức du lịch và quan điểm cho các nhà hoạch định chính sách ``` For translate Vietnamese to English ``` vi2en = 'VnExpress là một tờ báo tại Việt Nam được thành lập bởi tập đoàn FPT, ra mắt vào ngày 26 tháng 2 năm 2001' prefix = 'vi: ' result = translate(text = vi2en, prefix = prefix) # VnExpress is a newspaper in Vietnam founded by FPT Corporation, launched on February 26, 2001. ``` # Hyperparameters Training ``` - batch_size = 2 - gradient_acc = 14 - total_steps = 100k - warm_up = 0.1 - max_len = 512 - learning_rate = 3e-5 - scheduler = cosine - deepspeed = v1 - gpus = 2x3090 - dataset_train = presencesw/hash_v3 - lora_target = ['q', 'wi_1', 'k', 'wi_0', 'v', 'wo', 'o', 'lm_head'] - trainable parameter: 0.38228893842642636% - LORA_R=8 - LORA_ALPHA=8 - LORA_DROPOUT = 0.1 ``` - Total time: ~168 hours - Total dataset: ~5.6M samples - Curve loss ![image/png](https://cdn-uploads.huggingface.co/production/uploads/622eb5d2165ba2c1bcbc76f1/PCui5aohDM2eyDI4OwTyk.png)
{"language": ["en", "vi"], "license": "apache-2.0", "pipeline_tag": "translation"}
translation
pythera/translator-v1
[ "safetensors", "translation", "en", "vi", "license:apache-2.0", "region:us" ]
2024-02-08T06:31:33+00:00
[]
[ "en", "vi" ]
TAGS #safetensors #translation #en #vi #license-apache-2.0 #region-us
Finetune mT5-large with LoRA for English-Vietnamese translation # How to use it # Inference ## Translate sentences For translate English to Vietnamese For translate Vietnamese to English # Hyperparameters Training - Total time: ~168 hours - Total dataset: ~5.6M samples - Curve loss !image/png
[ "# How to use it", "# Inference", "## Translate sentences\nFor translate English to Vietnamese\n\n\nFor translate Vietnamese to English", "# Hyperparameters Training\n\n- Total time: ~168 hours\n- Total dataset: ~5.6M samples\n\n- Curve loss\n\n!image/png" ]
[ "TAGS\n#safetensors #translation #en #vi #license-apache-2.0 #region-us \n", "# How to use it", "# Inference", "## Translate sentences\nFor translate English to Vietnamese\n\n\nFor translate Vietnamese to English", "# Hyperparameters Training\n\n- Total time: ~168 hours\n- Total dataset: ~5.6M samples\n\n- Curve loss\n\n!image/png" ]
[ 26, 5, 4, 19, 31 ]
[ "passage: TAGS\n#safetensors #translation #en #vi #license-apache-2.0 #region-us \n# How to use it# Inference## Translate sentences\nFor translate English to Vietnamese\n\n\nFor translate Vietnamese to English# Hyperparameters Training\n\n- Total time: ~168 hours\n- Total dataset: ~5.6M samples\n\n- Curve loss\n\n!image/png" ]
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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. --> # Fine-Tuned_Model3 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7362 - Accuracy: 0.608 - F1: 0.5096 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 3.2255 | 5.0 | 20 | 1.9574 | 0.512 | 0.3083 | | 1.3773 | 10.0 | 40 | 0.8854 | 0.584 | 0.4617 | | 0.869 | 15.0 | 60 | 0.7880 | 0.608 | 0.4795 | | 0.7966 | 20.0 | 80 | 0.7732 | 0.6 | 0.4846 | | 0.8458 | 25.0 | 100 | 0.7795 | 0.576 | 0.4112 | | 0.8135 | 30.0 | 120 | 0.7362 | 0.608 | 0.5096 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "f1"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "Fine-Tuned_Model3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.608, "name": "Accuracy"}, {"type": "f1", "value": 0.5096170704866357, "name": "F1"}]}]}]}
image-classification
arpanl/Fine-Tuned_Model3
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T06:31:34+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
Fine-Tuned\_Model3 ================== This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.7362 * Accuracy: 0.608 * F1: 0.5096 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 30 ### 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: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30", "### 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 #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30", "### 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" ]
[ 83, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30### 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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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-uncased-finetuned-amazon_reviews_multi This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2149 - Accuracy: 0.9402 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.215 | 1.0 | 1250 | 0.1709 | 0.9352 | | 0.136 | 2.0 | 2500 | 0.2149 | 0.9402 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-finetuned-amazon_reviews_multi", "results": []}]}
text-classification
JoelVIU/bert-base-uncased-finetuned-amazon_reviews_multi
[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T06:32:09+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
bert-base-uncased-finetuned-amazon\_reviews\_multi ================================================== This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.2149 * Accuracy: 0.9402 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 68, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
# ITT-AF/ITT-42dot_LLM-SFT-1.3B-v2.0 This model is a fine-tuned version of [42dot/42dot_LLM-SFT-1.3B](https://huggingface.co/42dot/42dot_LLM-SFT-1.3B) on an custom 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: 24 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.0.0 - Tokenizers 0.15.0
{"license": "cc-by-nc-4.0"}
text-generation
ITT-AF/ITT-42dot_LLM-SFT-1.3B-v2.0
[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T06:34:04+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# ITT-AF/ITT-42dot_LLM-SFT-1.3B-v2.0 This model is a fine-tuned version of 42dot/42dot_LLM-SFT-1.3B on an custom 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: 24 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.0.0 - Tokenizers 0.15.0
[ "# ITT-AF/ITT-42dot_LLM-SFT-1.3B-v2.0\n\nThis model is a fine-tuned version of 42dot/42dot_LLM-SFT-1.3B on an custom 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: 24\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 96\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.0.0\n- Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ITT-AF/ITT-42dot_LLM-SFT-1.3B-v2.0\n\nThis model is a fine-tuned version of 42dot/42dot_LLM-SFT-1.3B on an custom 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: 24\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 96\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.0.0\n- Tokenizers 0.15.0" ]
[ 58, 51, 6, 12, 8, 3, 126, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ITT-AF/ITT-42dot_LLM-SFT-1.3B-v2.0\n\nThis model is a fine-tuned version of 42dot/42dot_LLM-SFT-1.3B on an custom 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: 24\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 96\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1.0\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.0.0\n- Tokenizers 0.15.0" ]
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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. --> # outputs This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4939 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0181 | 0.24 | 4 | 1.9684 | | 2.0616 | 0.47 | 8 | 1.8863 | | 1.8467 | 0.71 | 12 | 1.8116 | | 1.707 | 0.94 | 16 | 1.7309 | | 1.7886 | 1.18 | 20 | 1.6529 | | 1.6539 | 1.41 | 24 | 1.5884 | | 1.5149 | 1.65 | 28 | 1.5568 | | 1.4526 | 1.88 | 32 | 1.5390 | | 1.5335 | 2.12 | 36 | 1.5283 | | 1.5668 | 2.35 | 40 | 1.5211 | | 1.3914 | 2.59 | 44 | 1.5158 | | 1.5769 | 2.82 | 48 | 1.5113 | | 1.3794 | 3.06 | 52 | 1.5075 | | 1.5274 | 3.29 | 56 | 1.5043 | | 1.5247 | 3.53 | 60 | 1.5016 | | 1.4291 | 3.76 | 64 | 1.4993 | | 1.4233 | 4.0 | 68 | 1.4974 | | 1.4353 | 4.24 | 72 | 1.4960 | | 1.6016 | 4.47 | 76 | 1.4949 | | 1.4416 | 4.71 | 80 | 1.4942 | | 1.4654 | 4.94 | 84 | 1.4939 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu118 - Datasets 2.17.0 - Tokenizers 0.15.1
{"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "LLama_weights/tmp", "model-index": [{"name": "outputs", "results": []}]}
null
Basha738/outputs
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:LLama_weights/tmp", "region:us" ]
2024-02-08T06:34:14+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-LLama_weights/tmp #region-us
outputs ======= This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.4939 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: 1 * eval\_batch\_size: 1 * seed: 42 * gradient\_accumulation\_steps: 16 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.2.0+cu118 * 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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-LLama_weights/tmp #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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 44, 141, 4, 39 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-LLama_weights/tmp #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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\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 the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-cybersac This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.3745 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.8257 | 1.0 | 1004 | 7.7518 | | 7.4738 | 2.0 | 2008 | 7.4700 | | 7.3193 | 3.0 | 3012 | 7.3745 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1 - Datasets 2.16.1 - Tokenizers 0.15.0
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "gpt2-cybersac", "results": []}]}
text-generation
thomaslwang/gpt2-cybersac
[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T06:34:29+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
gpt2-cybersac ============= This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 7.3745 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1 * Datasets 2.16.1 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]
[ 72, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]
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null
null
transformers
# SeverusWestLake-7B-DPO - AWQ - Model creator: [s3nh](https://huggingface.co/s3nh) - Original model: [SeverusWestLake-7B-DPO](https://huggingface.co/s3nh/SeverusWestLake-7B-DPO) ![image](west.png)
{"license": "mit", "library_name": "transformers", "tags": ["mergekit", "merge"], "model_name": "SeverusWestLake-7B-DPO", "base_model": ["FelixChao/Sectumsempra-7B-DPO", "cognitivecomputations/WestLake-7B-v2-laser"], "model_creator": "s3nh", "model_type": "mistral", "pipeline_tag": "text-generation", "prompt_template": "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", "quantized_by": "Suparious"}
text-generation
solidrust/SeverusWestLake-7B-DPO-AWQ
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:FelixChao/Sectumsempra-7B-DPO", "base_model:cognitivecomputations/WestLake-7B-v2-laser", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T06:34:29+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-FelixChao/Sectumsempra-7B-DPO #base_model-cognitivecomputations/WestLake-7B-v2-laser #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
# SeverusWestLake-7B-DPO - AWQ - Model creator: s3nh - Original model: SeverusWestLake-7B-DPO !image
[ "# SeverusWestLake-7B-DPO - AWQ\n\n- Model creator: s3nh\n- Original model: SeverusWestLake-7B-DPO\n\n!image" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-FelixChao/Sectumsempra-7B-DPO #base_model-cognitivecomputations/WestLake-7B-v2-laser #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# SeverusWestLake-7B-DPO - AWQ\n\n- Model creator: s3nh\n- Original model: SeverusWestLake-7B-DPO\n\n!image" ]
[ 102, 37 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-FelixChao/Sectumsempra-7B-DPO #base_model-cognitivecomputations/WestLake-7B-v2-laser #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# SeverusWestLake-7B-DPO - AWQ\n\n- Model creator: s3nh\n- Original model: SeverusWestLake-7B-DPO\n\n!image" ]
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null
null
diffusers
# LoRA text2image fine-tuning - nightdude/kanji-lora These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "inference": true}
text-to-image
nightdude/kanji-lora
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]
2024-02-08T06:35:22+00:00
[]
[]
TAGS #diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
# LoRA text2image fine-tuning - nightdude/kanji-lora These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3
[ "# LoRA text2image fine-tuning - nightdude/kanji-lora\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]
[ "TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA text2image fine-tuning - nightdude/kanji-lora\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]
[ 66, 93 ]
[ "passage: TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - nightdude/kanji-lora\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]
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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": "meta-llama/Llama-2-7b-hf"}
null
ManuThakur/Llama2Trained
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]
2024-02-08T06:37:31+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #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-meta-llama/Llama-2-7b-hf #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" ]
[ 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 #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #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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# **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": "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": "25.70 +/- 13.80", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
turgutburak01/Pixelcopter-PLE-v0
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
2024-02-08T06:38:29+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
<!-- 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. --> # smolm-autoreg-bpe-counterfactual-babylm-random_removal-seed_211-3e-4 This model was trained from scratch on the kanishka/counterfactual-babylm-random_removal dataset. It achieves the following results on the evaluation set: - Loss: 3.4079 - Accuracy: 0.4097 ## 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 64 - seed: 211 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.7403 | 1.0 | 18586 | 3.8964 | 0.3473 | | 3.4414 | 2.0 | 37172 | 3.6106 | 0.3762 | | 3.2952 | 3.0 | 55758 | 3.5047 | 0.3879 | | 3.2087 | 4.0 | 74344 | 3.4663 | 0.3950 | | 3.149 | 5.0 | 92930 | 3.4383 | 0.3987 | | 3.101 | 6.0 | 111516 | 3.3864 | 0.4021 | | 3.0614 | 7.0 | 130102 | 3.3728 | 0.4053 | | 3.0311 | 8.0 | 148688 | 3.3712 | 0.4057 | | 2.9996 | 9.0 | 167274 | 3.3636 | 0.4071 | | 2.9768 | 10.0 | 185860 | 3.3474 | 0.4088 | | 2.9515 | 11.0 | 204446 | 3.3726 | 0.4089 | | 2.9309 | 12.0 | 223032 | 3.3788 | 0.4076 | | 2.9078 | 13.0 | 241618 | 3.3546 | 0.4109 | | 2.8874 | 14.0 | 260204 | 3.3762 | 0.4093 | | 2.8664 | 15.0 | 278790 | 3.3832 | 0.4096 | | 2.8486 | 16.0 | 297376 | 3.3725 | 0.4112 | | 2.827 | 17.0 | 315962 | 3.3913 | 0.4099 | | 2.8082 | 18.0 | 334548 | 3.3945 | 0.4100 | | 2.7888 | 19.0 | 353134 | 3.4077 | 0.4095 | | 2.7762 | 20.0 | 371720 | 3.4079 | 0.4097 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"tags": ["generated_from_trainer"], "datasets": ["kanishka/counterfactual-babylm-random_removal"], "metrics": ["accuracy"], "model-index": [{"name": "smolm-autoreg-bpe-counterfactual-babylm-random_removal-seed_211-3e-4", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "kanishka/counterfactual-babylm-random_removal", "type": "kanishka/counterfactual-babylm-random_removal"}, "metrics": [{"type": "accuracy", "value": 0.40973803931895864, "name": "Accuracy"}]}]}]}
text-generation
kanishka/smolm-autoreg-bpe-counterfactual-babylm-random_removal-seed_211-3e-4
[ "transformers", "tensorboard", "safetensors", "opt", "text-generation", "generated_from_trainer", "dataset:kanishka/counterfactual-babylm-random_removal", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T06:38:30+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-random_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
smolm-autoreg-bpe-counterfactual-babylm-random\_removal-seed\_211-3e-4 ====================================================================== This model was trained from scratch on the kanishka/counterfactual-babylm-random\_removal dataset. It achieves the following results on the evaluation set: * Loss: 3.4079 * Accuracy: 0.4097 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: 0.0003 * train\_batch\_size: 32 * eval\_batch\_size: 64 * seed: 211 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 32000 * num\_epochs: 20.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 211\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\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 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-random_removal #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: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 211\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\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 2.16.1\n* Tokenizers 0.15.1" ]
[ 82, 132, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-random_removal #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: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 211\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\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 2.16.1\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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{"library_name": "transformers", "tags": []}
text-generation
anish005/mistral-reddit
[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T06:40:22+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mistral #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 #mistral #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" ]
[ 59, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #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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null
null
transformers
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{"library_name": "transformers", "tags": []}
null
Jaerim/bloom-7b1-lora-tagger_3
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T06:49:56+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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
# merged 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 passthrough merge method. ### Models Merged The following models were included in the merge: * [ChaiML/season_4_top_solution](https://huggingface.co/ChaiML/season_4_top_solution) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: bfloat16 merge_method: passthrough slices: - sources: - layer_range: [0, 30] model: model: path: ChaiML/season_4_top_solution - sources: - layer_range: [10, 40] model: model: path: ChaiML/season_4_top_solution ```
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["ChaiML/season_4_top_solution"]}
text-generation
gotchu/s8-knarf
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:ChaiML/season_4_top_solution", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T06:50:26+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #base_model-ChaiML/season_4_top_solution #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# merged This is a merge of pre-trained language models created using mergekit. ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * ChaiML/season_4_top_solution ### Configuration The following YAML configuration was used to produce this model:
[ "# merged\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/season_4_top_solution", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-ChaiML/season_4_top_solution #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# merged\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/season_4_top_solution", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 72, 19, 4, 17, 29, 17 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-ChaiML/season_4_top_solution #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# merged\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/season_4_top_solution### 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. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1599 - Accuracy: 0.5813 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 13 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 1.8887 | 0.35 | | No log | 2.0 | 80 | 1.5494 | 0.425 | | No log | 3.0 | 120 | 1.4015 | 0.5188 | | No log | 4.0 | 160 | 1.2919 | 0.55 | | No log | 5.0 | 200 | 1.2205 | 0.5813 | | No log | 6.0 | 240 | 1.2246 | 0.575 | | No log | 7.0 | 280 | 1.2053 | 0.5312 | | No log | 8.0 | 320 | 1.1487 | 0.5687 | | No log | 9.0 | 360 | 1.1727 | 0.5437 | | No log | 10.0 | 400 | 1.1459 | 0.55 | | No log | 11.0 | 440 | 1.1313 | 0.5813 | | No log | 12.0 | 480 | 1.0990 | 0.6062 | | 1.1138 | 13.0 | 520 | 1.1020 | 0.6188 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "image_classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.58125, "name": "Accuracy"}]}]}]}
image-classification
yangswei/image_classification
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T06:51:26+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
image\_classification ===================== This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.1599 * Accuracy: 0.5813 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 13 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 13", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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: 13", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 86, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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: 13### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\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. --> # TrOCR_0208-2 This model is a fine-tuned version of [microsoft/trocr-base-stage1](https://huggingface.co/microsoft/trocr-base-stage1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2584 - Cer: 0.1211 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.3873 | 1.71 | 500 | 1.6813 | 0.2361 | | 0.8298 | 3.42 | 1000 | 1.7390 | 0.2441 | | 0.5587 | 5.14 | 1500 | 1.5896 | 0.2090 | | 0.376 | 6.85 | 2000 | 1.4717 | 0.1775 | | 0.2847 | 8.56 | 2500 | 1.5528 | 0.1928 | | 0.2376 | 10.27 | 3000 | 1.4412 | 0.1727 | | 0.2101 | 11.99 | 3500 | 1.3770 | 0.1592 | | 0.2551 | 13.7 | 4000 | 1.4311 | 0.1564 | | 0.226 | 15.41 | 4500 | 1.2536 | 0.1337 | | 0.1365 | 17.12 | 5000 | 1.2753 | 0.1272 | | 0.14 | 18.84 | 5500 | 1.2584 | 0.1211 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.13.0 - Tokenizers 0.15.0
{"tags": ["generated_from_trainer"], "base_model": "microsoft/trocr-base-stage1", "model-index": [{"name": "TrOCR_0208-2", "results": []}]}
null
yoon1000/TrOCR_0208-2
[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "base_model:microsoft/trocr-base-stage1", "endpoints_compatible", "region:us" ]
2024-02-08T06:51:30+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-microsoft/trocr-base-stage1 #endpoints_compatible #region-us
TrOCR\_0208-2 ============= This model is a fine-tuned version of microsoft/trocr-base-stage1 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.2584 * Cer: 0.1211 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.13.0 * Tokenizers 0.15.0
[ "### 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* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.13.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-microsoft/trocr-base-stage1 #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* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.13.0\n* Tokenizers 0.15.0" ]
[ 56, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-microsoft/trocr-base-stage1 #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* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.13.0\n* Tokenizers 0.15.0" ]
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null
null
transformers
# MiquMaid-v2-70B 2.4bpw ## Description Exllama quant of [NeverSleep/MiquMaid-v2-70B](https://huggingface.co/NeverSleep/MiquMaid-v2-70B) ## Other quants: EXL2: [4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-3.5bpw-exl2), [3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-3bpw-exl2), [2.4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-2.4bpw-exl2), [2.3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-2.3bpw-exl2) 2.4bpw is probably the most you can fit in a 24gb card GGUF: [2bit Imatrix GGUF](https://huggingface.co/Kooten/MiquMaid-v2-70B-Imatrix-GGUF) ## Prompt format: Alpaca ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}
text-generation
Kooten/MiquMaid-v2-70B-2.4bpw-exl2
[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T06:55:07+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# MiquMaid-v2-70B 2.4bpw ## Description Exllama quant of NeverSleep/MiquMaid-v2-70B ## Other quants: EXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw 2.4bpw is probably the most you can fit in a 24gb card GGUF: 2bit Imatrix GGUF ## Prompt format: Alpaca ## Contact Kooten on discord URL
[ "# MiquMaid-v2-70B 2.4bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MiquMaid-v2-70B 2.4bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ 75, 14, 21, 60, 8, 7 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MiquMaid-v2-70B 2.4bpw## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL" ]
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null
null
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见[nenekochan/Yi-6B-yoruno](https://huggingface.co/nenekochan/Yi-6B-yoruno)
{"language": ["zh"], "license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences"], "base_model": "nenekochan/Yi-6B-yoruno", "inference": false}
null
nenekochan/Yi-6B-yoruno-GGUF
[ "gguf", "not-for-all-audiences", "zh", "base_model:nenekochan/Yi-6B-yoruno", "license:cc-by-nc-4.0", "region:us" ]
2024-02-08T06:56:27+00:00
[]
[ "zh" ]
TAGS #gguf #not-for-all-audiences #zh #base_model-nenekochan/Yi-6B-yoruno #license-cc-by-nc-4.0 #region-us
见nenekochan/Yi-6B-yoruno
[]
[ "TAGS\n#gguf #not-for-all-audiences #zh #base_model-nenekochan/Yi-6B-yoruno #license-cc-by-nc-4.0 #region-us \n" ]
[ 47 ]
[ "passage: TAGS\n#gguf #not-for-all-audiences #zh #base_model-nenekochan/Yi-6B-yoruno #license-cc-by-nc-4.0 #region-us \n" ]
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null
null
transformers
# MiquMaid-v2-70B-DPO 2.4bpw ## Description Exllama quant of [NeverSleep/MiquMaid-v2-70B-DPO](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) ## Other quants: EXL2: [4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3.5bpw-exl2), [3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3bpw-exl2), [2.4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.4bpw-exl2), [2.3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.3bpw-exl2) 2.4bpw is probably the most you can fit in a 24gb card GGUF: [2bit Imatrix GGUF](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-Imatrix-GGUF) ## Prompt format: Alpaca ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}
text-generation
Kooten/MiquMaid-v2-70B-DPO-2.4bpw-exl2
[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T06:56:41+00:00
[]
[]
TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# MiquMaid-v2-70B-DPO 2.4bpw ## Description Exllama quant of NeverSleep/MiquMaid-v2-70B-DPO ## Other quants: EXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw 2.4bpw is probably the most you can fit in a 24gb card GGUF: 2bit Imatrix GGUF ## Prompt format: Alpaca ## Contact Kooten on discord URL
[ "# MiquMaid-v2-70B-DPO 2.4bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MiquMaid-v2-70B-DPO 2.4bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ 74, 17, 24, 60, 8, 7 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MiquMaid-v2-70B-DPO 2.4bpw## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF## Prompt format: Alpaca## Contact\nKooten on discord\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. --> # 1000_STEPS_5e7 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6624 - Rewards/chosen: -0.0929 - Rewards/rejected: -0.1682 - Rewards/accuracies: 0.5451 - Rewards/margins: 0.0754 - Logps/rejected: -16.8218 - Logps/chosen: -15.0454 - Logits/rejected: -0.1317 - Logits/chosen: -0.1315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6932 | 0.1 | 50 | 0.6927 | 0.0015 | 0.0005 | 0.4242 | 0.0010 | -15.1347 | -14.1020 | -0.0215 | -0.0215 | | 0.6901 | 0.2 | 100 | 0.6901 | -0.0121 | -0.0185 | 0.4835 | 0.0063 | -15.3239 | -14.2383 | -0.0268 | -0.0268 | | 0.6837 | 0.29 | 150 | 0.6838 | -0.0153 | -0.0352 | 0.5209 | 0.0199 | -15.4913 | -14.2697 | -0.0332 | -0.0331 | | 0.6775 | 0.39 | 200 | 0.6808 | -0.0261 | -0.0529 | 0.5363 | 0.0268 | -15.6684 | -14.3783 | -0.0453 | -0.0451 | | 0.6761 | 0.49 | 250 | 0.6779 | -0.0555 | -0.0896 | 0.5297 | 0.0340 | -16.0350 | -14.6723 | -0.0533 | -0.0531 | | 0.6692 | 0.59 | 300 | 0.6771 | -0.0812 | -0.1192 | 0.5121 | 0.0380 | -16.3311 | -14.9285 | -0.0659 | -0.0657 | | 0.683 | 0.68 | 350 | 0.6739 | -0.0352 | -0.0789 | 0.5385 | 0.0437 | -15.9286 | -14.4687 | -0.0583 | -0.0581 | | 0.677 | 0.78 | 400 | 0.6733 | -0.0430 | -0.0895 | 0.5451 | 0.0464 | -16.0340 | -14.5472 | -0.0635 | -0.0633 | | 0.6665 | 0.88 | 450 | 0.6692 | -0.0347 | -0.0904 | 0.5516 | 0.0557 | -16.0436 | -14.4638 | -0.0724 | -0.0721 | | 0.6559 | 0.98 | 500 | 0.6668 | -0.0374 | -0.0989 | 0.5516 | 0.0615 | -16.1283 | -14.4907 | -0.0752 | -0.0750 | | 0.6406 | 1.07 | 550 | 0.6665 | -0.0482 | -0.1114 | 0.5582 | 0.0632 | -16.2528 | -14.5988 | -0.0983 | -0.0981 | | 0.6301 | 1.17 | 600 | 0.6656 | -0.0655 | -0.1316 | 0.5495 | 0.0661 | -16.4553 | -14.7718 | -0.1067 | -0.1065 | | 0.6206 | 1.27 | 650 | 0.6648 | -0.0581 | -0.1265 | 0.5407 | 0.0684 | -16.4041 | -14.6977 | -0.1163 | -0.1161 | | 0.6015 | 1.37 | 700 | 0.6641 | -0.0734 | -0.1439 | 0.5495 | 0.0706 | -16.5788 | -14.8504 | -0.1219 | -0.1217 | | 0.6299 | 1.46 | 750 | 0.6637 | -0.0883 | -0.1601 | 0.5429 | 0.0719 | -16.7407 | -14.9994 | -0.1233 | -0.1231 | | 0.6031 | 1.56 | 800 | 0.6630 | -0.0881 | -0.1617 | 0.5407 | 0.0736 | -16.7566 | -14.9977 | -0.1270 | -0.1267 | | 0.6474 | 1.66 | 850 | 0.6633 | -0.0908 | -0.1640 | 0.5451 | 0.0733 | -16.7795 | -15.0245 | -0.1278 | -0.1276 | | 0.6229 | 1.76 | 900 | 0.6630 | -0.0923 | -0.1664 | 0.5473 | 0.0740 | -16.8031 | -15.0403 | -0.1290 | -0.1288 | | 0.6085 | 1.86 | 950 | 0.6628 | -0.0924 | -0.1669 | 0.5429 | 0.0745 | -16.8081 | -15.0405 | -0.1305 | -0.1303 | | 0.6062 | 1.95 | 1000 | 0.6629 | -0.0932 | -0.1676 | 0.5363 | 0.0744 | -16.8155 | -15.0492 | -0.1302 | -0.1300 | | 0.6236 | 2.05 | 1050 | 0.6628 | -0.0937 | -0.1684 | 0.5451 | 0.0747 | -16.8231 | -15.0539 | -0.1314 | -0.1311 | | 0.6217 | 2.15 | 1100 | 0.6629 | -0.0932 | -0.1676 | 0.5451 | 0.0744 | -16.8150 | -15.0489 | -0.1308 | -0.1305 | | 0.6255 | 2.25 | 1150 | 0.6624 | -0.0927 | -0.1682 | 0.5495 | 0.0755 | -16.8214 | -15.0442 | -0.1315 | -0.1313 | | 0.598 | 2.34 | 1200 | 0.6624 | -0.0929 | -0.1682 | 0.5451 | 0.0754 | -16.8218 | -15.0454 | -0.1317 | -0.1315 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.0+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1
{"tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "1000_STEPS_5e7", "results": []}]}
text-generation
tsavage68/1200STEPS_5e7_0.1beta_DPO_zeroshot
[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T06:57:08+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
1000\_STEPS\_5e7 ================ This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.6624 * Rewards/chosen: -0.0929 * Rewards/rejected: -0.1682 * Rewards/accuracies: 0.5451 * Rewards/margins: 0.0754 * Logps/rejected: -16.8218 * Logps/chosen: -15.0454 * Logits/rejected: -0.1317 * Logits/chosen: -0.1315 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-07 * train\_batch\_size: 4 * eval\_batch\_size: 1 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 8 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 100 * training\_steps: 1200 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.0.0+cu117 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1200", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #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: 5e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1200", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 78, 145, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #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: 5e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1200### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\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. --> # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6585 - Accuracy: 0.0973 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 29 | 2.6509 | 0.0796 | | No log | 2.0 | 58 | 2.6585 | 0.0973 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["minds14"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "audio_classification", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "minds14", "type": "minds14", "config": "en-US", "split": "train", "args": "en-US"}, "metrics": [{"type": "accuracy", "value": 0.09734513274336283, "name": "Accuracy"}]}]}]}
audio-classification
rendy-k/audio_classification
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
2024-02-08T06:57:41+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us
audio\_classification ===================== This model is a fine-tuned version of facebook/wav2vec2-base on the minds14 dataset. It achieves the following results on the evaluation set: * Loss: 2.6585 * Accuracy: 0.0973 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: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 77, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
# MiquMaid-v2-70B-DPO 3.5bpw ## Description Exllama quant of [NeverSleep/MiquMaid-v2-70B-DPO](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) ## Other quants: EXL2: [4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3.5bpw-exl2), [3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3bpw-exl2), [2.4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.4bpw-exl2), [2.3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.3bpw-exl2) 2.4bpw is probably the most you can fit in a 24gb card GGUF: [2bit Imatrix GGUF](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-Imatrix-GGUF) ## Prompt format: Alpaca ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}
text-generation
Kooten/MiquMaid-v2-70B-DPO-3.5bpw-exl2
[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T06:59:42+00:00
[]
[]
TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# MiquMaid-v2-70B-DPO 3.5bpw ## Description Exllama quant of NeverSleep/MiquMaid-v2-70B-DPO ## Other quants: EXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw 2.4bpw is probably the most you can fit in a 24gb card GGUF: 2bit Imatrix GGUF ## Prompt format: Alpaca ## Contact Kooten on discord URL
[ "# MiquMaid-v2-70B-DPO 3.5bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MiquMaid-v2-70B-DPO 3.5bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ 74, 17, 24, 60, 8, 7 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MiquMaid-v2-70B-DPO 3.5bpw## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL" ]
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<!-- 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. --> # outputs This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - 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 - lr_scheduler_warmup_steps: 100 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "tiiuae/falcon-7b-instruct", "model-index": [{"name": "outputs", "results": []}]}
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madhiarasan/outputs
[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:tiiuae/falcon-7b-instruct", "license:apache-2.0", "region:us" ]
2024-02-08T07:00:02+00:00
[]
[]
TAGS #tensorboard #safetensors #generated_from_trainer #base_model-tiiuae/falcon-7b-instruct #license-apache-2.0 #region-us
# outputs This model is a fine-tuned version of tiiuae/falcon-7b-instruct on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - 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 - lr_scheduler_warmup_steps: 100 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# outputs\n\nThis model is a fine-tuned version of tiiuae/falcon-7b-instruct on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\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- lr_scheduler_warmup_steps: 100\n- training_steps: 200\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-tiiuae/falcon-7b-instruct #license-apache-2.0 #region-us \n", "# outputs\n\nThis model is a fine-tuned version of tiiuae/falcon-7b-instruct on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\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- lr_scheduler_warmup_steps: 100\n- training_steps: 200\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 46, 33, 6, 12, 8, 3, 139, 4, 33 ]
[ "passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-tiiuae/falcon-7b-instruct #license-apache-2.0 #region-us \n# outputs\n\nThis model is a fine-tuned version of tiiuae/falcon-7b-instruct on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\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- lr_scheduler_warmup_steps: 100\n- training_steps: 200\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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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. --> # phi-1_5-query_classify This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-1_5", "model-index": [{"name": "phi-1_5-query_classify", "results": []}]}
null
akashAD/phi-1_5-query_classify
[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-1_5", "license:mit", "region:us" ]
2024-02-08T07:00:07+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #generated_from_trainer #base_model-microsoft/phi-1_5 #license-mit #region-us
# phi-1_5-query_classify This model is a fine-tuned version of microsoft/phi-1_5 on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# phi-1_5-query_classify\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-microsoft/phi-1_5 #license-mit #region-us \n", "# phi-1_5-query_classify\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 41, 35, 6, 12, 8, 3, 89, 4, 44 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-microsoft/phi-1_5 #license-mit #region-us \n# phi-1_5-query_classify\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
# MiquMaid-v2-70B-DPO 3bpw ## Description Exllama quant of [NeverSleep/MiquMaid-v2-70B-DPO](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) ## Other quants: EXL2: [4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3.5bpw-exl2), [3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3bpw-exl2), [2.4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.4bpw-exl2), [2.3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.3bpw-exl2) 2.4bpw is probably the most you can fit in a 24gb card GGUF: [2bit Imatrix GGUF](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-Imatrix-GGUF) ## Prompt format: Alpaca ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}
text-generation
Kooten/MiquMaid-v2-70B-DPO-3bpw-exl2
[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T07:00:23+00:00
[]
[]
TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# MiquMaid-v2-70B-DPO 3bpw ## Description Exllama quant of NeverSleep/MiquMaid-v2-70B-DPO ## Other quants: EXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw 2.4bpw is probably the most you can fit in a 24gb card GGUF: 2bit Imatrix GGUF ## Prompt format: Alpaca ## Contact Kooten on discord URL
[ "# MiquMaid-v2-70B-DPO 3bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MiquMaid-v2-70B-DPO 3bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ 74, 17, 24, 60, 8, 7 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MiquMaid-v2-70B-DPO 3bpw## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL" ]
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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. --> # tamil-llama-7b-instruct-quantized-ASR-output-fine-tuning This model is a fine-tuned version of [abhinand/tamil-llama-7b-instruct-v0.1](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.3527 ## 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: 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: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 1500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4659 | 2.58 | 500 | 4.2383 | | 1.9248 | 5.17 | 1000 | 4.6944 | | 1.4112 | 7.75 | 1500 | 5.3527 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1
{"language": ["ta"], "license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "Tamil-ASR, ASR-fine-tuning, Tamil-llama", "generated_from_trainer"], "base_model": "abhinand/tamil-llama-7b-instruct-v0.1", "model-index": [{"name": "tamil-llama-7b-instruct-quantized-ASR-output-fine-tuning", "results": []}]}
null
sujith013/tamil-llama-7b-instruct-quantized-ASR-output-fine-tuning
[ "peft", "tensorboard", "safetensors", "trl", "sft", "Tamil-ASR, ASR-fine-tuning, Tamil-llama", "generated_from_trainer", "ta", "base_model:abhinand/tamil-llama-7b-instruct-v0.1", "license:llama2", "region:us" ]
2024-02-08T07:03:25+00:00
[]
[ "ta" ]
TAGS #peft #tensorboard #safetensors #trl #sft #Tamil-ASR, ASR-fine-tuning, Tamil-llama #generated_from_trainer #ta #base_model-abhinand/tamil-llama-7b-instruct-v0.1 #license-llama2 #region-us
tamil-llama-7b-instruct-quantized-ASR-output-fine-tuning ======================================================== This model is a fine-tuned version of abhinand/tamil-llama-7b-instruct-v0.1 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 5.3527 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: 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: constant * lr\_scheduler\_warmup\_ratio: 0.03 * training\_steps: 1500 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 1500", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #Tamil-ASR, ASR-fine-tuning, Tamil-llama #generated_from_trainer #ta #base_model-abhinand/tamil-llama-7b-instruct-v0.1 #license-llama2 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 1500", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 79, 143, 4, 36 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #Tamil-ASR, ASR-fine-tuning, Tamil-llama #generated_from_trainer #ta #base_model-abhinand/tamil-llama-7b-instruct-v0.1 #license-llama2 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 1500### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
# Kunocchini-7b - AWQ - Model creator: [Test157t](https://huggingface.co/Test157t) - Original model: [Kunocchini-7b](https://huggingface.co/Test157t/Kunocchini-7b) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/9obNSalcJqCilQwr_4ssM.jpeg)
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge", "alpaca", "mistral"], "model_name": "Kunocchini-7b", "base_model": ["SanjiWatsuki/Kunoichi-DPO-v2-7B", "Epiculous/Fett-uccine-7B"], "model_creator": "Test157t", "model_type": "mistral", "pipeline_tag": "text-generation", "prompt_template": "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", "quantized_by": "Suparious"}
text-generation
solidrust/Kunocchini-7b-AWQ
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "alpaca", "en", "base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B", "base_model:Epiculous/Fett-uccine-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T07:03:47+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #en #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
# Kunocchini-7b - AWQ - Model creator: Test157t - Original model: Kunocchini-7b !image/jpeg
[ "# Kunocchini-7b - AWQ\n\n- Model creator: Test157t\n- Original model: Kunocchini-7b\n\n!image/jpeg" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #en #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Kunocchini-7b - AWQ\n\n- Model creator: Test157t\n- Original model: Kunocchini-7b\n\n!image/jpeg" ]
[ 109, 31 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #en #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Kunocchini-7b - AWQ\n\n- Model creator: Test157t\n- Original model: Kunocchini-7b\n\n!image/jpeg" ]
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null
null
transformers
dreamsim! now in quasi-transformers quasi-diffusers form. this probably won't work for you! but if it works for what i'm experimenting with, i'll try to get it upstreamed.
{"license": "mit", "library_name": "transformers", "datasets": ["PerceptionEval/DreamSim"]}
null
neggles/dreamsim
[ "transformers", "dataset:PerceptionEval/DreamSim", "license:mit", "endpoints_compatible", "region:us" ]
2024-02-08T07:04:13+00:00
[]
[]
TAGS #transformers #dataset-PerceptionEval/DreamSim #license-mit #endpoints_compatible #region-us
dreamsim! now in quasi-transformers quasi-diffusers form. this probably won't work for you! but if it works for what i'm experimenting with, i'll try to get it upstreamed.
[]
[ "TAGS\n#transformers #dataset-PerceptionEval/DreamSim #license-mit #endpoints_compatible #region-us \n" ]
[ 33 ]
[ "passage: TAGS\n#transformers #dataset-PerceptionEval/DreamSim #license-mit #endpoints_compatible #region-us \n" ]
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# Lora of furina/フリーナ/芙宁娜 (Genshin Impact) ## What Is This? This is the LoRA model of waifu furina/フリーナ/芙宁娜 (Genshin Impact). ## How Is It Trained? * This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). * The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs). * The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest). * Dataset used for training is the `stage3-p480-800` in [CyberHarem/furina_genshin](https://huggingface.co/datasets/CyberHarem/furina_genshin), which contains 1295 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets. * Trained for 10000 steps, 40 checkpoints were saved and evaluated. * **Trigger word is `furina_genshin`.** * Pruned core tags for this waifu are `blue_eyes, blue_hair, bangs, white_hair, ahoge, hair_between_eyes, long_hair, multicolored_hair, hat, bow, streaked_hair, very_long_hair, breasts`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. ## How to Use It? ### If You Are Using A1111 WebUI v1.7+ **Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5500, you need to download [`5500/furina_genshin.pt`](https://huggingface.co/CyberHarem/furina_genshin/resolve/main/5500/furina_genshin.pt) as the embedding and [`5500/furina_genshin.safetensors`](https://huggingface.co/CyberHarem/furina_genshin/resolve/main/5500/furina_genshin.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters. ## Which Step Should I Use? We selected 5 good steps for you to choose. The best one is step 5500. 1760 images (1.93 GiB) were generated for auto-testing. ![Metrics Plot](metrics_plot.png) The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). Here are the preview of the recommended steps: | Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1_0 | pattern_1_1 | pattern_2 | pattern_3 | pattern_4_0 | pattern_4_1 | pattern_5 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 | |-------:|--------:|:----------|:-------------|:--------------|:----------|:---------------------------------------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:---------------------------------|:-------------------------------------|:---------------------------------|:-----------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------|:-----------------------------------|:-----------------------------------|:---------------------------------|:-------------------------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------|:---------------------------------------| | 5500 | 17 | **0.991** | **0.956** | 0.849 | **0.832** | [Download](https://huggingface.co/CyberHarem/furina_genshin/resolve/main/5500/furina_genshin.zip) | ![pattern_0_0](5500/previews/pattern_0_0.png) | ![pattern_0_1](5500/previews/pattern_0_1.png) | ![pattern_1_0](5500/previews/pattern_1_0.png) | ![pattern_1_1](5500/previews/pattern_1_1.png) | ![pattern_2](5500/previews/pattern_2.png) | ![pattern_3](5500/previews/pattern_3.png) | ![pattern_4_0](5500/previews/pattern_4_0.png) | ![pattern_4_1](5500/previews/pattern_4_1.png) | ![pattern_5](5500/previews/pattern_5.png) | ![portrait_0](5500/previews/portrait_0.png) | ![portrait_1](5500/previews/portrait_1.png) | ![portrait_2](5500/previews/portrait_2.png) | ![full_body_0](5500/previews/full_body_0.png) | ![full_body_1](5500/previews/full_body_1.png) | ![profile_0](5500/previews/profile_0.png) | ![profile_1](5500/previews/profile_1.png) | ![free_0](5500/previews/free_0.png) | ![free_1](5500/previews/free_1.png) | ![shorts](5500/previews/shorts.png) | ![maid_0](5500/previews/maid_0.png) | ![maid_1](5500/previews/maid_1.png) | ![miko](5500/previews/miko.png) | ![yukata](5500/previews/yukata.png) | ![suit](5500/previews/suit.png) | ![china](5500/previews/china.png) | ![bikini_0](5500/previews/bikini_0.png) | ![bikini_1](5500/previews/bikini_1.png) | ![bikini_2](5500/previews/bikini_2.png) | ![sit](5500/previews/sit.png) | ![squat](5500/previews/squat.png) | ![kneel](5500/previews/kneel.png) | ![jump](5500/previews/jump.png) | ![crossed_arms](5500/previews/crossed_arms.png) | ![angry](5500/previews/angry.png) | ![smile](5500/previews/smile.png) | ![cry](5500/previews/cry.png) | ![grin](5500/previews/grin.png) | ![n_lie_0](5500/previews/n_lie_0.png) | ![n_lie_1](5500/previews/n_lie_1.png) | ![n_stand_0](5500/previews/n_stand_0.png) | ![n_stand_1](5500/previews/n_stand_1.png) | ![n_stand_2](5500/previews/n_stand_2.png) | ![n_sex_0](5500/previews/n_sex_0.png) | ![n_sex_1](5500/previews/n_sex_1.png) | | 3250 | 11 | 0.990 | 0.947 | **0.850** | 0.824 | [Download](https://huggingface.co/CyberHarem/furina_genshin/resolve/main/3250/furina_genshin.zip) | ![pattern_0_0](3250/previews/pattern_0_0.png) | ![pattern_0_1](3250/previews/pattern_0_1.png) | ![pattern_1_0](3250/previews/pattern_1_0.png) | ![pattern_1_1](3250/previews/pattern_1_1.png) | ![pattern_2](3250/previews/pattern_2.png) | ![pattern_3](3250/previews/pattern_3.png) | ![pattern_4_0](3250/previews/pattern_4_0.png) | ![pattern_4_1](3250/previews/pattern_4_1.png) | ![pattern_5](3250/previews/pattern_5.png) | ![portrait_0](3250/previews/portrait_0.png) | ![portrait_1](3250/previews/portrait_1.png) | ![portrait_2](3250/previews/portrait_2.png) | ![full_body_0](3250/previews/full_body_0.png) | ![full_body_1](3250/previews/full_body_1.png) | ![profile_0](3250/previews/profile_0.png) | ![profile_1](3250/previews/profile_1.png) | ![free_0](3250/previews/free_0.png) | ![free_1](3250/previews/free_1.png) | ![shorts](3250/previews/shorts.png) | ![maid_0](3250/previews/maid_0.png) | ![maid_1](3250/previews/maid_1.png) | ![miko](3250/previews/miko.png) | ![yukata](3250/previews/yukata.png) | ![suit](3250/previews/suit.png) | ![china](3250/previews/china.png) | ![bikini_0](3250/previews/bikini_0.png) | ![bikini_1](3250/previews/bikini_1.png) | ![bikini_2](3250/previews/bikini_2.png) | ![sit](3250/previews/sit.png) | ![squat](3250/previews/squat.png) | ![kneel](3250/previews/kneel.png) | ![jump](3250/previews/jump.png) | ![crossed_arms](3250/previews/crossed_arms.png) | ![angry](3250/previews/angry.png) | ![smile](3250/previews/smile.png) | ![cry](3250/previews/cry.png) | ![grin](3250/previews/grin.png) | ![n_lie_0](3250/previews/n_lie_0.png) | ![n_lie_1](3250/previews/n_lie_1.png) | ![n_stand_0](3250/previews/n_stand_0.png) | ![n_stand_1](3250/previews/n_stand_1.png) | ![n_stand_2](3250/previews/n_stand_2.png) | ![n_sex_0](3250/previews/n_sex_0.png) | ![n_sex_1](3250/previews/n_sex_1.png) | | 750 | 3 | 0.988 | 0.948 | 0.850 | 0.814 | [Download](https://huggingface.co/CyberHarem/furina_genshin/resolve/main/750/furina_genshin.zip) | ![pattern_0_0](750/previews/pattern_0_0.png) | ![pattern_0_1](750/previews/pattern_0_1.png) | ![pattern_1_0](750/previews/pattern_1_0.png) | ![pattern_1_1](750/previews/pattern_1_1.png) | ![pattern_2](750/previews/pattern_2.png) | ![pattern_3](750/previews/pattern_3.png) | ![pattern_4_0](750/previews/pattern_4_0.png) | ![pattern_4_1](750/previews/pattern_4_1.png) | ![pattern_5](750/previews/pattern_5.png) | ![portrait_0](750/previews/portrait_0.png) | ![portrait_1](750/previews/portrait_1.png) | ![portrait_2](750/previews/portrait_2.png) | ![full_body_0](750/previews/full_body_0.png) | ![full_body_1](750/previews/full_body_1.png) | ![profile_0](750/previews/profile_0.png) | ![profile_1](750/previews/profile_1.png) | ![free_0](750/previews/free_0.png) | ![free_1](750/previews/free_1.png) | ![shorts](750/previews/shorts.png) | ![maid_0](750/previews/maid_0.png) | ![maid_1](750/previews/maid_1.png) | ![miko](750/previews/miko.png) | ![yukata](750/previews/yukata.png) | ![suit](750/previews/suit.png) | ![china](750/previews/china.png) | ![bikini_0](750/previews/bikini_0.png) | ![bikini_1](750/previews/bikini_1.png) | ![bikini_2](750/previews/bikini_2.png) | ![sit](750/previews/sit.png) | ![squat](750/previews/squat.png) | ![kneel](750/previews/kneel.png) | ![jump](750/previews/jump.png) | ![crossed_arms](750/previews/crossed_arms.png) | ![angry](750/previews/angry.png) | ![smile](750/previews/smile.png) | ![cry](750/previews/cry.png) | ![grin](750/previews/grin.png) | ![n_lie_0](750/previews/n_lie_0.png) | ![n_lie_1](750/previews/n_lie_1.png) | ![n_stand_0](750/previews/n_stand_0.png) | ![n_stand_1](750/previews/n_stand_1.png) | ![n_stand_2](750/previews/n_stand_2.png) | ![n_sex_0](750/previews/n_sex_0.png) | ![n_sex_1](750/previews/n_sex_1.png) | | 10000 | 31 | 0.990 | 0.917 | 0.844 | 0.814 | [Download](https://huggingface.co/CyberHarem/furina_genshin/resolve/main/10000/furina_genshin.zip) | ![pattern_0_0](10000/previews/pattern_0_0.png) | ![pattern_0_1](10000/previews/pattern_0_1.png) | ![pattern_1_0](10000/previews/pattern_1_0.png) | ![pattern_1_1](10000/previews/pattern_1_1.png) | ![pattern_2](10000/previews/pattern_2.png) | ![pattern_3](10000/previews/pattern_3.png) | ![pattern_4_0](10000/previews/pattern_4_0.png) | ![pattern_4_1](10000/previews/pattern_4_1.png) | ![pattern_5](10000/previews/pattern_5.png) | ![portrait_0](10000/previews/portrait_0.png) | ![portrait_1](10000/previews/portrait_1.png) | ![portrait_2](10000/previews/portrait_2.png) | ![full_body_0](10000/previews/full_body_0.png) | ![full_body_1](10000/previews/full_body_1.png) | ![profile_0](10000/previews/profile_0.png) | ![profile_1](10000/previews/profile_1.png) | ![free_0](10000/previews/free_0.png) | ![free_1](10000/previews/free_1.png) | ![shorts](10000/previews/shorts.png) | ![maid_0](10000/previews/maid_0.png) | ![maid_1](10000/previews/maid_1.png) | ![miko](10000/previews/miko.png) | ![yukata](10000/previews/yukata.png) | ![suit](10000/previews/suit.png) | ![china](10000/previews/china.png) | ![bikini_0](10000/previews/bikini_0.png) | ![bikini_1](10000/previews/bikini_1.png) | ![bikini_2](10000/previews/bikini_2.png) | ![sit](10000/previews/sit.png) | ![squat](10000/previews/squat.png) | ![kneel](10000/previews/kneel.png) | ![jump](10000/previews/jump.png) | ![crossed_arms](10000/previews/crossed_arms.png) | ![angry](10000/previews/angry.png) | ![smile](10000/previews/smile.png) | ![cry](10000/previews/cry.png) | ![grin](10000/previews/grin.png) | ![n_lie_0](10000/previews/n_lie_0.png) | ![n_lie_1](10000/previews/n_lie_1.png) | ![n_stand_0](10000/previews/n_stand_0.png) | ![n_stand_1](10000/previews/n_stand_1.png) | ![n_stand_2](10000/previews/n_stand_2.png) | ![n_sex_0](10000/previews/n_sex_0.png) | ![n_sex_1](10000/previews/n_sex_1.png) | | 4750 | 15 | 0.988 | 0.932 | 0.847 | 0.812 | [Download](https://huggingface.co/CyberHarem/furina_genshin/resolve/main/4750/furina_genshin.zip) | ![pattern_0_0](4750/previews/pattern_0_0.png) | ![pattern_0_1](4750/previews/pattern_0_1.png) | ![pattern_1_0](4750/previews/pattern_1_0.png) | ![pattern_1_1](4750/previews/pattern_1_1.png) | ![pattern_2](4750/previews/pattern_2.png) | ![pattern_3](4750/previews/pattern_3.png) | ![pattern_4_0](4750/previews/pattern_4_0.png) | ![pattern_4_1](4750/previews/pattern_4_1.png) | ![pattern_5](4750/previews/pattern_5.png) | ![portrait_0](4750/previews/portrait_0.png) | ![portrait_1](4750/previews/portrait_1.png) | ![portrait_2](4750/previews/portrait_2.png) | ![full_body_0](4750/previews/full_body_0.png) | ![full_body_1](4750/previews/full_body_1.png) | ![profile_0](4750/previews/profile_0.png) | ![profile_1](4750/previews/profile_1.png) | ![free_0](4750/previews/free_0.png) | ![free_1](4750/previews/free_1.png) | ![shorts](4750/previews/shorts.png) | ![maid_0](4750/previews/maid_0.png) | ![maid_1](4750/previews/maid_1.png) | ![miko](4750/previews/miko.png) | ![yukata](4750/previews/yukata.png) | ![suit](4750/previews/suit.png) | ![china](4750/previews/china.png) | ![bikini_0](4750/previews/bikini_0.png) | ![bikini_1](4750/previews/bikini_1.png) | ![bikini_2](4750/previews/bikini_2.png) | ![sit](4750/previews/sit.png) | ![squat](4750/previews/squat.png) | ![kneel](4750/previews/kneel.png) | ![jump](4750/previews/jump.png) | ![crossed_arms](4750/previews/crossed_arms.png) | ![angry](4750/previews/angry.png) | ![smile](4750/previews/smile.png) | ![cry](4750/previews/cry.png) | ![grin](4750/previews/grin.png) | ![n_lie_0](4750/previews/n_lie_0.png) | ![n_lie_1](4750/previews/n_lie_1.png) | ![n_stand_0](4750/previews/n_stand_0.png) | ![n_stand_1](4750/previews/n_stand_1.png) | ![n_stand_2](4750/previews/n_stand_2.png) | ![n_sex_0](4750/previews/n_sex_0.png) | ![n_sex_1](4750/previews/n_sex_1.png) | ## Anything Else? Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. ## All Steps We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * [Steps From 7750 to 10000](all/0.md) * [Steps From 5250 to 7500](all/1.md) * [Steps From 2750 to 5000](all/2.md) * [Steps From 250 to 2500](all/3.md)
{"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/furina_genshin"], "pipeline_tag": "text-to-image"}
text-to-image
CyberHarem/furina_genshin
[ "art", "not-for-all-audiences", "text-to-image", "dataset:CyberHarem/furina_genshin", "license:mit", "region:us" ]
2024-02-08T07:07:03+00:00
[]
[]
TAGS #art #not-for-all-audiences #text-to-image #dataset-CyberHarem/furina_genshin #license-mit #region-us
Lora of furina/フリーナ/芙宁娜 (Genshin Impact) ======================================== What Is This? ------------- This is the LoRA model of waifu furina/フリーナ/芙宁娜 (Genshin Impact). How Is It Trained? ------------------ * This model is trained with HCP-Diffusion. * The auto-training framework is maintained by DeepGHS Team. * The base model used for training is deepghs/animefull-latest. * Dataset used for training is the 'stage3-p480-800' in CyberHarem/furina\_genshin, which contains 1295 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets. * Trained for 10000 steps, 40 checkpoints were saved and evaluated. * Trigger word is 'furina\_genshin'. * Pruned core tags for this waifu are 'blue\_eyes, blue\_hair, bangs, white\_hair, ahoge, hair\_between\_eyes, long\_hair, multicolored\_hair, hat, bow, streaked\_hair, very\_long\_hair, breasts'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. How to Use It? -------------- ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5500, you need to download '5500/furina\_genshin.pt' as the embedding and '5500/furina\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters. Which Step Should I Use? ------------------------ We selected 5 good steps for you to choose. The best one is step 5500. 1760 images (1.93 GiB) were generated for auto-testing. !Metrics Plot The base model used for generating preview images is Meina/MeinaMix\_V11. Here are the preview of the recommended steps: Anything Else? -------------- Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. All Steps --------- We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * Steps From 7750 to 10000 * Steps From 5250 to 7500 * Steps From 2750 to 5000 * Steps From 250 to 2500
[ "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 5500, you need to download '5500/furina\\_genshin.pt' as the embedding and '5500/furina\\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 5500.\n\n\n1760 images (1.93 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500" ]
[ "TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/furina_genshin #license-mit #region-us \n", "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 5500, you need to download '5500/furina\\_genshin.pt' as the embedding and '5500/furina\\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 5500.\n\n\n1760 images (1.93 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500" ]
[ 43, 38, 470 ]
[ "passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/furina_genshin #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file." ]
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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-base-squad-model1 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) 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: 64 - eval_batch_size: 16 - seed: 59 - 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": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "t5-base", "model-index": [{"name": "t5-base-squad-model1", "results": []}]}
question-answering
varun-v-rao/t5-base-squad-model1
[ "transformers", "tensorboard", "safetensors", "t5", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:t5-base", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T07:07:29+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-base #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
# t5-base-squad-model1 This model is a fine-tuned version of t5-base 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: 64 - eval_batch_size: 16 - seed: 59 - 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
[ "# t5-base-squad-model1\n\nThis model is a fine-tuned version of t5-base 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: 64\n- eval_batch_size: 16\n- seed: 59\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 #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-base #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "# t5-base-squad-model1\n\nThis model is a fine-tuned version of t5-base 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: 64\n- eval_batch_size: 16\n- seed: 59\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" ]
[ 80, 30, 6, 12, 8, 3, 90, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-base #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n# t5-base-squad-model1\n\nThis model is a fine-tuned version of t5-base 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: 64\n- eval_batch_size: 16\n- seed: 59\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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setfit
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 30 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:----------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| | ls | <ul><li>'List all files and directories'</li><li>'Show files in the current directory'</li><li>'Display contents of the current directory'</li></ul> | | cd | <ul><li>'Change to the specified directory'</li><li>'Move to the home directory'</li><li>'Navigate to the specified directory path'</li></ul> | | mkdir docs | <ul><li>"Create a new directory named 'docs'"</li></ul> | | mkdir projects | <ul><li>"Make a directory named 'projects'"</li></ul> | | mkdir data | <ul><li>"Create a folder called 'data'"</li></ul> | | mkdir images | <ul><li>"Make a directory named 'images'"</li></ul> | | mkdir scripts | <ul><li>"Create a new folder named 'scripts'"</li></ul> | | rm example.txt | <ul><li>"Remove the file named 'example.txt'"</li></ul> | | rm temp.txt | <ul><li>"Delete the file called 'temp.txt'"</li></ul> | | rm file1 | <ul><li>"Remove the file named 'file1'"</li></ul> | | rm file2 | <ul><li>"Delete the file named 'file2'"</li></ul> | | rm backup.txt | <ul><li>"Remove the file named 'backup.txt'"</li></ul> | | cp file1 /destination | <ul><li>'Copy file1 to directory /destination'</li></ul> | | cp file2 /backup | <ul><li>'Duplicate file2 to directory /backup'</li></ul> | | cp file3 /archive | <ul><li>'Copy file3 to folder /archive'</li></ul> | | cp file4 /temp | <ul><li>'Duplicate file4 to folder /temp'</li></ul> | | cp file5 /images | <ul><li>'Copy file5 to directory /images'</li></ul> | | mv file2 /new_location | <ul><li>'Move file2 to directory /new_location'</li></ul> | | mv file3 /backup | <ul><li>'Transfer file3 to directory /backup'</li></ul> | | mv file4 /archive | <ul><li>'Move file4 to folder /archive'</li></ul> | | mv file5 /temp | <ul><li>'Transfer file5 to folder /temp'</li></ul> | | mv file6 /images | <ul><li>'Move file6 to directory /images'</li></ul> | | cat README.md | <ul><li>"Display the contents of file 'README.md'"</li></ul> | | cat notes.txt | <ul><li>"Show the content of file 'notes.txt'"</li></ul> | | cat data.csv | <ul><li>"Print the contents of file 'data.csv'"</li></ul> | | cat script.sh | <ul><li>"Display the content of file 'script.sh'"</li></ul> | | cat config.ini | <ul><li>"Show the contents of file 'config.ini'"</li></ul> | | grep 'pattern' data.txt | <ul><li>"Search for 'pattern' in file 'data.txt'"</li></ul> | | grep 'word' text.txt | <ul><li>"Find occurrences of 'word' in file 'text.txt'"</li></ul> | | grep 'keyword' document.txt | <ul><li>"Search for 'keyword' in file 'document.txt'"</li></ul> | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.0 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("souvenger/NLP2Linux") # Run inference preds = model("Install package 'vim' as superuser") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 5 | 5.6667 | 9 | | Label | Training Sample Count | |:----------------------------|:----------------------| | cat README.md | 1 | | cat config.ini | 1 | | cat data.csv | 1 | | cat notes.txt | 1 | | cat script.sh | 1 | | cd | 10 | | cp file1 /destination | 1 | | cp file2 /backup | 1 | | cp file3 /archive | 1 | | cp file4 /temp | 1 | | cp file5 /images | 1 | | grep 'keyword' document.txt | 1 | | grep 'pattern' data.txt | 1 | | grep 'word' text.txt | 1 | | ls | 10 | | mkdir data | 1 | | mkdir docs | 1 | | mkdir images | 1 | | mkdir projects | 1 | | mkdir scripts | 1 | | mv file2 /new_location | 1 | | mv file3 /backup | 1 | | mv file4 /archive | 1 | | mv file5 /temp | 1 | | mv file6 /images | 1 | | rm backup.txt | 1 | | rm example.txt | 1 | | rm file1 | 1 | | rm file2 | 1 | | rm temp.txt | 1 | ### Training Hyperparameters - batch_size: (8, 8) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0042 | 1 | 0.1215 | - | | 0.2083 | 50 | 0.0232 | - | | 0.4167 | 100 | 0.01 | - | | 0.625 | 150 | 0.0044 | - | | 0.8333 | 200 | 0.0025 | - | ### Framework Versions - Python: 3.10.13 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.37.0 - PyTorch: 2.1.2 - Datasets: 2.1.0 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "Upgrade all installed packages with superuser privileges"}, {"text": "Install package 'vim' as superuser"}, {"text": "Remove package 'firefox' with superuser privileges"}, {"text": "Change permissions of directory 'docs' to writable"}, {"text": "Update package lists using superuser privileges"}], "pipeline_tag": "text-classification", "inference": true, "base_model": "sentence-transformers/paraphrase-mpnet-base-v2", "model-index": [{"name": "SetFit with sentence-transformers/paraphrase-mpnet-base-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.0, "name": "Accuracy"}]}]}]}
text-classification
souvenger/NLP2Linux
[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]
2024-02-08T07:09:07+00:00
[ "2209.11055" ]
[]
TAGS #setfit #safetensors #mpnet #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #model-index #region-us
SetFit with sentence-transformers/paraphrase-mpnet-base-v2 ========================================================== This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a Sentence Transformer with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. Model Details ------------- ### Model Description * Model Type: SetFit * Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2 * Classification head: a LogisticRegression instance * Maximum Sequence Length: 512 tokens * Number of Classes: 30 classes ### Model Sources * Repository: SetFit on GitHub * Paper: Efficient Few-Shot Learning Without Prompts * Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts ### Model Labels Evaluation ---------- ### Metrics Uses ---- ### Direct Use for Inference First install the SetFit library: Then you can load this model and run inference. Training Details ---------------- ### Training Set Metrics ### Training Hyperparameters * batch\_size: (8, 8) * num\_epochs: (1, 1) * max\_steps: -1 * sampling\_strategy: oversampling * num\_iterations: 20 * body\_learning\_rate: (2e-05, 2e-05) * head\_learning\_rate: 2e-05 * loss: CosineSimilarityLoss * distance\_metric: cosine\_distance * margin: 0.25 * end\_to\_end: False * use\_amp: False * warmup\_proportion: 0.1 * seed: 42 * eval\_max\_steps: -1 * load\_best\_model\_at\_end: False ### Training Results ### Framework Versions * Python: 3.10.13 * SetFit: 1.0.3 * Sentence Transformers: 2.3.1 * Transformers: 4.37.0 * PyTorch: 2.1.2 * Datasets: 2.1.0 * Tokenizers: 0.15.1 ### BibTeX
[ "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 30 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (8, 8)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 20\n* body\\_learning\\_rate: (2e-05, 2e-05)\n* head\\_learning\\_rate: 2e-05\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.37.0\n* PyTorch: 2.1.2\n* Datasets: 2.1.0\n* Tokenizers: 0.15.1", "### BibTeX" ]
[ "TAGS\n#setfit #safetensors #mpnet #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #model-index #region-us \n", "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 30 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (8, 8)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 20\n* body\\_learning\\_rate: (2e-05, 2e-05)\n* head\\_learning\\_rate: 2e-05\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.37.0\n* PyTorch: 2.1.2\n* Datasets: 2.1.0\n* Tokenizers: 0.15.1", "### BibTeX" ]
[ 72, 64, 52, 8, 8, 31, 7, 176, 4, 55, 6 ]
[ "passage: TAGS\n#setfit #safetensors #mpnet #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #model-index #region-us \n### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 30 classes### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts### Model Labels\n\n\n\nEvaluation\n----------### Metrics\n\n\n\nUses\n----### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------### Training Set Metrics### Training Hyperparameters\n\n\n* batch\\_size: (8, 8)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 20\n* body\\_learning\\_rate: (2e-05, 2e-05)\n* head\\_learning\\_rate: 2e-05\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False### Training Results### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.37.0\n* PyTorch: 2.1.2\n* Datasets: 2.1.0\n* Tokenizers: 0.15.1### BibTeX" ]
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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
humung/polyglot-ko-12.8b-vlending-v0.5
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T07:09:21+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
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1874 - Accuracy: 0.9517 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 52 | 0.2941 | 0.9227 | | No log | 2.0 | 104 | 0.2064 | 0.9517 | | No log | 3.0 | 156 | 0.2221 | 0.9372 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "image_classification", "results": []}]}
image-classification
rendy-k/image_classification
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T07:15:47+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
image\_classification ===================== This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.1874 * Accuracy: 0.9517 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 75, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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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 | | :-- | :-- | | 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 | 0.0010000000474974513 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
{"library_name": "keras", "tags": ["binary-classification", "keras", "tensorflow"]}
null
shuvom/churn-cl-v1
[ "keras", "binary-classification", "tensorflow", "region:us" ]
2024-02-08T07:16:03+00:00
[]
[]
TAGS #keras #binary-classification #tensorflow #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: Model Plot ---------- View Model Plot !Model Image
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ "TAGS\n#keras #binary-classification #tensorflow #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ 19, 28 ]
[ "passage: TAGS\n#keras #binary-classification #tensorflow #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
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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
ssaryssane/ssary-solar-10.7B
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T07:16:04+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
automatic-speech-recognition
SpideyDLK/wav2vec2-large-xls-r-300m-sinhala-test2
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T07:16:59+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" ]
[ 51, 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 #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
# MiquMaid-v2-70B 2.3bpw ## Description Exllama quant of [NeverSleep/MiquMaid-v2-70B](https://huggingface.co/NeverSleep/MiquMaid-v2-70B) ## Other quants: EXL2: [4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-3.5bpw-exl2), [3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-3bpw-exl2), [2.4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-2.4bpw-exl2), [2.3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-2.3bpw-exl2) 2.4bpw is probably the most you can fit in a 24gb card GGUF: [2bit Imatrix GGUF](https://huggingface.co/Kooten/MiquMaid-v2-70B-Imatrix-GGUF) ## Prompt format: Alpaca ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}
text-generation
Kooten/MiquMaid-v2-70B-2.3bpw-exl2
[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T07:19:49+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# MiquMaid-v2-70B 2.3bpw ## Description Exllama quant of NeverSleep/MiquMaid-v2-70B ## Other quants: EXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw 2.4bpw is probably the most you can fit in a 24gb card GGUF: 2bit Imatrix GGUF ## Prompt format: Alpaca ## Contact Kooten on discord URL
[ "# MiquMaid-v2-70B 2.3bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MiquMaid-v2-70B 2.3bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ 75, 14, 21, 60, 8, 7 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MiquMaid-v2-70B 2.3bpw## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL" ]
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null
null
transformers
# MiquMaid-v2-70B-DPO 2.3bpw ## Description Exllama quant of [NeverSleep/MiquMaid-v2-70B-DPO](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) ## Other quants: EXL2: [4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3.5bpw-exl2), [3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3bpw-exl2), [2.4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.4bpw-exl2), [2.3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.3bpw-exl2) 2.4bpw is probably the most you can fit in a 24gb card GGUF: [2bit Imatrix GGUF](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-Imatrix-GGUF) ## Prompt format: Alpaca ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}
text-generation
Kooten/MiquMaid-v2-70B-DPO-2.3bpw-exl2
[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T07:21:08+00:00
[]
[]
TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# MiquMaid-v2-70B-DPO 2.3bpw ## Description Exllama quant of NeverSleep/MiquMaid-v2-70B-DPO ## Other quants: EXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw 2.4bpw is probably the most you can fit in a 24gb card GGUF: 2bit Imatrix GGUF ## Prompt format: Alpaca ## Contact Kooten on discord URL
[ "# MiquMaid-v2-70B-DPO 2.3bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MiquMaid-v2-70B-DPO 2.3bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ 74, 17, 24, 60, 8, 7 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MiquMaid-v2-70B-DPO 2.3bpw## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL" ]
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null
null
transformers
# MiquMaid-v2-70B-DPO 4bpw ## Description Exllama quant of [NeverSleep/MiquMaid-v2-70B-DPO](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) ## Other quants: EXL2: [4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3.5bpw-exl2), [3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-3bpw-exl2), [2.4bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.4bpw-exl2), [2.3bpw](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-2.3bpw-exl2) 2.4bpw is probably the most you can fit in a 24gb card GGUF: [2bit Imatrix GGUF](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-Imatrix-GGUF) ## Prompt format: Alpaca ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}
text-generation
Kooten/MiquMaid-v2-70B-DPO-4bpw-exl2
[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T07:30:38+00:00
[]
[]
TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# MiquMaid-v2-70B-DPO 4bpw ## Description Exllama quant of NeverSleep/MiquMaid-v2-70B-DPO ## Other quants: EXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw 2.4bpw is probably the most you can fit in a 24gb card GGUF: 2bit Imatrix GGUF ## Prompt format: Alpaca ## Contact Kooten on discord URL
[ "# MiquMaid-v2-70B-DPO 4bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MiquMaid-v2-70B-DPO 4bpw", "## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO", "## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL" ]
[ 74, 17, 24, 60, 8, 7 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MiquMaid-v2-70B-DPO 4bpw## Description\nExllama quant of NeverSleep/MiquMaid-v2-70B-DPO## Other quants:\nEXL2: 4bpw, 3.5bpw, 3bpw, 2.4bpw, 2.3bpw\n\n2.4bpw is probably the most you can fit in a 24gb card\n\nGGUF:\n2bit Imatrix GGUF## Prompt format: Alpaca## Contact\nKooten on discord\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. --> # bert-finetuned-ner 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: - Loss: 0.0662 - Precision: 0.9272 - Recall: 0.9472 - F1: 0.9371 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0811 | 1.0 | 1756 | 0.0764 | 0.9101 | 0.9335 | 0.9217 | 0.9809 | | 0.0408 | 2.0 | 3512 | 0.0595 | 0.9268 | 0.9465 | 0.9366 | 0.9852 | | 0.0231 | 3.0 | 5268 | 0.0662 | 0.9272 | 0.9472 | 0.9371 | 0.9850 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+rocm5.7 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": []}]}
token-classification
Ankush-Chander/bert-finetuned-ner
[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T07:32:02+00:00
[]
[]
TAGS #transformers #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
bert-finetuned-ner ================== This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0662 * Precision: 0.9272 * Recall: 0.9472 * F1: 0.9371 * Accuracy: 0.9850 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0+rocm5.7 * 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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+rocm5.7\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+rocm5.7\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 64, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+rocm5.7\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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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 0.8.2
{"library_name": "peft", "base_model": "models/mixtral-lora-merged-3675"}
null
atom-team/mixtral
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:models/mixtral-lora-merged-3675", "region:us" ]
2024-02-08T07:33:31+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-models/mixtral-lora-merged-3675 #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-models/mixtral-lora-merged-3675 #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" ]
[ 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 #safetensors #arxiv-1910.09700 #base_model-models/mixtral-lora-merged-3675 #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
transformers
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{"library_name": "transformers", "tags": []}
text-classification
trtd56/practical_nlp_course_3
[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T07:35:19+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #distilbert #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 #distilbert #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 #distilbert #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
<!-- 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. --> # RewardModel_RobertaBase This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5009 - F1: 0.7738 - Roc Auc: 0.7738 - Accuracy: 0.7698 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 1.0 | 93 | 0.5440 | 0.7331 | 0.7341 | 0.7302 | | 0.648 | 2.0 | 186 | 0.5009 | 0.7738 | 0.7738 | 0.7698 | | 0.5515 | 3.0 | 279 | 0.4938 | 0.7545 | 0.7560 | 0.75 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "base_model": "roberta-base", "model-index": [{"name": "RewardModel_RobertaBase", "results": []}]}
text-classification
RajuEEE/RewardModel_RobertaBase
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T07:40:00+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
RewardModel\_RobertaBase ======================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.5009 * F1: 0.7738 * Roc Auc: 0.7738 * Accuracy: 0.7698 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: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 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: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #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: 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: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 63, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #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: 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: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
# WestLake 7B v2 laser - AWQ - Model creator: [Common Sense](https://huggingface.co/senseable) - Original model: [WestLake 7B v2](https://huggingface.co/senseable/WestLake-7B-v2) - Fine Tuning: [cognitivecomputations](https://huggingface.co/cognitivecomputations/WestLake-7B-v2-laser) It follows the implementation of [laserRMT](https://github.com/cognitivecomputations/laserRMT) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ffb10eeafbd678d4b3fe/jnqnl8a_zYYMqJoBpX8yS.png) ## Model description This repo contains AWQ model files for [Common Sense's WestLake 7B v2](https://huggingface.co/senseable/WestLake-7B-v2). These files were quantised using hardware kindly provided by [SolidRusT Networks](https://solidrust.net/). ## How to use ### Install the necessary packages ```bash pip install --upgrade autoawq autoawq-kernels ``` ### Example Python code ```bash from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer quant_path = "/srv/home/shaun/repos/samantha-1.1-westlake-7b-laser-AWQ" # Load model model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """\ <|system|> </s> <|user|> {prompt}</s> <|assistant|>""" prompt = "You're standing on the surface of the Earth. "\ "You walk one mile south, one mile west and one mile north. "\ "You end up exactly where you started. Where are you?" tokens = tokenizer(prompt_template.format(prompt=prompt), return_tensors='pt').input_ids.cuda() # Generate output generation_output = model.generate(tokens, streamer=streamer, max_new_tokens=512) ``` ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code ## Prompt template: ChatML ```plaintext <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` Also working with Basic Mistral format: ```plaintext <|system|> </s> <|user|> {prompt}</s> <|assistant|> ```
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned", "quantized", "4-bit", "AWQ", "transformers", "pytorch", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us"], "model_name": "WestLake 7B v2", "base_model": "senseable/WestLake-7B-v2", "model_creator": "Common Sense", "model_type": "mistral", "pipeline_tag": "text-generation", "prompt_template": "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", "quantized_by": "Suparious"}
text-generation
solidrust/WestLake-7B-v2-laser-AWQ
[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "quantized", "4-bit", "AWQ", "pytorch", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "en", "base_model:senseable/WestLake-7B-v2" ]
2024-02-08T07:40:37+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #mistral #text-generation #finetuned #quantized #4-bit #AWQ #pytorch #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #en #base_model-senseable/WestLake-7B-v2
# WestLake 7B v2 laser - AWQ - Model creator: Common Sense - Original model: WestLake 7B v2 - Fine Tuning: cognitivecomputations It follows the implementation of laserRMT !image/png ## Model description This repo contains AWQ model files for Common Sense's WestLake 7B v2. These files were quantised using hardware kindly provided by SolidRusT Networks. ## How to use ### Install the necessary packages ### Example Python code ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - Text Generation Webui - using Loader: AutoAWQ - vLLM - version 0.2.2 or later for support for all model types. - Hugging Face Text Generation Inference (TGI) - Transformers version 4.35.0 and later, from any code or client that supports Transformers - AutoAWQ - for use from Python code ## Prompt template: ChatML Also working with Basic Mistral format:
[ "# WestLake 7B v2 laser - AWQ\n\n- Model creator: Common Sense\n- Original model: WestLake 7B v2\n- Fine Tuning: cognitivecomputations\n\nIt follows the implementation of laserRMT\n\n!image/png", "## Model description\n\nThis repo contains AWQ model files for Common Sense's WestLake 7B v2.\n\nThese files were quantised using hardware kindly provided by SolidRusT Networks.", "## How to use", "### Install the necessary packages", "### Example Python code", "### About AWQ\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\nAWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.\n\nIt is supported by:\n\n- Text Generation Webui - using Loader: AutoAWQ\n- vLLM - version 0.2.2 or later for support for all model types.\n- Hugging Face Text Generation Inference (TGI)\n- Transformers version 4.35.0 and later, from any code or client that supports Transformers\n- AutoAWQ - for use from Python code", "## Prompt template: ChatML\n\n\n\nAlso working with Basic Mistral format:" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #finetuned #quantized #4-bit #AWQ #pytorch #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #en #base_model-senseable/WestLake-7B-v2 \n", "# WestLake 7B v2 laser - AWQ\n\n- Model creator: Common Sense\n- Original model: WestLake 7B v2\n- Fine Tuning: cognitivecomputations\n\nIt follows the implementation of laserRMT\n\n!image/png", "## Model description\n\nThis repo contains AWQ model files for Common Sense's WestLake 7B v2.\n\nThese files were quantised using hardware kindly provided by SolidRusT Networks.", "## How to use", "### Install the necessary packages", "### Example Python code", "### About AWQ\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\nAWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.\n\nIt is supported by:\n\n- Text Generation Webui - using Loader: AutoAWQ\n- vLLM - version 0.2.2 or later for support for all model types.\n- Hugging Face Text Generation Inference (TGI)\n- Transformers version 4.35.0 and later, from any code or client that supports Transformers\n- AutoAWQ - for use from Python code", "## Prompt template: ChatML\n\n\n\nAlso working with Basic Mistral format:" ]
[ 94, 51, 40, 4, 7, 6, 180, 16 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #finetuned #quantized #4-bit #AWQ #pytorch #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #en #base_model-senseable/WestLake-7B-v2 \n# WestLake 7B v2 laser - AWQ\n\n- Model creator: Common Sense\n- Original model: WestLake 7B v2\n- Fine Tuning: cognitivecomputations\n\nIt follows the implementation of laserRMT\n\n!image/png## Model description\n\nThis repo contains AWQ model files for Common Sense's WestLake 7B v2.\n\nThese files were quantised using hardware kindly provided by SolidRusT Networks.## How to use### Install the necessary packages### Example Python code### About AWQ\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\nAWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.\n\nIt is supported by:\n\n- Text Generation Webui - using Loader: AutoAWQ\n- vLLM - version 0.2.2 or later for support for all model types.\n- Hugging Face Text Generation Inference (TGI)\n- Transformers version 4.35.0 and later, from any code or client that supports Transformers\n- AutoAWQ - for use from Python code## Prompt template: ChatML\n\n\n\nAlso working with Basic Mistral format:" ]
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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="Atozzio/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"], "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
Atozzio/q-FrozenLake-v1-4x4-noSlippery
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
2024-02-08T07:48:55+00:00
[]
[]
TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #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 #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" ]
[ 40, 39 ]
[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #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
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
akashAD/phi-1_5-finetuned-query-classify
[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T07:50:54+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #phi #text-generation #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 #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 #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
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. --> # output_llama2_instruct This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.6746 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 20 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9193 | 0.0 | 20 | 1.8124 | | 1.8847 | 0.01 | 40 | 1.7835 | | 1.813 | 0.01 | 60 | 1.7710 | | 1.9026 | 0.01 | 80 | 1.7605 | | 1.8352 | 0.01 | 100 | 1.7449 | | 1.7327 | 0.02 | 120 | 1.7100 | | 1.8625 | 0.02 | 140 | 1.7055 | | 1.9379 | 0.02 | 160 | 1.7008 | | 1.8597 | 0.02 | 180 | 1.6971 | | 1.8703 | 0.03 | 200 | 1.6943 | | 1.7749 | 0.03 | 220 | 1.6902 | | 1.7645 | 0.03 | 240 | 1.6898 | | 1.7894 | 0.04 | 260 | 1.6886 | | 1.8492 | 0.04 | 280 | 1.6870 | | 1.7331 | 0.04 | 300 | 1.6841 | | 1.7278 | 0.04 | 320 | 1.6830 | | 1.6963 | 0.05 | 340 | 1.6826 | | 1.8226 | 0.05 | 360 | 1.6813 | | 1.8246 | 0.05 | 380 | 1.6797 | | 1.8577 | 0.05 | 400 | 1.6778 | | 1.6691 | 0.06 | 420 | 1.6779 | | 1.7001 | 0.06 | 440 | 1.6774 | | 1.9008 | 0.06 | 460 | 1.6770 | | 1.7856 | 0.07 | 480 | 1.6733 | | 1.7674 | 0.07 | 500 | 1.6746 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "NousResearch/Llama-2-7b-hf", "model-index": [{"name": "output_llama2_instruct", "results": []}]}
null
lillybak/output_llama2_instruct
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:NousResearch/Llama-2-7b-hf", "region:us" ]
2024-02-08T07:51:11+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-NousResearch/Llama-2-7b-hf #region-us
output\_llama2\_instruct ======================== This model is a fine-tuned version of NousResearch/Llama-2-7b-hf on the generator dataset. It achieves the following results on the evaluation set: * Loss: 1.6746 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0002 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant * lr\_scheduler\_warmup\_steps: 20 * training\_steps: 500 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 20\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.0+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-NousResearch/Llama-2-7b-hf #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 20\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 55, 114, 4, 39 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-NousResearch/Llama-2-7b-hf #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 20\n* training\\_steps: 500### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
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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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: 152334H/miqu-1-70b-sf model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer is_llama_derived_model: true load_in_8bit: false load_in_4bit: true strict: false datasets: - path: teknium/OpenHermes-2.5 type: sharegpt conversation: chatml dataset_prepared_path: hermes-prepped val_set_size: 0 output_dir: ./qlora-hermes adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: huggingface wandb_entity: 152334h wandb_watch: wandb_name: hermes2-miqu wandb_log_model: gradient_accumulation_steps: 16 micro_batch_size: 2 num_epochs: 3 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.0001 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_sample_packing: false saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.05 fsdp: fsdp_config: save_safetensors: true resize_token_embeddings_to_32x: true lora_modules_to_save: - embed_tokens - lm_head special_tokens: eos_token: "<|im_end|>" tokens: - "<|im_start|>" - "<|im_end|>" ``` </details><br> # qlora-hermes This model is a fine-tuned version of [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) 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: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 16 - total_train_batch_size: 192 - total_eval_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
{"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "152334H/miqu-1-70b-sf", "model-index": [{"name": "qlora-hermes", "results": []}]}
null
152334H/miqu-1-70b-hermes2.5-qlora
[ "peft", "safetensors", "llama", "generated_from_trainer", "base_model:152334H/miqu-1-70b-sf", "4-bit", "region:us" ]
2024-02-08T07:53:23+00:00
[]
[]
TAGS #peft #safetensors #llama #generated_from_trainer #base_model-152334H/miqu-1-70b-sf #4-bit #region-us
<img src="URL alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary> axolotl version: '0.4.0' </details><br> # qlora-hermes This model is a fine-tuned version of 152334H/miqu-1-70b-sf 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: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 16 - total_train_batch_size: 192 - total_eval_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
[ "# qlora-hermes\n\nThis model is a fine-tuned version of 152334H/miqu-1-70b-sf 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: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 6\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 192\n- total_eval_batch_size: 12\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]
[ "TAGS\n#peft #safetensors #llama #generated_from_trainer #base_model-152334H/miqu-1-70b-sf #4-bit #region-us \n", "# qlora-hermes\n\nThis model is a fine-tuned version of 152334H/miqu-1-70b-sf 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: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 6\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 192\n- total_eval_batch_size: 12\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]
[ 44, 36, 6, 12, 8, 3, 157, 4, 44 ]
[ "passage: TAGS\n#peft #safetensors #llama #generated_from_trainer #base_model-152334H/miqu-1-70b-sf #4-bit #region-us \n# qlora-hermes\n\nThis model is a fine-tuned version of 152334H/miqu-1-70b-sf 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: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 6\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 192\n- total_eval_batch_size: 12\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 3### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]
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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
mertllc/mms-tts-tur-thirties-male
[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T07:57:19+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #vits #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 #vits #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 #vits #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
peft
见[nenekochan/Yi-6B-yoruno](https://huggingface.co/nenekochan/Yi-6B-yoruno) ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
{"library_name": "peft"}
null
nenekochan/Yi-6B-yoruno-peft
[ "peft", "safetensors", "region:us" ]
2024-02-08T07:58:00+00:00
[]
[]
TAGS #peft #safetensors #region-us
见nenekochan/Yi-6B-yoruno ## Training procedure The following 'bitsandbytes' quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
[ "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: QuantizationMethod.BITS_AND_BYTES\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: True\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\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: QuantizationMethod.BITS_AND_BYTES\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: True\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]
[ 14, 171, 11 ]
[ "passage: TAGS\n#peft #safetensors #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: QuantizationMethod.BITS_AND_BYTES\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: True\n- bnb_4bit_compute_dtype: float16### Framework versions\n\n\n- PEFT 0.4.0" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
YashRawal225/New-3-7b-chat-finetune-german500
[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T07:58:06+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
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
RajuEEE/LlaMa_FineTunedModel
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T08:00:43+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
# Darcy-7b - AWQ - Model creator: [gmonsoon](https://huggingface.co/gmonsoon) - Original model: [Darcy-7b](https://huggingface.co/gmonsoon/Darcy-7b) ## Model description Darcy-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing). - [macadeliccc/WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo) - [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2) - [FelixChao/Faraday-7B](https://huggingface.co/FelixChao/Faraday-7B) ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo", "FelixChao/WestSeverus-7B-DPO-v2", "FelixChao/Faraday-7B"], "model_name": "Darcy-7b", "base_model": ["macadeliccc/WestLake-7B-v2-laser-truthy-dpo", "FelixChao/WestSeverus-7B-DPO-v2", "FelixChao/Faraday-7B"], "pipeline_tag": "text-generation", "model_type": "mistral", "model_creator": "gmonsoon", "quantized_by": "Suparious"}
text-generation
solidrust/Darcy-7b-AWQ
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo", "FelixChao/WestSeverus-7B-DPO-v2", "FelixChao/Faraday-7B", "base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo", "base_model:FelixChao/WestSeverus-7B-DPO-v2", "base_model:FelixChao/Faraday-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T08:02:55+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #macadeliccc/WestLake-7B-v2-laser-truthy-dpo #FelixChao/WestSeverus-7B-DPO-v2 #FelixChao/Faraday-7B #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #base_model-FelixChao/WestSeverus-7B-DPO-v2 #base_model-FelixChao/Faraday-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
# Darcy-7b - AWQ - Model creator: gmonsoon - Original model: Darcy-7b ## Model description Darcy-7b is a merge of the following models using LazyMergekit. - macadeliccc/WestLake-7B-v2-laser-truthy-dpo - FelixChao/WestSeverus-7B-DPO-v2 - FelixChao/Faraday-7B ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - Text Generation Webui - using Loader: AutoAWQ - vLLM - version 0.2.2 or later for support for all model types. - Hugging Face Text Generation Inference (TGI) - Transformers version 4.35.0 and later, from any code or client that supports Transformers - AutoAWQ - for use from Python code
[ "# Darcy-7b - AWQ\n\n- Model creator: gmonsoon\n- Original model: Darcy-7b", "## Model description\n\nDarcy-7b is a merge of the following models using LazyMergekit.\n\n- macadeliccc/WestLake-7B-v2-laser-truthy-dpo\n- FelixChao/WestSeverus-7B-DPO-v2\n- FelixChao/Faraday-7B", "### About AWQ\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\nAWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.\n\nIt is supported by:\n\n- Text Generation Webui - using Loader: AutoAWQ\n- vLLM - version 0.2.2 or later for support for all model types.\n- Hugging Face Text Generation Inference (TGI)\n- Transformers version 4.35.0 and later, from any code or client that supports Transformers\n- AutoAWQ - for use from Python code" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #macadeliccc/WestLake-7B-v2-laser-truthy-dpo #FelixChao/WestSeverus-7B-DPO-v2 #FelixChao/Faraday-7B #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #base_model-FelixChao/WestSeverus-7B-DPO-v2 #base_model-FelixChao/Faraday-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Darcy-7b - AWQ\n\n- Model creator: gmonsoon\n- Original model: Darcy-7b", "## Model description\n\nDarcy-7b is a merge of the following models using LazyMergekit.\n\n- macadeliccc/WestLake-7B-v2-laser-truthy-dpo\n- FelixChao/WestSeverus-7B-DPO-v2\n- FelixChao/Faraday-7B", "### About AWQ\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\nAWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.\n\nIt is supported by:\n\n- Text Generation Webui - using Loader: AutoAWQ\n- vLLM - version 0.2.2 or later for support for all model types.\n- Hugging Face Text Generation Inference (TGI)\n- Transformers version 4.35.0 and later, from any code or client that supports Transformers\n- AutoAWQ - for use from Python code" ]
[ 185, 23, 70, 180 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #macadeliccc/WestLake-7B-v2-laser-truthy-dpo #FelixChao/WestSeverus-7B-DPO-v2 #FelixChao/Faraday-7B #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #base_model-FelixChao/WestSeverus-7B-DPO-v2 #base_model-FelixChao/Faraday-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Darcy-7b - AWQ\n\n- Model creator: gmonsoon\n- Original model: Darcy-7b## Model description\n\nDarcy-7b is a merge of the following models using LazyMergekit.\n\n- macadeliccc/WestLake-7B-v2-laser-truthy-dpo\n- FelixChao/WestSeverus-7B-DPO-v2\n- FelixChao/Faraday-7B### About AWQ\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\nAWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.\n\nIt is supported by:\n\n- Text Generation Webui - using Loader: AutoAWQ\n- vLLM - version 0.2.2 or later for support for all model types.\n- Hugging Face Text Generation Inference (TGI)\n- Transformers version 4.35.0 and later, from any code or client that supports Transformers\n- AutoAWQ - for use from Python code" ]
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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. --> # SMIDS_3x_beit_large_Adamax_lr001_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2566 - Accuracy: 0.8785 ## 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: 0.001 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4861 | 1.0 | 450 | 0.5758 | 0.7937 | | 0.471 | 2.0 | 900 | 0.4153 | 0.8286 | | 0.2546 | 3.0 | 1350 | 0.5147 | 0.8170 | | 0.438 | 4.0 | 1800 | 0.3988 | 0.8319 | | 0.5075 | 5.0 | 2250 | 0.3343 | 0.8669 | | 0.3039 | 6.0 | 2700 | 0.3548 | 0.8702 | | 0.3013 | 7.0 | 3150 | 0.3367 | 0.8636 | | 0.2694 | 8.0 | 3600 | 0.3849 | 0.8636 | | 0.2787 | 9.0 | 4050 | 0.4740 | 0.8436 | | 0.1686 | 10.0 | 4500 | 0.4075 | 0.8586 | | 0.1552 | 11.0 | 4950 | 0.5130 | 0.8569 | | 0.1072 | 12.0 | 5400 | 0.5022 | 0.8719 | | 0.0735 | 13.0 | 5850 | 0.5368 | 0.8702 | | 0.0949 | 14.0 | 6300 | 0.5410 | 0.8636 | | 0.0733 | 15.0 | 6750 | 0.8280 | 0.8669 | | 0.0079 | 16.0 | 7200 | 0.6797 | 0.8702 | | 0.1273 | 17.0 | 7650 | 0.7963 | 0.8686 | | 0.0013 | 18.0 | 8100 | 0.8007 | 0.8602 | | 0.0492 | 19.0 | 8550 | 0.5349 | 0.8752 | | 0.0161 | 20.0 | 9000 | 0.8632 | 0.8619 | | 0.0683 | 21.0 | 9450 | 0.6745 | 0.8719 | | 0.0031 | 22.0 | 9900 | 0.7968 | 0.8652 | | 0.0491 | 23.0 | 10350 | 0.7553 | 0.8669 | | 0.0163 | 24.0 | 10800 | 0.8260 | 0.8769 | | 0.0161 | 25.0 | 11250 | 0.8713 | 0.8652 | | 0.0231 | 26.0 | 11700 | 0.9006 | 0.8785 | | 0.0001 | 27.0 | 12150 | 0.7668 | 0.8835 | | 0.0005 | 28.0 | 12600 | 0.9973 | 0.8819 | | 0.0015 | 29.0 | 13050 | 0.8626 | 0.8952 | | 0.006 | 30.0 | 13500 | 0.8797 | 0.8902 | | 0.0008 | 31.0 | 13950 | 0.8543 | 0.8985 | | 0.0 | 32.0 | 14400 | 0.9436 | 0.8902 | | 0.0002 | 33.0 | 14850 | 0.8985 | 0.8918 | | 0.0001 | 34.0 | 15300 | 1.0603 | 0.8869 | | 0.0005 | 35.0 | 15750 | 1.1369 | 0.8852 | | 0.0 | 36.0 | 16200 | 1.0524 | 0.8852 | | 0.0001 | 37.0 | 16650 | 1.1134 | 0.8835 | | 0.0 | 38.0 | 17100 | 1.0243 | 0.8835 | | 0.0 | 39.0 | 17550 | 1.1383 | 0.8835 | | 0.0046 | 40.0 | 18000 | 1.2573 | 0.8802 | | 0.0 | 41.0 | 18450 | 1.0366 | 0.8852 | | 0.0 | 42.0 | 18900 | 1.1028 | 0.8802 | | 0.0 | 43.0 | 19350 | 1.1434 | 0.8802 | | 0.0 | 44.0 | 19800 | 1.2147 | 0.8769 | | 0.0 | 45.0 | 20250 | 1.2232 | 0.8802 | | 0.0 | 46.0 | 20700 | 1.2154 | 0.8819 | | 0.0 | 47.0 | 21150 | 1.2480 | 0.8785 | | 0.0049 | 48.0 | 21600 | 1.2687 | 0.8785 | | 0.0 | 49.0 | 22050 | 1.2538 | 0.8785 | | 0.0 | 50.0 | 22500 | 1.2566 | 0.8785 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_Adamax_lr001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8785357737104825, "name": "Accuracy"}]}]}]}
image-classification
onizukal/SMIDS_3x_beit_large_Adamax_lr001_fold2
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T08:03:09+00:00
[]
[]
TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
SMIDS\_3x\_beit\_large\_Adamax\_lr001\_fold2 ============================================ This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.2566 * Accuracy: 0.8785 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: 0.001 * 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 * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
[ 81, 115, 4, 30 ]
[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
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null
null
transformers
# Caution: This model may output adult content. ![](https://files.catbox.moe/gw9y0r.jpg) # FISH - Fusion of Intuitive Semantic Heuristics For this model I took the following LoRAs [SeanWu25/Mixtral_8x7b_Medicine](https://huggingface.co/SeanWu25/Mixtral_8x7b_Medicine) [SeanWu25/Mixtral_8x7b_WuKurtz](https://huggingface.co/SeanWu25/Mixtral_8x7b_WuKurtz) and [wandb/Mixtral-8x7b-Remixtral](https://huggingface.co/wandb/Mixtral-8x7b-Remixtral) And merged them onto their base model. I then did a simple linear merge between them, an experimental unreleased 8x7B model, and an unreleased model that was a intermediate step in creating [Envoid/BondBurger-8x7B](https://huggingface.co/Envoid/BondBurger-8x7B?not-for-all-audiences=true) The end results are a surprisingly good model for role palying style entertainment. At first I was disappointed with the results but ended up settling on the following sampler parameters which really bring it to life. ![](https://files.catbox.moe/apinh7.png) ## It does eccentuate characters that have particularly aggressive personalities. ## Because this is part of the whole SensualNousInstruct family of models it still suffers from the same tokenizer/special tokens weirdness. ## This model has only been tested in Q8 GGUF form due to hardware limitations. It responds well to [INST] do a thing [/INST] instruct style formatting (although uses the ChatML special tokens)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences"]}
text-generation
Envoid/Fish-8x7B
[ "transformers", "safetensors", "mixtral", "text-generation", "not-for-all-audiences", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T08:04:10+00:00
[]
[]
TAGS #transformers #safetensors #mixtral #text-generation #not-for-all-audiences #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Caution: This model may output adult content. ![](URL # FISH - Fusion of Intuitive Semantic Heuristics For this model I took the following LoRAs SeanWu25/Mixtral_8x7b_Medicine SeanWu25/Mixtral_8x7b_WuKurtz and wandb/Mixtral-8x7b-Remixtral And merged them onto their base model. I then did a simple linear merge between them, an experimental unreleased 8x7B model, and an unreleased model that was a intermediate step in creating Envoid/BondBurger-8x7B The end results are a surprisingly good model for role palying style entertainment. At first I was disappointed with the results but ended up settling on the following sampler parameters which really bring it to life. ![](URL ## It does eccentuate characters that have particularly aggressive personalities. ## Because this is part of the whole SensualNousInstruct family of models it still suffers from the same tokenizer/special tokens weirdness. ## This model has only been tested in Q8 GGUF form due to hardware limitations. It responds well to [INST] do a thing [/INST] instruct style formatting (although uses the ChatML special tokens)
[ "# Caution: This model may output adult content.\n\n\n![](URL", "# FISH - Fusion of Intuitive Semantic Heuristics \n\nFor this model I took the following LoRAs\n\nSeanWu25/Mixtral_8x7b_Medicine\n\nSeanWu25/Mixtral_8x7b_WuKurtz\n\nand\n\nwandb/Mixtral-8x7b-Remixtral\n\nAnd merged them onto their base model. \n\nI then did a simple linear merge between them, an experimental unreleased 8x7B model, and an unreleased model that was a intermediate step in creating Envoid/BondBurger-8x7B\n\nThe end results are a surprisingly good model for role palying style entertainment. \n\nAt first I was disappointed with the results but ended up settling on the following sampler parameters which really bring it to life. \n\n\n![](URL", "## It does eccentuate characters that have particularly aggressive personalities.", "## Because this is part of the whole SensualNousInstruct family of models it still suffers from the same tokenizer/special tokens weirdness.", "## This model has only been tested in Q8 GGUF form due to hardware limitations. \n\nIt responds well to [INST] do a thing [/INST] instruct style formatting (although uses the ChatML special tokens)" ]
[ "TAGS\n#transformers #safetensors #mixtral #text-generation #not-for-all-audiences #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Caution: This model may output adult content.\n\n\n![](URL", "# FISH - Fusion of Intuitive Semantic Heuristics \n\nFor this model I took the following LoRAs\n\nSeanWu25/Mixtral_8x7b_Medicine\n\nSeanWu25/Mixtral_8x7b_WuKurtz\n\nand\n\nwandb/Mixtral-8x7b-Remixtral\n\nAnd merged them onto their base model. \n\nI then did a simple linear merge between them, an experimental unreleased 8x7B model, and an unreleased model that was a intermediate step in creating Envoid/BondBurger-8x7B\n\nThe end results are a surprisingly good model for role palying style entertainment. \n\nAt first I was disappointed with the results but ended up settling on the following sampler parameters which really bring it to life. \n\n\n![](URL", "## It does eccentuate characters that have particularly aggressive personalities.", "## Because this is part of the whole SensualNousInstruct family of models it still suffers from the same tokenizer/special tokens weirdness.", "## This model has only been tested in Q8 GGUF form due to hardware limitations. \n\nIt responds well to [INST] do a thing [/INST] instruct style formatting (although uses the ChatML special tokens)" ]
[ 71, 16, 178, 15, 34, 55 ]
[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #not-for-all-audiences #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Caution: This model may output adult content.\n\n\n![](URL# FISH - Fusion of Intuitive Semantic Heuristics \n\nFor this model I took the following LoRAs\n\nSeanWu25/Mixtral_8x7b_Medicine\n\nSeanWu25/Mixtral_8x7b_WuKurtz\n\nand\n\nwandb/Mixtral-8x7b-Remixtral\n\nAnd merged them onto their base model. \n\nI then did a simple linear merge between them, an experimental unreleased 8x7B model, and an unreleased model that was a intermediate step in creating Envoid/BondBurger-8x7B\n\nThe end results are a surprisingly good model for role palying style entertainment. \n\nAt first I was disappointed with the results but ended up settling on the following sampler parameters which really bring it to life. \n\n\n![](URL## It does eccentuate characters that have particularly aggressive personalities.## Because this is part of the whole SensualNousInstruct family of models it still suffers from the same tokenizer/special tokens weirdness.## This model has only been tested in Q8 GGUF form due to hardware limitations. \n\nIt responds well to [INST] do a thing [/INST] instruct style formatting (although uses the ChatML special tokens)" ]
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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. --> # emotion_model_1 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5356 - Accuracy: 0.4437 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0785 | 1.0 | 10 | 2.0617 | 0.125 | | 2.0054 | 2.0 | 20 | 1.9826 | 0.275 | | 1.8694 | 3.0 | 30 | 1.8516 | 0.325 | | 1.7212 | 4.0 | 40 | 1.7082 | 0.3812 | | 1.6101 | 5.0 | 50 | 1.6297 | 0.4375 | | 1.5409 | 6.0 | 60 | 1.5981 | 0.4188 | | 1.4801 | 7.0 | 70 | 1.5526 | 0.4437 | | 1.433 | 8.0 | 80 | 1.5574 | 0.4813 | | 1.4056 | 9.0 | 90 | 1.5094 | 0.5062 | | 1.3797 | 10.0 | 100 | 1.5232 | 0.4688 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "emotion_model_1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.44375, "name": "Accuracy"}]}]}]}
image-classification
citradiani/emotion_model_1
[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T08:04:21+00:00
[]
[]
TAGS #transformers #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
emotion\_model\_1 ================= This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.5356 * Accuracy: 0.4437 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 64 * 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 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\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", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\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", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 82, 144, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\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### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
flair
## Polish Flair Model -- Part-of-Speech
{"language": "pl", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Jan Brzechwa - polski poeta i adwokat \u017cydowskiego pochodzenia, autor bajek i wierszy dla dzieci, satyrycznych tekst\u00f3w dla doros\u0142ych, a tak\u017ce t\u0142umacz literatury rosyjskiej."}]}
token-classification
clarin-knext/morpho-flair-pos
[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "pl", "region:us" ]
2024-02-08T08:08:06+00:00
[]
[ "pl" ]
TAGS #flair #pytorch #token-classification #sequence-tagger-model #pl #region-us
## Polish Flair Model -- Part-of-Speech
[ "## Polish Flair Model -- Part-of-Speech" ]
[ "TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #pl #region-us \n", "## Polish Flair Model -- Part-of-Speech" ]
[ 30, 14 ]
[ "passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #pl #region-us \n## Polish Flair Model -- Part-of-Speech" ]
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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. --> # hubert_3 This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0584 - Wer: 0.1836 - Cer: 0.0589 ## 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: 0.00027 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 11.7882 | 1.0 | 60 | 9.4723 | 0.9944 | 0.9975 | | 6.3705 | 2.0 | 120 | 5.9403 | 0.9944 | 0.9975 | | 4.7536 | 3.0 | 180 | 4.5186 | 0.9944 | 0.9975 | | 3.5501 | 4.0 | 240 | 3.3998 | 0.9944 | 0.9975 | | 2.9838 | 5.0 | 300 | 2.9474 | 0.9944 | 0.9975 | | 2.4703 | 6.0 | 360 | 2.2713 | 0.9944 | 0.9975 | | 1.6962 | 7.0 | 420 | 1.6251 | 1.0 | 0.7253 | | 1.228 | 8.0 | 480 | 1.1017 | 0.9547 | 0.5135 | | 0.8611 | 9.0 | 540 | 0.8005 | 0.7756 | 0.4432 | | 0.8303 | 10.0 | 600 | 0.7502 | 0.7823 | 0.4729 | | 0.8582 | 11.0 | 660 | 0.7064 | 0.7771 | 0.4860 | | 0.6971 | 12.0 | 720 | 0.6950 | 0.7878 | 0.4560 | | 0.6735 | 13.0 | 780 | 0.7334 | 0.7545 | 0.4045 | | 0.643 | 14.0 | 840 | 0.5926 | 0.7637 | 0.4227 | | 0.6702 | 15.0 | 900 | 0.5447 | 0.6832 | 0.3137 | | 0.909 | 16.0 | 960 | 0.5550 | 0.6955 | 0.3118 | | 0.5869 | 17.0 | 1020 | 0.5446 | 0.7760 | 0.3850 | | 1.2649 | 18.0 | 1080 | 0.4579 | 0.7066 | 0.3267 | | 0.475 | 19.0 | 1140 | 0.4564 | 0.6187 | 0.2544 | | 0.4629 | 20.0 | 1200 | 0.4068 | 0.6024 | 0.2213 | | 0.4432 | 21.0 | 1260 | 0.3811 | 0.5987 | 0.2510 | | 0.5371 | 22.0 | 1320 | 0.3753 | 0.5679 | 0.1950 | | 0.3914 | 23.0 | 1380 | 0.3413 | 0.5879 | 0.2578 | | 0.389 | 24.0 | 1440 | 0.3278 | 0.5130 | 0.1947 | | 0.349 | 25.0 | 1500 | 0.2986 | 0.4811 | 0.1626 | | 0.3343 | 26.0 | 1560 | 0.3187 | 0.4607 | 0.1637 | | 0.2964 | 27.0 | 1620 | 0.2471 | 0.4236 | 0.1679 | | 0.2935 | 28.0 | 1680 | 0.2539 | 0.4206 | 0.1476 | | 0.2287 | 29.0 | 1740 | 0.2014 | 0.3576 | 0.1343 | | 0.2223 | 30.0 | 1800 | 0.1745 | 0.3309 | 0.1146 | | 0.2107 | 31.0 | 1860 | 0.1532 | 0.3049 | 0.1001 | | 0.2042 | 32.0 | 1920 | 0.1219 | 0.2893 | 0.1260 | | 0.1734 | 33.0 | 1980 | 0.1088 | 0.2596 | 0.0942 | | 0.1543 | 34.0 | 2040 | 0.0889 | 0.2200 | 0.0723 | | 0.1954 | 35.0 | 2100 | 0.0943 | 0.2285 | 0.0745 | | 0.125 | 36.0 | 2160 | 0.0749 | 0.1996 | 0.0652 | | 0.132 | 37.0 | 2220 | 0.0734 | 0.2029 | 0.0653 | | 0.1136 | 38.0 | 2280 | 0.0656 | 0.1951 | 0.0624 | | 0.1081 | 39.0 | 2340 | 0.0606 | 0.1881 | 0.0599 | | 0.1512 | 40.0 | 2400 | 0.0584 | 0.1836 | 0.0589 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "rinna/japanese-hubert-base", "model-index": [{"name": "hubert_3", "results": []}]}
automatic-speech-recognition
tndklab/hubert_3
[ "transformers", "safetensors", "hubert", "automatic-speech-recognition", "generated_from_trainer", "base_model:rinna/japanese-hubert-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-08T08:08:11+00:00
[]
[]
TAGS #transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us
hubert\_3 ========= This model is a fine-tuned version of rinna/japanese-hubert-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0584 * Wer: 0.1836 * Cer: 0.0589 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: 0.00027 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 40 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00027\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 40", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-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: 0.00027\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 40", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ 66, 116, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-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: 0.00027\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 40### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
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null
null
peft
- PEFT 0.6.0
{"library_name": "peft", "base_model": "upstage/SOLAR-10.7B-v1.0"}
null
RefalMachine/solar_10.7_saiga_lora
[ "peft", "base_model:upstage/SOLAR-10.7B-v1.0", "region:us" ]
2024-02-08T08:08:35+00:00
[]
[]
TAGS #peft #base_model-upstage/SOLAR-10.7B-v1.0 #region-us
- PEFT 0.6.0
[]
[ "TAGS\n#peft #base_model-upstage/SOLAR-10.7B-v1.0 #region-us \n" ]
[ 26 ]
[ "passage: TAGS\n#peft #base_model-upstage/SOLAR-10.7B-v1.0 #region-us \n" ]
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null
null
transformers
This model is the fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on bsd_ja_en dataset. This will translate Japanese sentences to English sentences.
{"language": ["ja", "en"], "license": "mit", "library_name": "transformers", "datasets": ["bsd_ja_en"], "metrics": ["sacrebleu"], "pipeline_tag": "translation", "widget": [{"text": "\u304a\u306f\u3044\u3088\u3002"}, {"text": "\u50d5\u306e\u56fd\u3067\u3000\u3068\u3066\u3082\u3000\u7dba\u9e97\u306a\u3000\u6240\u304c\u3000\u6709\u308a\u307e\u3059\u3002"}]}
translation
minkhantycc/translation-en-ja
[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "ja", "en", "dataset:bsd_ja_en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T08:08:41+00:00
[]
[ "ja", "en" ]
TAGS #transformers #safetensors #marian #text2text-generation #translation #ja #en #dataset-bsd_ja_en #license-mit #autotrain_compatible #endpoints_compatible #region-us
This model is the fine-tuned version of Helsinki-NLP/opus-mt-ja-en on bsd_ja_en dataset. This will translate Japanese sentences to English sentences.
[]
[ "TAGS\n#transformers #safetensors #marian #text2text-generation #translation #ja #en #dataset-bsd_ja_en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 62 ]
[ "passage: TAGS\n#transformers #safetensors #marian #text2text-generation #translation #ja #en #dataset-bsd_ja_en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
flair
## Polish Flair Model -- NER
{"language": "pl", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Jan Brzechwa - polski poeta i adwokat \u017cydowskiego pochodzenia, autor bajek i wierszy dla dzieci, satyrycznych tekst\u00f3w dla doros\u0142ych, a tak\u017ce t\u0142umacz literatury rosyjskiej."}]}
token-classification
clarin-knext/morpho-flair-ner
[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "pl", "region:us" ]
2024-02-08T08:12:07+00:00
[]
[ "pl" ]
TAGS #flair #pytorch #token-classification #sequence-tagger-model #pl #region-us
## Polish Flair Model -- NER
[ "## Polish Flair Model -- NER" ]
[ "TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #pl #region-us \n", "## Polish Flair Model -- NER" ]
[ 30, 9 ]
[ "passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #pl #region-us \n## Polish Flair Model -- NER" ]
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null
null
flair
## Polish Flair Model -- Noun and Verb Phrases
{"language": "pl", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Jan Brzechwa - polski poeta i adwokat \u017cydowskiego pochodzenia, autor bajek i wierszy dla dzieci, satyrycznych tekst\u00f3w dla doros\u0142ych, a tak\u017ce t\u0142umacz literatury rosyjskiej."}]}
token-classification
clarin-knext/morpho-flair-chunk
[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "pl", "region:us" ]
2024-02-08T08:12:17+00:00
[]
[ "pl" ]
TAGS #flair #pytorch #token-classification #sequence-tagger-model #pl #region-us
## Polish Flair Model -- Noun and Verb Phrases
[ "## Polish Flair Model -- Noun and Verb Phrases" ]
[ "TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #pl #region-us \n", "## Polish Flair Model -- Noun and Verb Phrases" ]
[ 30, 15 ]
[ "passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #pl #region-us \n## Polish Flair Model -- Noun and Verb Phrases" ]
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null
null
flair
## Polish Flair Model -- Agreement Phrases
{"language": "pl", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Jan Brzechwa - polski poeta i adwokat \u017cydowskiego pochodzenia, autor bajek i wierszy dla dzieci, satyrycznych tekst\u00f3w dla doros\u0142ych, a tak\u017ce t\u0142umacz literatury rosyjskiej."}]}
token-classification
clarin-knext/morpho-flair-agp
[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "pl", "region:us" ]
2024-02-08T08:12:22+00:00
[]
[ "pl" ]
TAGS #flair #pytorch #token-classification #sequence-tagger-model #pl #region-us
## Polish Flair Model -- Agreement Phrases
[ "## Polish Flair Model -- Agreement Phrases" ]
[ "TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #pl #region-us \n", "## Polish Flair Model -- Agreement Phrases" ]
[ 30, 11 ]
[ "passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #pl #region-us \n## Polish Flair Model -- Agreement Phrases" ]
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This repositories enable third-party libraries integrated with [huggingface_hub](https://github.com/huggingface/huggingface_hub/) to create their own docker so that the widgets on the hub can work as the `transformers` one do. The hardware to run the API will be provided by Hugging Face for now. The `docker_images/common` folder is intended to be a starter point for all new libs that want to be integrated. ### Adding a new container from a new lib. 1. Copy the `docker_images/common` folder into your library's name `docker_images/example`. 2. Edit: - `docker_images/example/requirements.txt` - `docker_images/example/app/main.py` - `docker_images/example/app/pipelines/{task_name}.py` to implement the desired functionality. All required code is marked with `IMPLEMENT_THIS` markup. 3. Remove: - Any pipeline files in `docker_images/example/app/pipelines/` that are not used. - Any tests associated with deleted pipelines in `docker_images/example/tests`. - Any imports of the pipelines you deleted from `docker_images/example/app/pipelines/__init__.py` 4. Feel free to customize anything required by your lib everywhere you want. The only real requirements, are to honor the HTTP endpoints, in the same fashion as the `common` folder for all your supported tasks. 5. Edit `example/tests/test_api.py` to add TESTABLE_MODELS. 6. Pass the test suite `pytest -sv --rootdir docker_images/example/ docker_images/example/` 7. Submit your PR and enjoy ! ### Going the full way Doing the first 7 steps is good enough to get started, however in the process you can anticipate some problems corrections early on. Maintainers will help you along the way if you don't feel confident to follow those steps yourself 1. Test your creation within a docker ```python ./manage.py docker MY_MODEL ``` should work and responds on port 8000. `curl -X POST -d "test" http://localhost:8000` for instance if the pipeline deals with simple text. If it doesn't work out of the box and/or docker is slow for some reason you can test locally (using your local python environment) with : `./manage.py start MY_MODEL` 2. Test your docker uses cache properly. When doing subsequent docker launch with the same model_id, the docker should start up very fast and not redownload the whole model file. If you see the model/repo being downloaded over and over, it means the cache is not being used correctly. You can edit the `docker_images/{framework}/Dockerfile` and add an environment variable (by default it assumes `HUGGINGFACE_HUB_CACHE`), or your code directly to put the model files in the `/data` folder. 3. Add a docker test. Edit the `tests/test_dockers.py` file to add a new test with your new framework in it (`def test_{framework}(self):` for instance). As a basic you should have 1 line per task in this test function with a real working model on the hub. Those tests are relatively slow but will check automatically that correct errors are replied by your API and that the cache works properly. To run those tests your can simply do: ```bash RUN_DOCKER_TESTS=1 pytest -sv tests/test_dockers.py::DockerImageTests::test_{framework} ``` ### Modifying files within `api-inference-community/{routes,validation,..}.py`. If you ever come across a bug within `api-inference-community/` package or want to update it the development process is slightly more involved. - First, make sure you need to change this package, each framework is very autonomous so if your code can get away by being standalone go that way first as it's much simpler. - If you can make the change only in `api-inference-community` without depending on it that's also a great option. Make sure to add the proper tests to your PR. - Finally, the best way to go is to develop locally using `manage.py` command: - Do the necessary modifications within `api-inference-community` first. - Install it locally in your environment with `pip install -e .` - Install your package dependencies locally. - Run your webserver locally: `./manage.py start --framework example --task audio-source-separation --model-id MY_MODEL` - When everything is working, you will need to split your PR in two, 1 for the `api-inference-community` part. The second one will be for your package specific modifications and will only land once the `api-inference-community` tag has landed. - This workflow is still work in progress, don't hesitate to ask questions to maintainers. Another similar command `./manage.py docker --framework example --task audio-source-separation --model-id MY_MODEL` Will launch the server, but this time in a protected, controlled docker environment making sure the behavior will be exactly the one in the API. ### Available tasks - **Automatic speech recognition**: Input is a file, output is a dict of understood words being said within the file - **Text generation**: Input is a text, output is a dict of generated text - **Image recognition**: Input is an image, output is a dict of generated text - **Question answering**: Input is a question + some context, output is a dict containing necessary information to locate the answer to the `question` within the `context`. - **Audio source separation**: Input is some audio, and the output is n audio files that sum up to the original audio but contain individual sources of sound (either speakers or instruments for instant). - **Token classification**: Input is some text, and the output is a list of entities mentioned in the text. Entities can be anything remarkable like locations, organisations, persons, times etc... - **Text to speech**: Input is some text, and the output is an audio file saying the text... - **Sentence Similarity**: Input is some sentence and a list of reference sentences, and the list of similarity scores.
{}
null
tulayaka/x
[ "region:us" ]
2024-02-08T08:17:52+00:00
[]
[]
TAGS #region-us
This repositories enable third-party libraries integrated with huggingface_hub to create their own docker so that the widgets on the hub can work as the 'transformers' one do. The hardware to run the API will be provided by Hugging Face for now. The 'docker_images/common' folder is intended to be a starter point for all new libs that want to be integrated. ### Adding a new container from a new lib. 1. Copy the 'docker_images/common' folder into your library's name 'docker_images/example'. 2. Edit: - 'docker_images/example/URL' - 'docker_images/example/app/URL' - 'docker_images/example/app/pipelines/{task_name}.py' to implement the desired functionality. All required code is marked with 'IMPLEMENT_THIS' markup. 3. Remove: - Any pipeline files in 'docker_images/example/app/pipelines/' that are not used. - Any tests associated with deleted pipelines in 'docker_images/example/tests'. - Any imports of the pipelines you deleted from 'docker_images/example/app/pipelines/__init__.py' 4. Feel free to customize anything required by your lib everywhere you want. The only real requirements, are to honor the HTTP endpoints, in the same fashion as the 'common' folder for all your supported tasks. 5. Edit 'example/tests/test_api.py' to add TESTABLE_MODELS. 6. Pass the test suite 'pytest -sv --rootdir docker_images/example/ docker_images/example/' 7. Submit your PR and enjoy ! ### Going the full way Doing the first 7 steps is good enough to get started, however in the process you can anticipate some problems corrections early on. Maintainers will help you along the way if you don't feel confident to follow those steps yourself 1. Test your creation within a docker should work and responds on port 8000. 'curl -X POST -d "test" http://localhost:8000' for instance if the pipeline deals with simple text. If it doesn't work out of the box and/or docker is slow for some reason you can test locally (using your local python environment) with : './URL start MY_MODEL' 2. Test your docker uses cache properly. When doing subsequent docker launch with the same model_id, the docker should start up very fast and not redownload the whole model file. If you see the model/repo being downloaded over and over, it means the cache is not being used correctly. You can edit the 'docker_images/{framework}/Dockerfile' and add an environment variable (by default it assumes 'HUGGINGFACE_HUB_CACHE'), or your code directly to put the model files in the '/data' folder. 3. Add a docker test. Edit the 'tests/test_dockers.py' file to add a new test with your new framework in it ('def test_{framework}(self):' for instance). As a basic you should have 1 line per task in this test function with a real working model on the hub. Those tests are relatively slow but will check automatically that correct errors are replied by your API and that the cache works properly. To run those tests your can simply do: ### Modifying files within 'api-inference-community/{routes,validation,..}.py'. If you ever come across a bug within 'api-inference-community/' package or want to update it the development process is slightly more involved. - First, make sure you need to change this package, each framework is very autonomous so if your code can get away by being standalone go that way first as it's much simpler. - If you can make the change only in 'api-inference-community' without depending on it that's also a great option. Make sure to add the proper tests to your PR. - Finally, the best way to go is to develop locally using 'URL' command: - Do the necessary modifications within 'api-inference-community' first. - Install it locally in your environment with 'pip install -e .' - Install your package dependencies locally. - Run your webserver locally: './URL start --framework example --task audio-source-separation --model-id MY_MODEL' - When everything is working, you will need to split your PR in two, 1 for the 'api-inference-community' part. The second one will be for your package specific modifications and will only land once the 'api-inference-community' tag has landed. - This workflow is still work in progress, don't hesitate to ask questions to maintainers. Another similar command './URL docker --framework example --task audio-source-separation --model-id MY_MODEL' Will launch the server, but this time in a protected, controlled docker environment making sure the behavior will be exactly the one in the API. ### Available tasks - Automatic speech recognition: Input is a file, output is a dict of understood words being said within the file - Text generation: Input is a text, output is a dict of generated text - Image recognition: Input is an image, output is a dict of generated text - Question answering: Input is a question + some context, output is a dict containing necessary information to locate the answer to the 'question' within the 'context'. - Audio source separation: Input is some audio, and the output is n audio files that sum up to the original audio but contain individual sources of sound (either speakers or instruments for instant). - Token classification: Input is some text, and the output is a list of entities mentioned in the text. Entities can be anything remarkable like locations, organisations, persons, times etc... - Text to speech: Input is some text, and the output is an audio file saying the text... - Sentence Similarity: Input is some sentence and a list of reference sentences, and the list of similarity scores.
[ "### Adding a new container from a new lib.\n\n\n1. Copy the 'docker_images/common' folder into your library's name 'docker_images/example'.\n2. Edit:\n - 'docker_images/example/URL'\n - 'docker_images/example/app/URL'\n - 'docker_images/example/app/pipelines/{task_name}.py' \n\n to implement the desired functionality. All required code is marked with 'IMPLEMENT_THIS' markup.\n3. Remove:\n - Any pipeline files in 'docker_images/example/app/pipelines/' that are not used.\n - Any tests associated with deleted pipelines in 'docker_images/example/tests'.\n - Any imports of the pipelines you deleted from 'docker_images/example/app/pipelines/__init__.py'\n\n4. Feel free to customize anything required by your lib everywhere you want. The only real requirements, are to honor the HTTP endpoints, in the same fashion as the 'common' folder for all your supported tasks.\n5. Edit 'example/tests/test_api.py' to add TESTABLE_MODELS.\n6. Pass the test suite 'pytest -sv --rootdir docker_images/example/ docker_images/example/'\n7. Submit your PR and enjoy !", "### Going the full way\n\nDoing the first 7 steps is good enough to get started, however in the process \nyou can anticipate some problems corrections early on. Maintainers will help you\nalong the way if you don't feel confident to follow those steps yourself\n\n1. Test your creation within a docker\n\n\n\nshould work and responds on port 8000. 'curl -X POST -d \"test\" http://localhost:8000' for instance if \nthe pipeline deals with simple text.\n\nIf it doesn't work out of the box and/or docker is slow for some reason you\ncan test locally (using your local python environment) with :\n\n'./URL start MY_MODEL'\n\n\n2. Test your docker uses cache properly.\n\nWhen doing subsequent docker launch with the same model_id, the docker should start up very fast and not redownload the whole model file. If you see the model/repo being downloaded over and over, it means the cache is not being used correctly.\nYou can edit the 'docker_images/{framework}/Dockerfile' and add an environment variable (by default it assumes 'HUGGINGFACE_HUB_CACHE'), or your code directly to put\nthe model files in the '/data' folder.\n\n3. Add a docker test.\n\nEdit the 'tests/test_dockers.py' file to add a new test with your new framework\nin it ('def test_{framework}(self):' for instance). As a basic you should have 1 line per task in this test function with a real working model on the hub. Those tests are relatively slow but will check automatically that correct errors are replied by your API and that the cache works properly. To run those tests your can simply do:", "### Modifying files within 'api-inference-community/{routes,validation,..}.py'.\n\nIf you ever come across a bug within 'api-inference-community/' package or want to update it\nthe development process is slightly more involved.\n\n- First, make sure you need to change this package, each framework is very autonomous\n so if your code can get away by being standalone go that way first as it's much simpler.\n- If you can make the change only in 'api-inference-community' without depending on it\n that's also a great option. Make sure to add the proper tests to your PR.\n- Finally, the best way to go is to develop locally using 'URL' command:\n- Do the necessary modifications within 'api-inference-community' first.\n- Install it locally in your environment with 'pip install -e .'\n- Install your package dependencies locally.\n- Run your webserver locally: './URL start --framework example --task audio-source-separation --model-id MY_MODEL'\n- When everything is working, you will need to split your PR in two, 1 for the 'api-inference-community' part.\n The second one will be for your package specific modifications and will only land once the 'api-inference-community' tag has landed.\n- This workflow is still work in progress, don't hesitate to ask questions to maintainers.\n\nAnother similar command './URL docker --framework example --task audio-source-separation --model-id MY_MODEL'\nWill launch the server, but this time in a protected, controlled docker environment making sure the behavior\nwill be exactly the one in the API.", "### Available tasks\n\n- Automatic speech recognition: Input is a file, output is a dict of understood words being said within the file\n- Text generation: Input is a text, output is a dict of generated text\n- Image recognition: Input is an image, output is a dict of generated text\n- Question answering: Input is a question + some context, output is a dict containing necessary information to locate the answer to the 'question' within the 'context'.\n- Audio source separation: Input is some audio, and the output is n audio files that sum up to the original audio but contain individual sources of sound (either speakers or instruments for instant).\n- Token classification: Input is some text, and the output is a list of entities mentioned in the text. Entities can be anything remarkable like locations, organisations, persons, times etc...\n- Text to speech: Input is some text, and the output is an audio file saying the text...\n- Sentence Similarity: Input is some sentence and a list of reference sentences, and the list of similarity scores." ]
[ "TAGS\n#region-us \n", "### Adding a new container from a new lib.\n\n\n1. Copy the 'docker_images/common' folder into your library's name 'docker_images/example'.\n2. Edit:\n - 'docker_images/example/URL'\n - 'docker_images/example/app/URL'\n - 'docker_images/example/app/pipelines/{task_name}.py' \n\n to implement the desired functionality. All required code is marked with 'IMPLEMENT_THIS' markup.\n3. Remove:\n - Any pipeline files in 'docker_images/example/app/pipelines/' that are not used.\n - Any tests associated with deleted pipelines in 'docker_images/example/tests'.\n - Any imports of the pipelines you deleted from 'docker_images/example/app/pipelines/__init__.py'\n\n4. Feel free to customize anything required by your lib everywhere you want. The only real requirements, are to honor the HTTP endpoints, in the same fashion as the 'common' folder for all your supported tasks.\n5. Edit 'example/tests/test_api.py' to add TESTABLE_MODELS.\n6. Pass the test suite 'pytest -sv --rootdir docker_images/example/ docker_images/example/'\n7. Submit your PR and enjoy !", "### Going the full way\n\nDoing the first 7 steps is good enough to get started, however in the process \nyou can anticipate some problems corrections early on. Maintainers will help you\nalong the way if you don't feel confident to follow those steps yourself\n\n1. Test your creation within a docker\n\n\n\nshould work and responds on port 8000. 'curl -X POST -d \"test\" http://localhost:8000' for instance if \nthe pipeline deals with simple text.\n\nIf it doesn't work out of the box and/or docker is slow for some reason you\ncan test locally (using your local python environment) with :\n\n'./URL start MY_MODEL'\n\n\n2. Test your docker uses cache properly.\n\nWhen doing subsequent docker launch with the same model_id, the docker should start up very fast and not redownload the whole model file. If you see the model/repo being downloaded over and over, it means the cache is not being used correctly.\nYou can edit the 'docker_images/{framework}/Dockerfile' and add an environment variable (by default it assumes 'HUGGINGFACE_HUB_CACHE'), or your code directly to put\nthe model files in the '/data' folder.\n\n3. Add a docker test.\n\nEdit the 'tests/test_dockers.py' file to add a new test with your new framework\nin it ('def test_{framework}(self):' for instance). As a basic you should have 1 line per task in this test function with a real working model on the hub. Those tests are relatively slow but will check automatically that correct errors are replied by your API and that the cache works properly. To run those tests your can simply do:", "### Modifying files within 'api-inference-community/{routes,validation,..}.py'.\n\nIf you ever come across a bug within 'api-inference-community/' package or want to update it\nthe development process is slightly more involved.\n\n- First, make sure you need to change this package, each framework is very autonomous\n so if your code can get away by being standalone go that way first as it's much simpler.\n- If you can make the change only in 'api-inference-community' without depending on it\n that's also a great option. Make sure to add the proper tests to your PR.\n- Finally, the best way to go is to develop locally using 'URL' command:\n- Do the necessary modifications within 'api-inference-community' first.\n- Install it locally in your environment with 'pip install -e .'\n- Install your package dependencies locally.\n- Run your webserver locally: './URL start --framework example --task audio-source-separation --model-id MY_MODEL'\n- When everything is working, you will need to split your PR in two, 1 for the 'api-inference-community' part.\n The second one will be for your package specific modifications and will only land once the 'api-inference-community' tag has landed.\n- This workflow is still work in progress, don't hesitate to ask questions to maintainers.\n\nAnother similar command './URL docker --framework example --task audio-source-separation --model-id MY_MODEL'\nWill launch the server, but this time in a protected, controlled docker environment making sure the behavior\nwill be exactly the one in the API.", "### Available tasks\n\n- Automatic speech recognition: Input is a file, output is a dict of understood words being said within the file\n- Text generation: Input is a text, output is a dict of generated text\n- Image recognition: Input is an image, output is a dict of generated text\n- Question answering: Input is a question + some context, output is a dict containing necessary information to locate the answer to the 'question' within the 'context'.\n- Audio source separation: Input is some audio, and the output is n audio files that sum up to the original audio but contain individual sources of sound (either speakers or instruments for instant).\n- Token classification: Input is some text, and the output is a list of entities mentioned in the text. Entities can be anything remarkable like locations, organisations, persons, times etc...\n- Text to speech: Input is some text, and the output is an audio file saying the text...\n- Sentence Similarity: Input is some sentence and a list of reference sentences, and the list of similarity scores." ]
[ 6, 339, 383, 403, 248 ]
[ "passage: TAGS\n#region-us \n### Adding a new container from a new lib.\n\n\n1. Copy the 'docker_images/common' folder into your library's name 'docker_images/example'.\n2. Edit:\n - 'docker_images/example/URL'\n - 'docker_images/example/app/URL'\n - 'docker_images/example/app/pipelines/{task_name}.py' \n\n to implement the desired functionality. All required code is marked with 'IMPLEMENT_THIS' markup.\n3. Remove:\n - Any pipeline files in 'docker_images/example/app/pipelines/' that are not used.\n - Any tests associated with deleted pipelines in 'docker_images/example/tests'.\n - Any imports of the pipelines you deleted from 'docker_images/example/app/pipelines/__init__.py'\n\n4. Feel free to customize anything required by your lib everywhere you want. The only real requirements, are to honor the HTTP endpoints, in the same fashion as the 'common' folder for all your supported tasks.\n5. Edit 'example/tests/test_api.py' to add TESTABLE_MODELS.\n6. Pass the test suite 'pytest -sv --rootdir docker_images/example/ docker_images/example/'\n7. Submit your PR and enjoy !", "passage: ### Going the full way\n\nDoing the first 7 steps is good enough to get started, however in the process \nyou can anticipate some problems corrections early on. Maintainers will help you\nalong the way if you don't feel confident to follow those steps yourself\n\n1. Test your creation within a docker\n\n\n\nshould work and responds on port 8000. 'curl -X POST -d \"test\" http://localhost:8000' for instance if \nthe pipeline deals with simple text.\n\nIf it doesn't work out of the box and/or docker is slow for some reason you\ncan test locally (using your local python environment) with :\n\n'./URL start MY_MODEL'\n\n\n2. Test your docker uses cache properly.\n\nWhen doing subsequent docker launch with the same model_id, the docker should start up very fast and not redownload the whole model file. If you see the model/repo being downloaded over and over, it means the cache is not being used correctly.\nYou can edit the 'docker_images/{framework}/Dockerfile' and add an environment variable (by default it assumes 'HUGGINGFACE_HUB_CACHE'), or your code directly to put\nthe model files in the '/data' folder.\n\n3. Add a docker test.\n\nEdit the 'tests/test_dockers.py' file to add a new test with your new framework\nin it ('def test_{framework}(self):' for instance). As a basic you should have 1 line per task in this test function with a real working model on the hub. Those tests are relatively slow but will check automatically that correct errors are replied by your API and that the cache works properly. To run those tests your can simply do:### Modifying files within 'api-inference-community/{routes,validation,..}.py'.\n\nIf you ever come across a bug within 'api-inference-community/' package or want to update it\nthe development process is slightly more involved.\n\n- First, make sure you need to change this package, each framework is very autonomous\n so if your code can get away by being standalone go that way first as it's much simpler.\n- If you can make the change only in 'api-inference-community' without depending on it\n that's also a great option. Make sure to add the proper tests to your PR.\n- Finally, the best way to go is to develop locally using 'URL' command:\n- Do the necessary modifications within 'api-inference-community' first.\n- Install it locally in your environment with 'pip install -e .'\n- Install your package dependencies locally.\n- Run your webserver locally: './URL start --framework example --task audio-source-separation --model-id MY_MODEL'\n- When everything is working, you will need to split your PR in two, 1 for the 'api-inference-community' part.\n The second one will be for your package specific modifications and will only land once the 'api-inference-community' tag has landed.\n- This workflow is still work in progress, don't hesitate to ask questions to maintainers.\n\nAnother similar command './URL docker --framework example --task audio-source-separation --model-id MY_MODEL'\nWill launch the server, but this time in a protected, controlled docker environment making sure the behavior\nwill be exactly the one in the 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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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
humung/polyglot-ko-12.8b-vlending-v0.6
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T08:19:31+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
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. --> # face_emotion_recognizer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.7251 - Accuracy: 0.4188 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 1.9125 | 0.4125 | | No log | 2.0 | 80 | 1.7183 | 0.4188 | | No log | 3.0 | 120 | 1.6596 | 0.4125 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "face_emotion_recognizer", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.41875, "name": "Accuracy"}]}]}]}
image-classification
rendy-k/face_emotion_recognizer
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T08:20:24+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
face\_emotion\_recognizer ========================= This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.7251 * Accuracy: 0.4188 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 86, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\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. --> # Classifier for Academic Text Contents This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on a collection of Linguistics publications. It achieves the following results on the evaluation set: - Loss: 0.4181 - Accuracy: 0.9193 ## Model description The model is fine-tuned with academic publications in Linguistics, to classify texts in publications into 4 classes as a filter to other tasks. The 4 classes: - 0: out of scope - materials that are of low significance, eg. page number and page header, noise from OCR/pdf-to-text convertion - 1: main text - texts that are the main texts of the publication, to be used for down-stream tasks - 2: examples - texts that are captions of the figures, or quotes or excerpts - 3: references - references of the publication, excluding in-text citations ## Intended uses & limitations Intended uses: - to extract main text in academic texts for down-stream tasks Limitations: - training and evaluation data is limited to English, and academic texts in Linguistics ## Try it yourself with the following examples (not in training/ evaluation data) Excerpts from Chomsky, N. (2014). Aspects of the Theory of Syntax (No. 11). MIT press. retrieved from https://apps.dtic.mil/sti/pdfs/AD0616323.pdf - In the case of (ioii) and (1 lii), the passive transformation will apply to the embedded sentence, and in all four cases other operations will give the final surface forms of (8) and (g). - (10) (i) Noun Phrase — Verb — Noun Phrase — Sentence (/ — persuaded — a specialist — a specialist will examine John) (ii) Noun Phrase — Verb — Noun Phrase — Sentence (/ — persuaded — John — a specialist will examine John) - (13) S Det Predicate-Phrase [+Definite] nom VP their F1...Fm Det N destroy [+Definite] G, ... G, the property - 184 SOME RESIDUAL PROBLEMS - Peshkovskii, A. M. (1956). Russkii Sintaksis v Nauchnom Osveshchenii. Moscow. ## Problematic cases Definitions or findings written in point form are challenging for the model. For example: - (2) (i) the string (1) is a Sentence (S); frighten the boy is a Verb Phrase (VP) consisting of the Verb (V) frighten and the Noun Phrase (NP) the boy; sincerity is also an NP; the NP the boy consists of the Determiner (Det) the, followed by a Noun (N); the NP sincerity consists of just an N; the is, furthermore, an Article (Art); may is a Verbal Auxiliary (Aux) and, furthermore, a Modal (M). - (v) specification of a function m such that m(i) is an integer associated with the grammar G4 as its value (with, let us say, lower value indicated by higher number) ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5772 | 1.0 | 762 | 0.3256 | 0.9062 | | 0.2692 | 2.0 | 1524 | 0.3038 | 0.9163 | | 0.217 | 3.0 | 2286 | 0.3109 | 0.9180 | | 0.1773 | 4.0 | 3048 | 0.3160 | 0.9209 | | 0.1619 | 5.0 | 3810 | 0.3440 | 0.9206 | | 0.1329 | 6.0 | 4572 | 0.3675 | 0.9160 | | 0.1165 | 7.0 | 5334 | 0.3770 | 0.9209 | | 0.0943 | 8.0 | 6096 | 0.4012 | 0.9203 | | 0.085 | 9.0 | 6858 | 0.4166 | 0.9196 | | 0.0811 | 10.0 | 7620 | 0.4181 | 0.9193 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cpu - Datasets 2.14.7 - Tokenizers 0.14.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "sentence-transformers/all-MiniLM-L6-v2", "model-index": [{"name": "new_classifier_model", "results": []}]}
text-classification
howanching-clara/classifier_for_academic_texts
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:sentence-transformers/all-MiniLM-L6-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T08:21:32+00:00
[]
[]
TAGS #transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-sentence-transformers/all-MiniLM-L6-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
Classifier for Academic Text Contents ===================================== This model is a fine-tuned version of sentence-transformers/all-MiniLM-L6-v2 on a collection of Linguistics publications. It achieves the following results on the evaluation set: * Loss: 0.4181 * Accuracy: 0.9193 Model description ----------------- The model is fine-tuned with academic publications in Linguistics, to classify texts in publications into 4 classes as a filter to other tasks. The 4 classes: * 0: out of scope - materials that are of low significance, eg. page number and page header, noise from OCR/pdf-to-text convertion * 1: main text - texts that are the main texts of the publication, to be used for down-stream tasks * 2: examples - texts that are captions of the figures, or quotes or excerpts * 3: references - references of the publication, excluding in-text citations Intended uses & limitations --------------------------- Intended uses: * to extract main text in academic texts for down-stream tasks Limitations: * training and evaluation data is limited to English, and academic texts in Linguistics Try it yourself with the following examples (not in training/ evaluation data) ------------------------------------------------------------------------------ Excerpts from Chomsky, N. (2014). Aspects of the Theory of Syntax (No. 11). MIT press. retrieved from URL * In the case of (ioii) and (1 lii), the passive transformation will apply to the embedded sentence, and in all four cases other operations will give the final surface forms of (8) and (g). * (10) (i) Noun Phrase — Verb — Noun Phrase — Sentence (/ — persuaded — a specialist — a specialist will examine John) (ii) Noun Phrase — Verb — Noun Phrase — Sentence (/ — persuaded — John — a specialist will examine John) * (13) S Det Predicate-Phrase [+Definite] nom VP their F1...Fm Det N destroy [+Definite] G, ... G, the property * 184 SOME RESIDUAL PROBLEMS * Peshkovskii, A. M. (1956). Russkii Sintaksis v Nauchnom Osveshchenii. Moscow. Problematic cases ----------------- Definitions or findings written in point form are challenging for the model. For example: * (2) (i) the string (1) is a Sentence (S); frighten the boy is a Verb Phrase (VP) consisting of the Verb (V) frighten and the Noun Phrase (NP) the boy; sincerity is also an NP; the NP the boy consists of the Determiner (Det) the, followed by a Noun (N); the NP sincerity consists of just an N; the is, furthermore, an Article (Art); may is a Verbal Auxiliary (Aux) and, furthermore, a Modal (M). * (v) specification of a function m such that m(i) is an integer associated with the grammar G4 as its value (with, let us say, lower value indicated by higher number) 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: 10 ### Training results ### Framework versions * Transformers 4.34.1 * Pytorch 2.1.0+cpu * Datasets 2.14.7 * Tokenizers 0.14.1
[ "### 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: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cpu\n* Datasets 2.14.7\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-sentence-transformers/all-MiniLM-L6-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cpu\n* Datasets 2.14.7\n* Tokenizers 0.14.1" ]
[ 72, 98, 4, 35 ]
[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-sentence-transformers/all-MiniLM-L6-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cpu\n* Datasets 2.14.7\n* Tokenizers 0.14.1" ]
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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 0.8.2
{"library_name": "peft", "base_model": "tiiuae/falcon-7b-instruct"}
null
madhiarasan/hr_qna
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:tiiuae/falcon-7b-instruct", "region:us" ]
2024-02-08T08:21:44+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-tiiuae/falcon-7b-instruct #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-tiiuae/falcon-7b-instruct #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" ]
[ 39, 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-tiiuae/falcon-7b-instruct #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
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 Tiny Hu v6 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1880 - Wer Ortho: 11.4928 - Wer: 10.6137 ## 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: 64 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - 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: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.0036 | 5.35 | 1000 | 0.1855 | 12.1161 | 11.2185 | | 0.0004 | 10.71 | 2000 | 0.1880 | 11.4928 | 10.6137 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
{"language": ["hu"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_0"], "metrics": ["wer"], "base_model": "openai/whisper-tiny", "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"}], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "Whisper Tiny Hungarian", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 16.0 - Hungarian", "type": "mozilla-foundation/common_voice_16_0", "config": "hu", "split": "test", "args": "hu"}, "metrics": [{"type": "wer", "value": 10.6137, "name": "Wer"}]}]}]}
automatic-speech-recognition
sarpba/whisper-tiny-cv16-hu-v6
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hu", "dataset:mozilla-foundation/common_voice_16_0", "base_model:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
2024-02-08T08:26:42+00:00
[]
[ "hu" ]
TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hu #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-tiny #license-apache-2.0 #model-index #endpoints_compatible #region-us
Whisper Tiny Hu v6 ================== This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set: * Loss: 0.1880 * Wer Ortho: 11.4928 * Wer: 10.6137 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: 64 * eval\_batch\_size: 32 * seed: 42 * gradient\_accumulation\_steps: 4 * 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: 2000 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 2000\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 2.16.1\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hu #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-tiny #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 2000\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 2.16.1\n* Tokenizers 0.15.0" ]
[ 103, 165, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hu #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-tiny #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 2000\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 2.16.1\n* Tokenizers 0.15.0" ]
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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. --> # SciBERT_TwoWayLoss_25K_bs64_P10_N5 This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 15.1250 - Accuracy: 0.7066 - Precision: 0.0321 - Recall: 0.9982 - F1: 0.0622 - Hamming: 0.2934 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 25000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| | 28.5732 | 0.16 | 5000 | 26.4288 | 0.6945 | 0.0307 | 0.9910 | 0.0595 | 0.3055 | | 19.8755 | 0.32 | 10000 | 18.9620 | 0.7010 | 0.0315 | 0.9959 | 0.0610 | 0.2990 | | 17.1294 | 0.47 | 15000 | 16.5587 | 0.7021 | 0.0316 | 0.9970 | 0.0613 | 0.2979 | | 15.8209 | 0.63 | 20000 | 15.4919 | 0.7053 | 0.0320 | 0.9982 | 0.0620 | 0.2947 | | 15.4304 | 0.79 | 25000 | 15.1250 | 0.7066 | 0.0321 | 0.9982 | 0.0622 | 0.2934 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "SciBERT_TwoWayLoss_25K_bs64_P10_N5", "results": []}]}
text-classification
bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:allenai/scibert_scivocab_uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T08:26:53+00:00
[]
[]
TAGS #transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us
SciBERT\_TwoWayLoss\_25K\_bs64\_P10\_N5 ======================================= This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the None dataset. It achieves the following results on the evaluation set: * Loss: 15.1250 * Accuracy: 0.7066 * Precision: 0.0321 * Recall: 0.9982 * F1: 0.0622 * Hamming: 0.2934 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * training\_steps: 25000 ### Training results ### Framework versions * Transformers 4.33.3 * Pytorch 2.2.0.dev20231002 * Datasets 2.7.1 * Tokenizers 0.13.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000", "### Training results", "### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.2.0.dev20231002\n* Datasets 2.7.1\n* Tokenizers 0.13.3" ]
[ "TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000", "### Training results", "### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.2.0.dev20231002\n* Datasets 2.7.1\n* Tokenizers 0.13.3" ]
[ 61, 116, 4, 35 ]
[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000### Training results### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.2.0.dev20231002\n* Datasets 2.7.1\n* Tokenizers 0.13.3" ]
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null
null
peft
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - 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 - lr_scheduler_warmup_steps: 100 - training_steps: 100 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "results", "results": []}]}
null
mahiatlinux/mistral_custom
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]
2024-02-08T08:27:08+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
# results This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - 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 - lr_scheduler_warmup_steps: 100 - training_steps: 100 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# results\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0004\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- lr_scheduler_warmup_steps: 100\n- training_steps: 100", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "# results\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0004\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- lr_scheduler_warmup_steps: 100\n- training_steps: 100", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 55, 31, 6, 12, 8, 3, 126, 4, 39 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n# results\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0004\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- lr_scheduler_warmup_steps: 100\n- training_steps: 100### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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transformers
<!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <div align="center"> <h1> <img src="image/huozi-logo.jpg" width="30" /> 活字通用大模型 </h1> </div> </p> <div align="center"> <a href="https://github.com/HIT-SCIR/huozi/pulls"> <image src="https://img.shields.io/badge/PRs-welcome-brightgreen"> </a> <a href="https://github.com/HIT-SCIR/huozi/pulls"> <image src="https://img.shields.io/badge/License-Apache_2.0-green.svg"> </a> <!-- <h4 align="center"> <p> <b>中文</b> | <a href="https://github.com/HIT-SCIR/huozi/blob/main/README_EN.md">English</a> <p> </h4> --> </div> ## 🔖 目录 |章节|说明| |---|---| |[💁🏻‍♂ 开源清单](#-开源清单)|本仓库开源项目清单| |[💡 模型介绍](#-模型介绍)|简要介绍活字模型结构和训练过程| |[📥 模型下载](#-模型下载)|活字模型下载链接| |[💻 模型推理](#-模型推理)|活字模型推理样例，包括vLLM推理加速、llama.cpp量化推理等框架的使用流程| |[📈 模型性能](#-模型性能)|活字模型在主流评测任务上的性能| |[🗂 生成样例](#-生成样例)|活字模型实际生成效果样例| ## 💁🏻‍♂ 开源清单 ![](image/models-v3.png) - **活字 3.0**: [[模型权重](#-模型下载)] - 活字3.0为一个稀疏混合专家模型，支持32K上下文，具有丰富的中、英文知识和强大的数学推理、代码生成能力。活字3.0较旧版活字具有更强的指令遵循能力和安全性。 - **中文MT-Bench**: [[数据集](data/mt-bench-zh/)] - 本数据集是英文MT-Bench对话能力评测数据集的中文版。它包含了一系列多轮对话问题，每一组问题都经过了精心的人工校对，并为适应中文语境进行了必要的调整。 - **《ChatGPT 调研报告》**: [[PDF](https://github.com/HIT-SCIR/huozi/blob/main/pdf/chatgpt_book.pdf)] - 哈工大自然语言处理研究所组织多位老师和同学撰写了本调研报告，从技术原理、应用场景、未来发展等方面对ChatGPT进行了尽量详尽的介绍及总结。 - **活字 2.0**: [[模型权重](https://huggingface.co/HIT-SCIR/huozi-7b-rlhf)] [[RLHF数据](data/huozi-rlhf/huozi_rlhf_data.csv)] - 在活字1.0基础上，通过人类反馈的强化学习（RLHF）进一步优化了模型回复质量，使其更加符合人类偏好。相较于上一个版本平均长度明显提高，遵从指令的能力更强，逻辑更加清晰。 - 16.9k 人工标注的偏好数据，回复来自活字模型，可以用于训练奖励模型。 - **活字 1.0**: [[模型权重](https://huggingface.co/HIT-SCIR/huozi-7b-sft)] - 在Bloom模型的基础上，在大约 150 亿 tokens 上进行指令微调训练得到的模型，具有更强的指令遵循能力、更好的安全性。 ## 💡 模型介绍 大规模语言模型（LLM）在自然语言处理领域取得了显著的进展，并在广泛的应用场景中展现了其强大的潜力。这一技术不仅吸引了学术界的广泛关注，也成为了工业界的热点。在此背景下，哈尔滨工业大学社会计算与信息检索研究中心（HIT-SCIR）近期推出了最新成果——**活字3.0**，致力于为自然语言处理的研究和实际应用提供更多可能性和选择。 活字3.0是基于Chinese-Mixtral-8x7B，在大约30万行指令数据上微调得到的模型。该模型支持**32K上下文**，能够有效处理长文本。活字3.0继承了基座模型丰富的**中英文知识**，并在**数学推理**、**代码生成**等任务上具有强大性能。经过指令微调，活字3.0还在**指令遵循能力**和**安全性**方面实现了显著提升。 此外，我们开源了**中文MT-Bench数据集**。这是一个中文开放问题集，包括80组对话任务，用于评估模型的多轮对话和指令遵循能力。该数据集是根据原始MT-Bench翻译得来的，每组问题均经过人工校对和中文语境下的适当调整。我们还对原始MT-Bench中的部分错误答案进行了修正。 > [!IMPORTANT] > 活字系列模型仍然可能生成包含事实性错误的误导性回复或包含偏见/歧视的有害内容，请谨慎鉴别和使用生成的内容，请勿将生成的有害内容传播至互联网。 ### 模型结构 活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。 ![](image/smoe.png) ### 训练过程 由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为[Chinese-Mixtral-8x7B](https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B)，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。 ## 📥 模型下载 |模型名称|文件大小|下载地址|备注| |:---:|:---:|:---:|:---:| |huozi3|88GB|[🤗HuggingFace](https://huggingface.co/HIT-SCIR/huozi3)<br>[ModelScope](https://modelscope.cn/models/HIT-SCIR/huozi3/summary)|活字3.0 完整模型| |huozi3-gguf|25GB|[🤗HuggingFace](https://huggingface.co/HIT-SCIR/huozi3-gguf)<br>[ModelScope](https://modelscope.cn/models/HIT-SCIR/huozi3-gguf/summary)|活字3.0 GGUF版本，适用于llama.cpp等推理框架| |huozi3-awq|24GB|[🤗HuggingFace](https://huggingface.co/HIT-SCIR/huozi3-awq)<br>[ModelScope](https://modelscope.cn/models/HIT-SCIR/huozi3-awq/summary)|活字3.0 AWQ版本，适用于AutoAWQ等推理框架| 如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考[此处训练代码](https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B?tab=readme-ov-file#%E5%BE%AE%E8%B0%83)。 ## 💻 模型推理 ### Quick Start 活字3.0采用ChatML格式的prompt模板，格式为： ``` <|beginofutterance|>系统 {system prompt}<|endofutterance|> <|beginofutterance|>用户 {input}<|endofutterance|> <|beginofutterance|>助手 {output}<|endofutterance|> ``` 使用活字3.0进行推理的示例代码如下： ```python # quickstart.py import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "HIT-SCIR/huozi3" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", ) text = """<|beginofutterance|>系统 你是一个智能助手<|endofutterance|> <|beginofutterance|>用户 请你用python写一段快速排序的代码<|endofutterance|> <|beginofutterance|>助手 """ inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate( **inputs, eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) print(tokenizer.decode(outputs[0], skip_special_tokens=False)) ``` 活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、llama.cpp、AutoAWQ、Text generation web UI等框架。 如果您在下载模型时遇到网络问题，可以使用我们在[ModelScope](#modelscope-模型推理)上提供的检查点。 <details> <summary> #### Transformers 模型推理 + 流式生成 </summary> transformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下： ```python # example/transformers-stream/stream.py import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_id = "HIT-SCIR/huozi3" model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.chat_template = """{% for message in messages %}{{'<|beginofutterance|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|endofutterance|>' + '\n'}}{% endif %}{% endfor %} {% if add_generation_prompt and messages[-1]['role'] != '助手' %}{{ '<|beginofutterance|>助手\n' }}{% endif %}""" chat = [ {"role": "系统", "content": "你是一个智能助手"}, {"role": "用户", "content": "请你用python写一段快速排序的代码"}, ] inputs = tokenizer.apply_chat_template( chat, tokenize=True, add_generation_prompt=True, return_tensors="pt", ).to(0) stream_output = model.generate( inputs, streamer=TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True), eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) ``` </details> <details> <summary> #### ModelScope 模型推理 </summary> ModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可： ```diff # example/modelscope-generate/generate.py import torch - from transformers import AutoModelForCausalLM, AutoTokenizer + from modelscope import AutoTokenizer, AutoModelForCausalLM model_id = "HIT-SCIR/huozi3" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", ) text = """<|beginofutterance|>系统 你是一个智能助手<|endofutterance|> <|beginofutterance|>用户 请你用python写一段快速排序的代码<|endofutterance|> <|beginofutterance|>助手 """ inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate( **inputs, eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) print(tokenizer.decode(outputs[0], skip_special_tokens=False)) ``` </details> <details> <summary> #### vLLM 推理加速 </summary> 活字3.0支持通过vLLM实现推理加速，示例代码如下： ```python # example/vllm-generate/generate.py from vllm import LLM, SamplingParams prompts = [ """<|beginofutterance|>系统 你是一个智能助手<|endofutterance|> <|beginofutterance|>用户 请你用python写一段快速排序的代码<|endofutterance|> <|beginofutterance|>助手 """, ] sampling_params = SamplingParams( temperature=0.8, top_p=0.95, stop_token_ids=[57001], max_tokens=2048 ) llm = LLM( model="HIT-SCIR/huozi3", tensor_parallel_size=4, ) outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(generated_text) ``` </details> <details> <summary> #### 部署 OpenAI API Server </summary> 活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。 环境准备： ```shell $ pip install vllm openai ``` 启动服务： ```shell $ python -m vllm.entrypoints.openai.api_server --model /path/to/huozi3/checkpoint --served-model-name huozi --chat-template template.jinja --tensor-parallel-size 8 --response-role 助手 --max-model-len 2048 ``` 使用OpenAI API发送请求： ```python # example/openai-api/openai-client.py from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="huozi", messages=[ {"role": "系统", "content": "你是一个智能助手"}, {"role": "用户", "content": "请你用python写一段快速排序的代码"}, ], extra_body={"stop_token_ids": [57001]}, ) print("Chat response:", chat_response.choices[0].message.content) ``` 下面是一个使用OpenAI API + Gradio + 流式生成的示例代码： ```python # example/openai-api/openai-client-gradio.py from openai import OpenAI import gradio as gr openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) def predict(message, history): history_openai_format = [ {"role": "系统", "content": "你是一个智能助手"}, ] for human, assistant in history: history_openai_format.append({"role": "用户", "content": human}) history_openai_format.append({"role": "助手", "content": assistant}) history_openai_format.append({"role": "用户", "content": message}) models = client.models.list() stream = client.chat.completions.create( model=models.data[0].id, messages=history_openai_format, temperature=0.8, stream=True, extra_body={"repetition_penalty": 1, "stop_token_ids": [57001]}, ) partial_message = "" for chunk in stream: partial_message += chunk.choices[0].delta.content or "" yield partial_message gr.ChatInterface(predict).queue().launch() ``` </details> ### 量化推理 活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量： |量化方法|显存占用| |:---:|:---:| |无|95GB| |AWQ|32GB| |GGUF(q4_0)|28GB| |GGUF(q2_k)|18GB| |GGUF(q2_k, offload 16层)|9.6GB| <details> <summary> #### GGUF 格式 </summary> GGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了[GGUF格式的活字3.0](https://huggingface.co/HIT-SCIR/huozi3-gguf)。 您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。 ##### Step 1 环境准备 首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理： ```shell $ git clone --recurse-submodules https://github.com/HIT-SCIR/huozi $ cd examples/llama.cpp ``` 您也可以下载最新版本的llama.cpp源码： ```shell $ git clone https://github.com/ggerganov/llama.cpp.git $ cd llama.cpp ``` 然后需要进行编译。根据您的硬件平台，编译命令有细微差异： ```shell $ make # 用于纯CPU推理 $ make LLAMA_CUBLAS=1 # 用于GPU推理 $ LLAMA_METAL=1 make # 用于Apple Silicon，暂未经过测试 ``` ##### Step 2 格式转换（可选） 以下命令需要在`llama.cpp/`目录下： ```shell # 转换为GGUF格式 $ python convert.py --outfile /path/to/huozi-gguf/huozi3.gguf /path/to/huozi3 # 进行GGUF格式的q4_0量化 $ quantize /path/to/huozi-gguf/huozi3.gguf /path/to/huozi-gguf/huozi3-q4_0.gguf q4_0 ``` ##### Step 3 开始推理 以下命令需要在`llama.cpp/`目录下： ```shell $ main -m /path/to/huozi-gguf/huozi3-q4_0.gguf --color --interactive-first -c 2048 -t 6 --temp 0.2 --repeat_penalty 1.1 -ngl 999 --in-prefix "<|beginofutterance|>用户\n" --in-suffix "<|endofutterance|>\n<|beginofutterance|>助手" -r "<|endofutterance|>" ``` `-ngl`参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型： ```shell $ main -m /path/to/huozi-gguf/huozi3-q2_k.gguf --color --interactive-first -c 2048 -t 6 --temp 0.2 --repeat_penalty 1.1 -ngl 16 --in-prefix "<|beginofutterance|>用户\n" --in-suffix "<|endofutterance|>\n<|beginofutterance|>助手" -r "<|endofutterance|>" ``` 关于`main`的更多参数，可以参考llama.cpp的[官方文档](https://github.com/ggerganov/llama.cpp/tree/master/examples/main)。 </details> <details> <summary> #### AWQ 格式 </summary> AWQ是一种量化模型的存储格式。我们已经提供了[AWQ格式的活字3.0](https://huggingface.co/HIT-SCIR/huozi3-awq)，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。 ##### Step 1 格式转换（可选） ```python # example/autoawq-generate/quant.py from awq import AutoAWQForCausalLM from transformers import AutoTokenizer model_path = "/path/to/huozi3" quant_path = "/path/to/save/huozi3-awq" modules_to_not_convert = ["gate"] quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM", "modules_to_not_convert": modules_to_not_convert, } model = AutoAWQForCausalLM.from_pretrained( model_path, safetensors=True, **{"low_cpu_mem_usage": True}, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model.quantize( tokenizer, quant_config=quant_config, modules_to_not_convert=modules_to_not_convert, ) model.save_quantized(quant_path) tokenizer.save_pretrained(quant_path) print(f'Model is quantized and saved at "{quant_path}"') ``` ##### Step 2 开始推理 在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下： ```diff # example/autoawq-generate/generate.py import torch + from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer - model_id = "HIT-SCIR/huozi3" + model_id = "HIT-SCIR/huozi3-awq" # or model_id = "/path/to/saved/huozi3-awq" + model = AutoAWQForCausalLM.from_quantized(model_id, fuse_layers=True) - model = AutoModelForCausalLM.from_pretrained( - model_id, - attn_implementation="flash_attention_2", - torch_dtype=torch.bfloat16, - device_map="auto", - ) tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.chat_template = """{% for message in messages %}{{'<|beginofutterance|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|endofutterance|>' + '\n'}}{% endif %}{% endfor %} {% if add_generation_prompt and messages[-1]['role'] != '助手' %}{{ '<|beginofutterance|>助手\n' }}{% endif %}""" chat = [ {"role": "系统", "content": "你是一个智能助手"}, {"role": "用户", "content": "请你用python写一段快速排序的代码"}, ] inputs = tokenizer.apply_chat_template( chat, tokenize=True, add_generation_prompt=True, return_tensors="pt", ).to(0) stream_output = model.generate( inputs, streamer=TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True), eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) ``` </details> ## 📈 模型性能 ![](image/metric-v3-h.png) 针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测： - C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。 - CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。 - GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。 - MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。 - HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。 - GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。 - HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。 - MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。 - MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在[此处](data/mt-bench-zh/)开源MT-Bench-zh数据集。 - MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。 活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果： <!-- | 模型名称 | 模型结构 | C-Eval<br>(中文) | CMMLU<br>(中文) | GAOKAO<br>(中文) | MT-Bench-zh<br>(中文对话) | MT-Bench-safety<br>(中文安全) | MMLU<br>(英文) | HellaSwag<br>(英文) | MT-Bench<br>(英文对话) | GSM8K<br>(数学) | HumanEval<br>(代码) | |---------------------------------------------|---------|--------------|-------------|---------------|--------------------------|-----------------------------|------------|------------------|-----------------------|-------------|-----------------| | baichuan-inc/Baichuan2-13B-Chat v2 | Baichuan| 56.13 | 58.50 | 48.99 | 6.74 | 8.30 | 54.50 | 51.19 | 6.59 | 25.17 | 20.12 | | wangrongsheng/Aurora-Plus | Mixtral | 47.67 | 48.75 | 35.05 | 5.47 | 6.70 | 67.80 | 78.27 | 7.13 | 66.26 | 27.44 | | TigerResearch/tigerbot-13b-chat-v5 | LLaMA | 49.78 | 51.28 | 41.31 | 5.98 | 7.63 | 56.34 | 35.17 | 4.88 | 66.19 | 14.63 | | hfl/chinese-alpaca-2-13b | LLaMA | 43.47 | 44.53 | 25.94 | 5.77 | 8.13 | 53.05 | 56.85 | 6.24 | 32.75 | 14.02 | | 活字1.0 | BLOOM | 37.27 | 36.24 | 19.72 | 4.48 | 7.18 | 39.68 | 33.21 | 4.34 | 21.99 | 13.41 | | 活字2.0 | BLOOM | 32.05 | 34.68 | 22.97 | 5.08 | 6.68 | 38.04 | 33.34 | 4.79 | 19.86 | 6.71 | | **活字3.0（最新版本）** | Mixtral | 51.82 | 51.06 | 41.21 | 6.29 | 7.58 | 69.48 | 65.18 | 7.62 | 65.81 | 40.85 | --> ![](image/evaluation-v3.png) > 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。 > > 我们使用OpenCompass作为评测框架，commit hash为[4c87e77](https://github.com/open-compass/opencompass/tree/4c87e777d855636b9eda7ec87bcbbf12b62caed3)。评测代码位于[此处](./evaluate/)。 根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。 ## 🗂 生成样例 下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比： ![](image/examples/v3-case1.png) ![](image/examples/v3-case2.png) ![](image/examples/v3-case3.png) ![](image/examples/v3-case4.png) ![](image/examples/v3-case5.png) ## <img src="https://cdn.jsdelivr.net/gh/LightChen233/blog-img/folders.png" width="25" /> 开源协议 对本仓库源码的使用遵循开源许可协议 [Apache 2.0](https://github.com/HIT-SCIR/huozi/blob/main/LICENSE)。 活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：<[email protected]>。 ## <img src="https://cdn.jsdelivr.net/gh/LightChen233/blog-img/notes.png" width="25" /> Citation ### 活字大模型 ```latex @misc{huozi, author = {Huozi-Team}. title = {Huozi: Leveraging Large Language Models for Enhanced Open-Domain Chatting} year = {2024}, publisher = {GitHub}, journal = {GitHub repository} howpublished = {\url{https://github.com/HIT-SCIR/huozi}} } ``` ## <img src="https://cdn.jsdelivr.net/gh/LightChen233/blog-img/star.png" width="25" /> Star History [![Star History Chart](https://api.star-history.com/svg?repos=HIT-SCIR/huozi&type=Date)](https://star-history.com/#HIT-SCIR/huozi&Date)
{}
text-generation
HIT-SCIR/huozi3
[ "transformers", "safetensors", "mixtral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T08:27:24+00:00
[]
[]
TAGS #transformers #safetensors #mixtral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
活字通用大模型 ========= 目录 -- ‍ 开源清单 ------ ![](image/URL) * 活字 3.0: [模型权重] + 活字3.0为一个稀疏混合专家模型，支持32K上下文，具有丰富的中、英文知识和强大的数学推理、代码生成能力。活字3.0较旧版活字具有更强的指令遵循能力和安全性。 * 中文MT-Bench: [数据集] + 本数据集是英文MT-Bench对话能力评测数据集的中文版。它包含了一系列多轮对话问题，每一组问题都经过了精心的人工校对，并为适应中文语境进行了必要的调整。 * 《ChatGPT 调研报告》: [PDF] + 哈工大自然语言处理研究所组织多位老师和同学撰写了本调研报告，从技术原理、应用场景、未来发展等方面对ChatGPT进行了尽量详尽的介绍及总结。 * 活字 2.0: [模型权重] [RLHF数据] + 在活字1.0基础上，通过人类反馈的强化学习（RLHF）进一步优化了模型回复质量，使其更加符合人类偏好。相较于上一个版本平均长度明显提高，遵从指令的能力更强，逻辑更加清晰。 + 16.9k 人工标注的偏好数据，回复来自活字模型，可以用于训练奖励模型。 * 活字 1.0: [模型权重] + 在Bloom模型的基础上，在大约 150 亿 tokens 上进行指令微调训练得到的模型，具有更强的指令遵循能力、更好的安全性。 模型介绍 ---- 大规模语言模型（LLM）在自然语言处理领域取得了显著的进展，并在广泛的应用场景中展现了其强大的潜力。这一技术不仅吸引了学术界的广泛关注，也成为了工业界的热点。在此背景下，哈尔滨工业大学社会计算与信息检索研究中心（HIT-SCIR）近期推出了最新成果——活字3.0，致力于为自然语言处理的研究和实际应用提供更多可能性和选择。 活字3.0是基于Chinese-Mixtral-8x7B，在大约30万行指令数据上微调得到的模型。该模型支持32K上下文，能够有效处理长文本。活字3.0继承了基座模型丰富的中英文知识，并在数学推理、代码生成等任务上具有强大性能。经过指令微调，活字3.0还在指令遵循能力和安全性方面实现了显著提升。 此外，我们开源了中文MT-Bench数据集。这是一个中文开放问题集，包括80组对话任务，用于评估模型的多轮对话和指令遵循能力。该数据集是根据原始MT-Bench翻译得来的，每组问题均经过人工校对和中文语境下的适当调整。我们还对原始MT-Bench中的部分错误答案进行了修正。 > > [!IMPORTANT] > 活字系列模型仍然可能生成包含事实性错误的误导性回复或包含偏见/歧视的有害内容，请谨慎鉴别和使用生成的内容，请勿将生成的有害内容传播至互联网。 > > > ### 模型结构 活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。 ![](image/URL) ### 训练过程 由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。 模型下载 ---- 如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。 模型推理 ---- ### Quick Start 活字3.0采用ChatML格式的prompt模板，格式为： 使用活字3.0进行推理的示例代码如下： 活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。 如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。 #### Transformers 模型推理 + 流式生成 transformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下： #### ModelScope 模型推理 ModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可： #### vLLM 推理加速 活字3.0支持通过vLLM实现推理加速，示例代码如下： #### 部署 OpenAI API Server 活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。 环境准备： 启动服务： 使用OpenAI API发送请求： 下面是一个使用OpenAI API + Gradio + 流式生成的示例代码： ### 量化推理 活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量： #### GGUF 格式 GGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。 您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。 ##### Step 1 环境准备 首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理： 您也可以下载最新版本的llama.cpp源码： 然后需要进行编译。根据您的硬件平台，编译命令有细微差异： ##### Step 2 格式转换（可选） 以下命令需要在'URL'目录下： ##### Step 3 开始推理 以下命令需要在'URL'目录下： '-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型： 关于'main'的更多参数，可以参考llama.cpp的官方文档。 #### AWQ 格式 AWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。 ##### Step 1 格式转换（可选） ##### Step 2 开始推理 在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下： 模型性能 ---- ![](image/URL) 针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测： * C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。 * CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。 * GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。 * MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。 * HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。 * GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。 * HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。 * MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。 * MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在此处开源MT-Bench-zh数据集。 * MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。 活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果： ![](image/URL) > > 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。 > > > 我们使用OpenCompass作为评测框架，commit hash为4c87e77。评测代码位于此处。 > > > 根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。 生成样例 ---- 下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比： ![](image/examples/URL) ![](image/examples/URL) ![](image/examples/URL) ![](image/examples/URL) ![](image/examples/URL) <img src="URL width="25" /> 开源协议 -------------------------------- 对本仓库源码的使用遵循开源许可协议 Apache 2.0。 活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：[jngao@URL](mailto:jngao@URL)。 <img src="URL width="25" /> Citation ------------------------------------ ### 活字大模型 <img src="URL width="25" /> Star History ---------------------------------------- ![Star History Chart](URL
[ "### 模型结构\n\n\n活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。\n\n\n![](image/URL)", "### 训练过程\n\n\n由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。\n\n\n模型下载\n----\n\n\n\n如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。\n\n\n模型推理\n----", "### Quick Start\n\n\n活字3.0采用ChatML格式的prompt模板，格式为：\n\n\n使用活字3.0进行推理的示例代码如下：\n\n\n活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。\n\n\n如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。", "#### Transformers 模型推理 + 流式生成\n\n\n\ntransformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下：", "#### ModelScope 模型推理\n\n\n\nModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可：", "#### vLLM 推理加速\n\n\n\n活字3.0支持通过vLLM实现推理加速，示例代码如下：", "#### 部署 OpenAI API Server\n\n\n\n活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。\n\n\n环境准备：\n\n\n启动服务：\n\n\n使用OpenAI API发送请求：\n\n\n下面是一个使用OpenAI API + Gradio + 流式生成的示例代码：", "### 量化推理\n\n\n活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量：", "#### GGUF 格式\n\n\n\nGGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。\n\n\n您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。", "##### Step 1 环境准备\n\n\n首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理：\n\n\n您也可以下载最新版本的llama.cpp源码：\n\n\n然后需要进行编译。根据您的硬件平台，编译命令有细微差异：", "##### Step 2 格式转换（可选）\n\n\n以下命令需要在'URL'目录下：", "##### Step 3 开始推理\n\n\n以下命令需要在'URL'目录下：\n\n\n'-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型：\n\n\n关于'main'的更多参数，可以参考llama.cpp的官方文档。", "#### AWQ 格式\n\n\n\nAWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。", "##### Step 1 格式转换（可选）", "##### Step 2 开始推理\n\n\n在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下：\n\n\n\n模型性能\n----\n\n\n![](image/URL)\n\n\n针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测：\n\n\n* C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。\n* CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。\n* GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。\n* MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。\n* HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。\n* GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。\n* HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。\n* MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。\n* MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在此处开源MT-Bench-zh数据集。\n* MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。\n\n\n活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果：\n\n\n![](image/URL)\n\n\n\n> \n> 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。\n> \n> \n> 我们使用OpenCompass作为评测框架，commit hash为4c87e77。评测代码位于此处。\n> \n> \n> \n\n\n根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。\n\n\n生成样例\n----\n\n\n下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比：\n\n\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n\n\n<img src=\"URL width=\"25\" /> 开源协议\n--------------------------------\n\n\n对本仓库源码的使用遵循开源许可协议 Apache 2.0。\n\n\n活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：[jngao@URL](mailto:jngao@URL)。\n\n\n<img src=\"URL width=\"25\" /> Citation\n------------------------------------", "### 活字大模型\n\n\n<img src=\"URL width=\"25\" /> Star History\n----------------------------------------\n\n\n![Star History Chart](URL" ]
[ "TAGS\n#transformers #safetensors #mixtral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### 模型结构\n\n\n活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。\n\n\n![](image/URL)", "### 训练过程\n\n\n由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。\n\n\n模型下载\n----\n\n\n\n如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。\n\n\n模型推理\n----", "### Quick Start\n\n\n活字3.0采用ChatML格式的prompt模板，格式为：\n\n\n使用活字3.0进行推理的示例代码如下：\n\n\n活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。\n\n\n如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。", "#### Transformers 模型推理 + 流式生成\n\n\n\ntransformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下：", "#### ModelScope 模型推理\n\n\n\nModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可：", "#### vLLM 推理加速\n\n\n\n活字3.0支持通过vLLM实现推理加速，示例代码如下：", "#### 部署 OpenAI API Server\n\n\n\n活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。\n\n\n环境准备：\n\n\n启动服务：\n\n\n使用OpenAI API发送请求：\n\n\n下面是一个使用OpenAI API + Gradio + 流式生成的示例代码：", "### 量化推理\n\n\n活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量：", "#### GGUF 格式\n\n\n\nGGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。\n\n\n您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。", "##### Step 1 环境准备\n\n\n首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理：\n\n\n您也可以下载最新版本的llama.cpp源码：\n\n\n然后需要进行编译。根据您的硬件平台，编译命令有细微差异：", "##### Step 2 格式转换（可选）\n\n\n以下命令需要在'URL'目录下：", "##### Step 3 开始推理\n\n\n以下命令需要在'URL'目录下：\n\n\n'-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型：\n\n\n关于'main'的更多参数，可以参考llama.cpp的官方文档。", "#### AWQ 格式\n\n\n\nAWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。", "##### Step 1 格式转换（可选）", "##### Step 2 开始推理\n\n\n在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下：\n\n\n\n模型性能\n----\n\n\n![](image/URL)\n\n\n针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测：\n\n\n* C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。\n* CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。\n* GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。\n* MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。\n* HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。\n* GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。\n* HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。\n* MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。\n* MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在此处开源MT-Bench-zh数据集。\n* MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。\n\n\n活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果：\n\n\n![](image/URL)\n\n\n\n> \n> 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。\n> \n> \n> 我们使用OpenCompass作为评测框架，commit hash为4c87e77。评测代码位于此处。\n> \n> \n> \n\n\n根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。\n\n\n生成样例\n----\n\n\n下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比：\n\n\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n\n\n<img src=\"URL width=\"25\" /> 开源协议\n--------------------------------\n\n\n对本仓库源码的使用遵循开源许可协议 Apache 2.0。\n\n\n活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：[jngao@URL](mailto:jngao@URL)。\n\n\n<img src=\"URL width=\"25\" /> Citation\n------------------------------------", "### 活字大模型\n\n\n<img src=\"URL width=\"25\" /> Star History\n----------------------------------------\n\n\n![Star History Chart](URL" ]
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[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### 模型结构\n\n\n活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。\n\n\n![](image/URL)### 训练过程\n\n\n由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。\n\n\n模型下载\n----\n\n\n\n如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。\n\n\n模型推理\n----### Quick Start\n\n\n活字3.0采用ChatML格式的prompt模板，格式为：\n\n\n使用活字3.0进行推理的示例代码如下：\n\n\n活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。\n\n\n如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。", "passage: #### Transformers 模型推理 + 流式生成\n\n\n\ntransformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下：#### ModelScope 模型推理\n\n\n\nModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可：#### vLLM 推理加速\n\n\n\n活字3.0支持通过vLLM实现推理加速，示例代码如下：#### 部署 OpenAI API Server\n\n\n\n活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。\n\n\n环境准备：\n\n\n启动服务：\n\n\n使用OpenAI API发送请求：\n\n\n下面是一个使用OpenAI API + Gradio + 流式生成的示例代码：### 量化推理\n\n\n活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量：#### GGUF 格式\n\n\n\nGGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。\n\n\n您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。##### Step 1 环境准备\n\n\n首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理：\n\n\n您也可以下载最新版本的llama.cpp源码：\n\n\n然后需要进行编译。根据您的硬件平台，编译命令有细微差异：##### Step 2 格式转换（可选）\n\n\n以下命令需要在'URL'目录下：##### Step 3 开始推理\n\n\n以下命令需要在'URL'目录下：\n\n\n'-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型：\n\n\n关于'main'的更多参数，可以参考llama.cpp的官方文档。#### AWQ 格式\n\n\n\nAWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。##### Step 1 格式转换（可选）" ]
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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. --> # roberta-large-squad-model1 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 64 - eval_batch_size: 16 - seed: 83 - 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": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-squad-model1", "results": []}]}
question-answering
varun-v-rao/roberta-large-squad-model1
[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:roberta-large", "license:mit", "endpoints_compatible", "region:us" ]
2024-02-08T08:29:10+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us
# roberta-large-squad-model1 This model is a fine-tuned version of roberta-large 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: 64 - eval_batch_size: 16 - seed: 83 - 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
[ "# roberta-large-squad-model1\n\nThis model is a fine-tuned version of roberta-large 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: 64\n- eval_batch_size: 16\n- seed: 83\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 #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us \n", "# roberta-large-squad-model1\n\nThis model is a fine-tuned version of roberta-large 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: 64\n- eval_batch_size: 16\n- seed: 83\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" ]
[ 70, 34, 6, 12, 8, 3, 90, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #endpoints_compatible #region-us \n# roberta-large-squad-model1\n\nThis model is a fine-tuned version of roberta-large 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: 64\n- eval_batch_size: 16\n- seed: 83\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
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
mertllc/mms-tts-tur-fifties_female
[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T08:29:32+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #vits #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 #vits #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 #vits #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
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{"library_name": "transformers", "tags": []}
automatic-speech-recognition
SiRoZaRuPa/EN-300m-clean-0208
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T08:31:59+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" ]
[ 51, 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 #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
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description Quantization of the model Galactica-6.7b-evol-instruct in 4 bits using GPTQ. - **Quantizated with:** GPTQ ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k - **Paper [optional]:** https://galactica.org/static/paper.pdf - **Demo :** https://github.com/paperswithcode/galai/blob/main/docs/model_card.md ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
text-generation
EtienneDu91/galactica-6.7b-evol-instruct-70k-GPTQ-4b
[ "transformers", "safetensors", "opt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T08:32:21+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #opt #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 Quantization of the model Galactica-6.7b-evol-instruct in 4 bits using GPTQ. - Quantizated with: GPTQ ### Model Sources [optional] - Repository: GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k - Paper [optional]: URL - Demo : URL ## 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\nQuantization of the model Galactica-6.7b-evol-instruct in 4 bits using GPTQ.\n\n- Quantizated with: GPTQ", "### Model Sources [optional]\n\n\n\n- Repository: GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k\n- Paper [optional]: URL\n- Demo : URL", "## 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 #opt #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\nQuantization of the model Galactica-6.7b-evol-instruct in 4 bits using GPTQ.\n\n- Quantizated with: GPTQ", "### Model Sources [optional]\n\n\n\n- Repository: GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k\n- Paper [optional]: URL\n- Demo : URL", "## 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 #opt #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\nQuantization of the model Galactica-6.7b-evol-instruct in 4 bits using GPTQ.\n\n- Quantizated with: GPTQ### Model Sources [optional]\n\n\n\n- Repository: GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k\n- Paper [optional]: URL\n- Demo : URL## 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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<!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <div align="center"> <h1> <img src="image/huozi-logo.jpg" width="30" /> 活字通用大模型 </h1> </div> </p> <div align="center"> <a href="https://github.com/HIT-SCIR/huozi/pulls"> <image src="https://img.shields.io/badge/PRs-welcome-brightgreen"> </a> <a href="https://github.com/HIT-SCIR/huozi/pulls"> <image src="https://img.shields.io/badge/License-Apache_2.0-green.svg"> </a> <!-- <h4 align="center"> <p> <b>中文</b> | <a href="https://github.com/HIT-SCIR/huozi/blob/main/README_EN.md">English</a> <p> </h4> --> </div> ## 🔖 目录 |章节|说明| |---|---| |[💁🏻‍♂ 开源清单](#-开源清单)|本仓库开源项目清单| |[💡 模型介绍](#-模型介绍)|简要介绍活字模型结构和训练过程| |[📥 模型下载](#-模型下载)|活字模型下载链接| |[💻 模型推理](#-模型推理)|活字模型推理样例，包括vLLM推理加速、llama.cpp量化推理等框架的使用流程| |[📈 模型性能](#-模型性能)|活字模型在主流评测任务上的性能| |[🗂 生成样例](#-生成样例)|活字模型实际生成效果样例| ## 💁🏻‍♂ 开源清单 ![](image/models-v3.png) - **活字 3.0**: [[模型权重](#-模型下载)] - 活字3.0为一个稀疏混合专家模型，支持32K上下文，具有丰富的中、英文知识和强大的数学推理、代码生成能力。活字3.0较旧版活字具有更强的指令遵循能力和安全性。 - **中文MT-Bench**: [[数据集](data/mt-bench-zh/)] - 本数据集是英文MT-Bench对话能力评测数据集的中文版。它包含了一系列多轮对话问题，每一组问题都经过了精心的人工校对，并为适应中文语境进行了必要的调整。 - **《ChatGPT 调研报告》**: [[PDF](https://github.com/HIT-SCIR/huozi/blob/main/pdf/chatgpt_book.pdf)] - 哈工大自然语言处理研究所组织多位老师和同学撰写了本调研报告，从技术原理、应用场景、未来发展等方面对ChatGPT进行了尽量详尽的介绍及总结。 - **活字 2.0**: [[模型权重](https://huggingface.co/HIT-SCIR/huozi-7b-rlhf)] [[RLHF数据](data/huozi-rlhf/huozi_rlhf_data.csv)] - 在活字1.0基础上，通过人类反馈的强化学习（RLHF）进一步优化了模型回复质量，使其更加符合人类偏好。相较于上一个版本平均长度明显提高，遵从指令的能力更强，逻辑更加清晰。 - 16.9k 人工标注的偏好数据，回复来自活字模型，可以用于训练奖励模型。 - **活字 1.0**: [[模型权重](https://huggingface.co/HIT-SCIR/huozi-7b-sft)] - 在Bloom模型的基础上，在大约 150 亿 tokens 上进行指令微调训练得到的模型，具有更强的指令遵循能力、更好的安全性。 ## 💡 模型介绍 大规模语言模型（LLM）在自然语言处理领域取得了显著的进展，并在广泛的应用场景中展现了其强大的潜力。这一技术不仅吸引了学术界的广泛关注，也成为了工业界的热点。在此背景下，哈尔滨工业大学社会计算与信息检索研究中心（HIT-SCIR）近期推出了最新成果——**活字3.0**，致力于为自然语言处理的研究和实际应用提供更多可能性和选择。 活字3.0是基于Chinese-Mixtral-8x7B，在大约30万行指令数据上微调得到的模型。该模型支持**32K上下文**，能够有效处理长文本。活字3.0继承了基座模型丰富的**中英文知识**，并在**数学推理**、**代码生成**等任务上具有强大性能。经过指令微调，活字3.0还在**指令遵循能力**和**安全性**方面实现了显著提升。 此外，我们开源了**中文MT-Bench数据集**。这是一个中文开放问题集，包括80组对话任务，用于评估模型的多轮对话和指令遵循能力。该数据集是根据原始MT-Bench翻译得来的，每组问题均经过人工校对和中文语境下的适当调整。我们还对原始MT-Bench中的部分错误答案进行了修正。 > [!IMPORTANT] > 活字系列模型仍然可能生成包含事实性错误的误导性回复或包含偏见/歧视的有害内容，请谨慎鉴别和使用生成的内容，请勿将生成的有害内容传播至互联网。 ### 模型结构 活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。 ![](image/smoe.png) ### 训练过程 由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为[Chinese-Mixtral-8x7B](https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B)，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。 ## 📥 模型下载 |模型名称|文件大小|下载地址|备注| |:---:|:---:|:---:|:---:| |huozi3|88GB|[🤗HuggingFace](https://huggingface.co/HIT-SCIR/huozi3)<br>[ModelScope](https://modelscope.cn/models/HIT-SCIR/huozi3/summary)|活字3.0 完整模型| |huozi3-gguf|25GB|[🤗HuggingFace](https://huggingface.co/HIT-SCIR/huozi3-gguf)<br>[ModelScope](https://modelscope.cn/models/HIT-SCIR/huozi3-gguf/summary)|活字3.0 GGUF版本，适用于llama.cpp等推理框架| |huozi3-awq|24GB|[🤗HuggingFace](https://huggingface.co/HIT-SCIR/huozi3-awq)<br>[ModelScope](https://modelscope.cn/models/HIT-SCIR/huozi3-awq/summary)|活字3.0 AWQ版本，适用于AutoAWQ等推理框架| 如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考[此处训练代码](https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B?tab=readme-ov-file#%E5%BE%AE%E8%B0%83)。 ## 💻 模型推理 ### Quick Start 活字3.0采用ChatML格式的prompt模板，格式为： ``` <|beginofutterance|>系统 {system prompt}<|endofutterance|> <|beginofutterance|>用户 {input}<|endofutterance|> <|beginofutterance|>助手 {output}<|endofutterance|> ``` 使用活字3.0进行推理的示例代码如下： ```python # quickstart.py import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "HIT-SCIR/huozi3" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", ) text = """<|beginofutterance|>系统 你是一个智能助手<|endofutterance|> <|beginofutterance|>用户 请你用python写一段快速排序的代码<|endofutterance|> <|beginofutterance|>助手 """ inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate( **inputs, eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) print(tokenizer.decode(outputs[0], skip_special_tokens=False)) ``` 活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、llama.cpp、AutoAWQ、Text generation web UI等框架。 如果您在下载模型时遇到网络问题，可以使用我们在[ModelScope](#modelscope-模型推理)上提供的检查点。 <details> <summary> #### Transformers 模型推理 + 流式生成 </summary> transformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下： ```python # example/transformers-stream/stream.py import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_id = "HIT-SCIR/huozi3" model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.chat_template = """{% for message in messages %}{{'<|beginofutterance|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|endofutterance|>' + '\n'}}{% endif %}{% endfor %} {% if add_generation_prompt and messages[-1]['role'] != '助手' %}{{ '<|beginofutterance|>助手\n' }}{% endif %}""" chat = [ {"role": "系统", "content": "你是一个智能助手"}, {"role": "用户", "content": "请你用python写一段快速排序的代码"}, ] inputs = tokenizer.apply_chat_template( chat, tokenize=True, add_generation_prompt=True, return_tensors="pt", ).to(0) stream_output = model.generate( inputs, streamer=TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True), eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) ``` </details> <details> <summary> #### ModelScope 模型推理 </summary> ModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可： ```diff # example/modelscope-generate/generate.py import torch - from transformers import AutoModelForCausalLM, AutoTokenizer + from modelscope import AutoTokenizer, AutoModelForCausalLM model_id = "HIT-SCIR/huozi3" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", ) text = """<|beginofutterance|>系统 你是一个智能助手<|endofutterance|> <|beginofutterance|>用户 请你用python写一段快速排序的代码<|endofutterance|> <|beginofutterance|>助手 """ inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate( **inputs, eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) print(tokenizer.decode(outputs[0], skip_special_tokens=False)) ``` </details> <details> <summary> #### vLLM 推理加速 </summary> 活字3.0支持通过vLLM实现推理加速，示例代码如下： ```python # example/vllm-generate/generate.py from vllm import LLM, SamplingParams prompts = [ """<|beginofutterance|>系统 你是一个智能助手<|endofutterance|> <|beginofutterance|>用户 请你用python写一段快速排序的代码<|endofutterance|> <|beginofutterance|>助手 """, ] sampling_params = SamplingParams( temperature=0.8, top_p=0.95, stop_token_ids=[57001], max_tokens=2048 ) llm = LLM( model="HIT-SCIR/huozi3", tensor_parallel_size=4, ) outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(generated_text) ``` </details> <details> <summary> #### 部署 OpenAI API Server </summary> 活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。 环境准备： ```shell $ pip install vllm openai ``` 启动服务： ```shell $ python -m vllm.entrypoints.openai.api_server --model /path/to/huozi3/checkpoint --served-model-name huozi --chat-template template.jinja --tensor-parallel-size 8 --response-role 助手 --max-model-len 2048 ``` 使用OpenAI API发送请求： ```python # example/openai-api/openai-client.py from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="huozi", messages=[ {"role": "系统", "content": "你是一个智能助手"}, {"role": "用户", "content": "请你用python写一段快速排序的代码"}, ], extra_body={"stop_token_ids": [57001]}, ) print("Chat response:", chat_response.choices[0].message.content) ``` 下面是一个使用OpenAI API + Gradio + 流式生成的示例代码： ```python # example/openai-api/openai-client-gradio.py from openai import OpenAI import gradio as gr openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) def predict(message, history): history_openai_format = [ {"role": "系统", "content": "你是一个智能助手"}, ] for human, assistant in history: history_openai_format.append({"role": "用户", "content": human}) history_openai_format.append({"role": "助手", "content": assistant}) history_openai_format.append({"role": "用户", "content": message}) models = client.models.list() stream = client.chat.completions.create( model=models.data[0].id, messages=history_openai_format, temperature=0.8, stream=True, extra_body={"repetition_penalty": 1, "stop_token_ids": [57001]}, ) partial_message = "" for chunk in stream: partial_message += chunk.choices[0].delta.content or "" yield partial_message gr.ChatInterface(predict).queue().launch() ``` </details> ### 量化推理 活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量： |量化方法|显存占用| |:---:|:---:| |无|95GB| |AWQ|32GB| |GGUF(q4_0)|28GB| |GGUF(q2_k)|18GB| |GGUF(q2_k, offload 16层)|9.6GB| <details> <summary> #### GGUF 格式 </summary> GGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了[GGUF格式的活字3.0](https://huggingface.co/HIT-SCIR/huozi3-gguf)。 您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。 ##### Step 1 环境准备 首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理： ```shell $ git clone --recurse-submodules https://github.com/HIT-SCIR/huozi $ cd examples/llama.cpp ``` 您也可以下载最新版本的llama.cpp源码： ```shell $ git clone https://github.com/ggerganov/llama.cpp.git $ cd llama.cpp ``` 然后需要进行编译。根据您的硬件平台，编译命令有细微差异： ```shell $ make # 用于纯CPU推理 $ make LLAMA_CUBLAS=1 # 用于GPU推理 $ LLAMA_METAL=1 make # 用于Apple Silicon，暂未经过测试 ``` ##### Step 2 格式转换（可选） 以下命令需要在`llama.cpp/`目录下： ```shell # 转换为GGUF格式 $ python convert.py --outfile /path/to/huozi-gguf/huozi3.gguf /path/to/huozi3 # 进行GGUF格式的q4_0量化 $ quantize /path/to/huozi-gguf/huozi3.gguf /path/to/huozi-gguf/huozi3-q4_0.gguf q4_0 ``` ##### Step 3 开始推理 以下命令需要在`llama.cpp/`目录下： ```shell $ main -m /path/to/huozi-gguf/huozi3-q4_0.gguf --color --interactive-first -c 2048 -t 6 --temp 0.2 --repeat_penalty 1.1 -ngl 999 --in-prefix "<|beginofutterance|>用户\n" --in-suffix "<|endofutterance|>\n<|beginofutterance|>助手" -r "<|endofutterance|>" ``` `-ngl`参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型： ```shell $ main -m /path/to/huozi-gguf/huozi3-q2_k.gguf --color --interactive-first -c 2048 -t 6 --temp 0.2 --repeat_penalty 1.1 -ngl 16 --in-prefix "<|beginofutterance|>用户\n" --in-suffix "<|endofutterance|>\n<|beginofutterance|>助手" -r "<|endofutterance|>" ``` 关于`main`的更多参数，可以参考llama.cpp的[官方文档](https://github.com/ggerganov/llama.cpp/tree/master/examples/main)。 </details> <details> <summary> #### AWQ 格式 </summary> AWQ是一种量化模型的存储格式。我们已经提供了[AWQ格式的活字3.0](https://huggingface.co/HIT-SCIR/huozi3-awq)，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。 ##### Step 1 格式转换（可选） ```python # example/autoawq-generate/quant.py from awq import AutoAWQForCausalLM from transformers import AutoTokenizer model_path = "/path/to/huozi3" quant_path = "/path/to/save/huozi3-awq" modules_to_not_convert = ["gate"] quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM", "modules_to_not_convert": modules_to_not_convert, } model = AutoAWQForCausalLM.from_pretrained( model_path, safetensors=True, **{"low_cpu_mem_usage": True}, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model.quantize( tokenizer, quant_config=quant_config, modules_to_not_convert=modules_to_not_convert, ) model.save_quantized(quant_path) tokenizer.save_pretrained(quant_path) print(f'Model is quantized and saved at "{quant_path}"') ``` ##### Step 2 开始推理 在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下： ```diff # example/autoawq-generate/generate.py import torch + from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer - model_id = "HIT-SCIR/huozi3" + model_id = "HIT-SCIR/huozi3-awq" # or model_id = "/path/to/saved/huozi3-awq" + model = AutoAWQForCausalLM.from_quantized(model_id, fuse_layers=True) - model = AutoModelForCausalLM.from_pretrained( - model_id, - attn_implementation="flash_attention_2", - torch_dtype=torch.bfloat16, - device_map="auto", - ) tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.chat_template = """{% for message in messages %}{{'<|beginofutterance|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|endofutterance|>' + '\n'}}{% endif %}{% endfor %} {% if add_generation_prompt and messages[-1]['role'] != '助手' %}{{ '<|beginofutterance|>助手\n' }}{% endif %}""" chat = [ {"role": "系统", "content": "你是一个智能助手"}, {"role": "用户", "content": "请你用python写一段快速排序的代码"}, ] inputs = tokenizer.apply_chat_template( chat, tokenize=True, add_generation_prompt=True, return_tensors="pt", ).to(0) stream_output = model.generate( inputs, streamer=TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True), eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) ``` </details> ## 📈 模型性能 ![](image/metric-v3-h.png) 针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测： - C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。 - CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。 - GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。 - MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。 - HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。 - GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。 - HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。 - MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。 - MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在[此处](data/mt-bench-zh/)开源MT-Bench-zh数据集。 - MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。 活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果： <!-- | 模型名称 | 模型结构 | C-Eval<br>(中文) | CMMLU<br>(中文) | GAOKAO<br>(中文) | MT-Bench-zh<br>(中文对话) | MT-Bench-safety<br>(中文安全) | MMLU<br>(英文) | HellaSwag<br>(英文) | MT-Bench<br>(英文对话) | GSM8K<br>(数学) | HumanEval<br>(代码) | |---------------------------------------------|---------|--------------|-------------|---------------|--------------------------|-----------------------------|------------|------------------|-----------------------|-------------|-----------------| | baichuan-inc/Baichuan2-13B-Chat v2 | Baichuan| 56.13 | 58.50 | 48.99 | 6.74 | 8.30 | 54.50 | 51.19 | 6.59 | 25.17 | 20.12 | | wangrongsheng/Aurora-Plus | Mixtral | 47.67 | 48.75 | 35.05 | 5.47 | 6.70 | 67.80 | 78.27 | 7.13 | 66.26 | 27.44 | | TigerResearch/tigerbot-13b-chat-v5 | LLaMA | 49.78 | 51.28 | 41.31 | 5.98 | 7.63 | 56.34 | 35.17 | 4.88 | 66.19 | 14.63 | | hfl/chinese-alpaca-2-13b | LLaMA | 43.47 | 44.53 | 25.94 | 5.77 | 8.13 | 53.05 | 56.85 | 6.24 | 32.75 | 14.02 | | 活字1.0 | BLOOM | 37.27 | 36.24 | 19.72 | 4.48 | 7.18 | 39.68 | 33.21 | 4.34 | 21.99 | 13.41 | | 活字2.0 | BLOOM | 32.05 | 34.68 | 22.97 | 5.08 | 6.68 | 38.04 | 33.34 | 4.79 | 19.86 | 6.71 | | **活字3.0（最新版本）** | Mixtral | 51.82 | 51.06 | 41.21 | 6.29 | 7.58 | 69.48 | 65.18 | 7.62 | 65.81 | 40.85 | --> ![](image/evaluation-v3.png) > 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。 > > 我们使用OpenCompass作为评测框架，commit hash为[4c87e77](https://github.com/open-compass/opencompass/tree/4c87e777d855636b9eda7ec87bcbbf12b62caed3)。评测代码位于[此处](./evaluate/)。 根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。 ## 🗂 生成样例 下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比： ![](image/examples/v3-case1.png) ![](image/examples/v3-case2.png) ![](image/examples/v3-case3.png) ![](image/examples/v3-case4.png) ![](image/examples/v3-case5.png) ## <img src="https://cdn.jsdelivr.net/gh/LightChen233/blog-img/folders.png" width="25" /> 开源协议 对本仓库源码的使用遵循开源许可协议 [Apache 2.0](https://github.com/HIT-SCIR/huozi/blob/main/LICENSE)。 活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：<[email protected]>。 ## <img src="https://cdn.jsdelivr.net/gh/LightChen233/blog-img/notes.png" width="25" /> Citation ### 活字大模型 ```latex @misc{huozi, author = {Huozi-Team}. title = {Huozi: Leveraging Large Language Models for Enhanced Open-Domain Chatting} year = {2024}, publisher = {GitHub}, journal = {GitHub repository} howpublished = {\url{https://github.com/HIT-SCIR/huozi}} } ``` ## <img src="https://cdn.jsdelivr.net/gh/LightChen233/blog-img/star.png" width="25" /> Star History [![Star History Chart](https://api.star-history.com/svg?repos=HIT-SCIR/huozi&type=Date)](https://star-history.com/#HIT-SCIR/huozi&Date)
{}
null
HIT-SCIR/huozi3-gguf
[ "gguf", "region:us" ]
2024-02-08T08:33:16+00:00
[]
[]
TAGS #gguf #region-us
活字通用大模型 ========= 目录 -- ‍ 开源清单 ------ ![](image/URL) * 活字 3.0: [模型权重] + 活字3.0为一个稀疏混合专家模型，支持32K上下文，具有丰富的中、英文知识和强大的数学推理、代码生成能力。活字3.0较旧版活字具有更强的指令遵循能力和安全性。 * 中文MT-Bench: [数据集] + 本数据集是英文MT-Bench对话能力评测数据集的中文版。它包含了一系列多轮对话问题，每一组问题都经过了精心的人工校对，并为适应中文语境进行了必要的调整。 * 《ChatGPT 调研报告》: [PDF] + 哈工大自然语言处理研究所组织多位老师和同学撰写了本调研报告，从技术原理、应用场景、未来发展等方面对ChatGPT进行了尽量详尽的介绍及总结。 * 活字 2.0: [模型权重] [RLHF数据] + 在活字1.0基础上，通过人类反馈的强化学习（RLHF）进一步优化了模型回复质量，使其更加符合人类偏好。相较于上一个版本平均长度明显提高，遵从指令的能力更强，逻辑更加清晰。 + 16.9k 人工标注的偏好数据，回复来自活字模型，可以用于训练奖励模型。 * 活字 1.0: [模型权重] + 在Bloom模型的基础上，在大约 150 亿 tokens 上进行指令微调训练得到的模型，具有更强的指令遵循能力、更好的安全性。 模型介绍 ---- 大规模语言模型（LLM）在自然语言处理领域取得了显著的进展，并在广泛的应用场景中展现了其强大的潜力。这一技术不仅吸引了学术界的广泛关注，也成为了工业界的热点。在此背景下，哈尔滨工业大学社会计算与信息检索研究中心（HIT-SCIR）近期推出了最新成果——活字3.0，致力于为自然语言处理的研究和实际应用提供更多可能性和选择。 活字3.0是基于Chinese-Mixtral-8x7B，在大约30万行指令数据上微调得到的模型。该模型支持32K上下文，能够有效处理长文本。活字3.0继承了基座模型丰富的中英文知识，并在数学推理、代码生成等任务上具有强大性能。经过指令微调，活字3.0还在指令遵循能力和安全性方面实现了显著提升。 此外，我们开源了中文MT-Bench数据集。这是一个中文开放问题集，包括80组对话任务，用于评估模型的多轮对话和指令遵循能力。该数据集是根据原始MT-Bench翻译得来的，每组问题均经过人工校对和中文语境下的适当调整。我们还对原始MT-Bench中的部分错误答案进行了修正。 > > [!IMPORTANT] > 活字系列模型仍然可能生成包含事实性错误的误导性回复或包含偏见/歧视的有害内容，请谨慎鉴别和使用生成的内容，请勿将生成的有害内容传播至互联网。 > > > ### 模型结构 活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。 ![](image/URL) ### 训练过程 由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。 模型下载 ---- 如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。 模型推理 ---- ### Quick Start 活字3.0采用ChatML格式的prompt模板，格式为： 使用活字3.0进行推理的示例代码如下： 活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。 如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。 #### Transformers 模型推理 + 流式生成 transformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下： #### ModelScope 模型推理 ModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可： #### vLLM 推理加速 活字3.0支持通过vLLM实现推理加速，示例代码如下： #### 部署 OpenAI API Server 活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。 环境准备： 启动服务： 使用OpenAI API发送请求： 下面是一个使用OpenAI API + Gradio + 流式生成的示例代码： ### 量化推理 活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量： #### GGUF 格式 GGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。 您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。 ##### Step 1 环境准备 首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理： 您也可以下载最新版本的llama.cpp源码： 然后需要进行编译。根据您的硬件平台，编译命令有细微差异： ##### Step 2 格式转换（可选） 以下命令需要在'URL'目录下： ##### Step 3 开始推理 以下命令需要在'URL'目录下： '-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型： 关于'main'的更多参数，可以参考llama.cpp的官方文档。 #### AWQ 格式 AWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。 ##### Step 1 格式转换（可选） ##### Step 2 开始推理 在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下： 模型性能 ---- ![](image/URL) 针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测： * C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。 * CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。 * GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。 * MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。 * HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。 * GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。 * HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。 * MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。 * MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在此处开源MT-Bench-zh数据集。 * MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。 活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果： ![](image/URL) > > 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。 > > > 我们使用OpenCompass作为评测框架，commit hash为4c87e77。评测代码位于此处。 > > > 根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。 生成样例 ---- 下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比： ![](image/examples/URL) ![](image/examples/URL) ![](image/examples/URL) ![](image/examples/URL) ![](image/examples/URL) <img src="URL width="25" /> 开源协议 -------------------------------- 对本仓库源码的使用遵循开源许可协议 Apache 2.0。 活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：[jngao@URL](mailto:jngao@URL)。 <img src="URL width="25" /> Citation ------------------------------------ ### 活字大模型 <img src="URL width="25" /> Star History ---------------------------------------- ![Star History Chart](URL
[ "### 模型结构\n\n\n活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。\n\n\n![](image/URL)", "### 训练过程\n\n\n由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。\n\n\n模型下载\n----\n\n\n\n如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。\n\n\n模型推理\n----", "### Quick Start\n\n\n活字3.0采用ChatML格式的prompt模板，格式为：\n\n\n使用活字3.0进行推理的示例代码如下：\n\n\n活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。\n\n\n如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。", "#### Transformers 模型推理 + 流式生成\n\n\n\ntransformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下：", "#### ModelScope 模型推理\n\n\n\nModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可：", "#### vLLM 推理加速\n\n\n\n活字3.0支持通过vLLM实现推理加速，示例代码如下：", "#### 部署 OpenAI API Server\n\n\n\n活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。\n\n\n环境准备：\n\n\n启动服务：\n\n\n使用OpenAI API发送请求：\n\n\n下面是一个使用OpenAI API + Gradio + 流式生成的示例代码：", "### 量化推理\n\n\n活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量：", "#### GGUF 格式\n\n\n\nGGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。\n\n\n您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。", "##### Step 1 环境准备\n\n\n首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理：\n\n\n您也可以下载最新版本的llama.cpp源码：\n\n\n然后需要进行编译。根据您的硬件平台，编译命令有细微差异：", "##### Step 2 格式转换（可选）\n\n\n以下命令需要在'URL'目录下：", "##### Step 3 开始推理\n\n\n以下命令需要在'URL'目录下：\n\n\n'-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型：\n\n\n关于'main'的更多参数，可以参考llama.cpp的官方文档。", "#### AWQ 格式\n\n\n\nAWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。", "##### Step 1 格式转换（可选）", "##### Step 2 开始推理\n\n\n在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下：\n\n\n\n模型性能\n----\n\n\n![](image/URL)\n\n\n针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测：\n\n\n* C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。\n* CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。\n* GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。\n* MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。\n* HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。\n* GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。\n* HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。\n* MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。\n* MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在此处开源MT-Bench-zh数据集。\n* MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。\n\n\n活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果：\n\n\n![](image/URL)\n\n\n\n> \n> 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。\n> \n> \n> 我们使用OpenCompass作为评测框架，commit hash为4c87e77。评测代码位于此处。\n> \n> \n> \n\n\n根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。\n\n\n生成样例\n----\n\n\n下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比：\n\n\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n\n\n<img src=\"URL width=\"25\" /> 开源协议\n--------------------------------\n\n\n对本仓库源码的使用遵循开源许可协议 Apache 2.0。\n\n\n活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：[jngao@URL](mailto:jngao@URL)。\n\n\n<img src=\"URL width=\"25\" /> Citation\n------------------------------------", "### 活字大模型\n\n\n<img src=\"URL width=\"25\" /> Star History\n----------------------------------------\n\n\n![Star History Chart](URL" ]
[ "TAGS\n#gguf #region-us \n", "### 模型结构\n\n\n活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。\n\n\n![](image/URL)", "### 训练过程\n\n\n由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。\n\n\n模型下载\n----\n\n\n\n如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。\n\n\n模型推理\n----", "### Quick Start\n\n\n活字3.0采用ChatML格式的prompt模板，格式为：\n\n\n使用活字3.0进行推理的示例代码如下：\n\n\n活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。\n\n\n如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。", "#### Transformers 模型推理 + 流式生成\n\n\n\ntransformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下：", "#### ModelScope 模型推理\n\n\n\nModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可：", "#### vLLM 推理加速\n\n\n\n活字3.0支持通过vLLM实现推理加速，示例代码如下：", "#### 部署 OpenAI API Server\n\n\n\n活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。\n\n\n环境准备：\n\n\n启动服务：\n\n\n使用OpenAI API发送请求：\n\n\n下面是一个使用OpenAI API + Gradio + 流式生成的示例代码：", "### 量化推理\n\n\n活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量：", "#### GGUF 格式\n\n\n\nGGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。\n\n\n您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。", "##### Step 1 环境准备\n\n\n首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理：\n\n\n您也可以下载最新版本的llama.cpp源码：\n\n\n然后需要进行编译。根据您的硬件平台，编译命令有细微差异：", "##### Step 2 格式转换（可选）\n\n\n以下命令需要在'URL'目录下：", "##### Step 3 开始推理\n\n\n以下命令需要在'URL'目录下：\n\n\n'-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型：\n\n\n关于'main'的更多参数，可以参考llama.cpp的官方文档。", "#### AWQ 格式\n\n\n\nAWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。", "##### Step 1 格式转换（可选）", "##### Step 2 开始推理\n\n\n在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下：\n\n\n\n模型性能\n----\n\n\n![](image/URL)\n\n\n针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测：\n\n\n* C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。\n* CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。\n* GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。\n* MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。\n* HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。\n* GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。\n* HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。\n* MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。\n* MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在此处开源MT-Bench-zh数据集。\n* MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。\n\n\n活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果：\n\n\n![](image/URL)\n\n\n\n> \n> 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。\n> \n> \n> 我们使用OpenCompass作为评测框架，commit hash为4c87e77。评测代码位于此处。\n> \n> \n> \n\n\n根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。\n\n\n生成样例\n----\n\n\n下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比：\n\n\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n\n\n<img src=\"URL width=\"25\" /> 开源协议\n--------------------------------\n\n\n对本仓库源码的使用遵循开源许可协议 Apache 2.0。\n\n\n活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：[jngao@URL](mailto:jngao@URL)。\n\n\n<img src=\"URL width=\"25\" /> Citation\n------------------------------------", "### 活字大模型\n\n\n<img src=\"URL width=\"25\" /> Star History\n----------------------------------------\n\n\n![Star History Chart](URL" ]
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[ "passage: TAGS\n#gguf #region-us \n### 模型结构\n\n\n活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。\n\n\n![](image/URL)### 训练过程\n\n\n由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。\n\n\n模型下载\n----\n\n\n\n如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。\n\n\n模型推理\n----### Quick Start\n\n\n活字3.0采用ChatML格式的prompt模板，格式为：\n\n\n使用活字3.0进行推理的示例代码如下：\n\n\n活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。\n\n\n如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。#### Transformers 模型推理 + 流式生成\n\n\n\ntransformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下：", "passage: #### ModelScope 模型推理\n\n\n\nModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可：#### vLLM 推理加速\n\n\n\n活字3.0支持通过vLLM实现推理加速，示例代码如下：#### 部署 OpenAI API Server\n\n\n\n活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。\n\n\n环境准备：\n\n\n启动服务：\n\n\n使用OpenAI API发送请求：\n\n\n下面是一个使用OpenAI API + Gradio + 流式生成的示例代码：### 量化推理\n\n\n活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量：#### GGUF 格式\n\n\n\nGGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。\n\n\n您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。##### Step 1 环境准备\n\n\n首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理：\n\n\n您也可以下载最新版本的llama.cpp源码：\n\n\n然后需要进行编译。根据您的硬件平台，编译命令有细微差异：##### Step 2 格式转换（可选）\n\n\n以下命令需要在'URL'目录下：##### Step 3 开始推理\n\n\n以下命令需要在'URL'目录下：\n\n\n'-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型：\n\n\n关于'main'的更多参数，可以参考llama.cpp的官方文档。#### AWQ 格式\n\n\n\nAWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。##### Step 1 格式转换（可选）" ]
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transformers
<!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <div align="center"> <h1> <img src="image/huozi-logo.jpg" width="30" /> 活字通用大模型 </h1> </div> </p> <div align="center"> <a href="https://github.com/HIT-SCIR/huozi/pulls"> <image src="https://img.shields.io/badge/PRs-welcome-brightgreen"> </a> <a href="https://github.com/HIT-SCIR/huozi/pulls"> <image src="https://img.shields.io/badge/License-Apache_2.0-green.svg"> </a> <!-- <h4 align="center"> <p> <b>中文</b> | <a href="https://github.com/HIT-SCIR/huozi/blob/main/README_EN.md">English</a> <p> </h4> --> </div> ## 🔖 目录 |章节|说明| |---|---| |[💁🏻‍♂ 开源清单](#-开源清单)|本仓库开源项目清单| |[💡 模型介绍](#-模型介绍)|简要介绍活字模型结构和训练过程| |[📥 模型下载](#-模型下载)|活字模型下载链接| |[💻 模型推理](#-模型推理)|活字模型推理样例，包括vLLM推理加速、llama.cpp量化推理等框架的使用流程| |[📈 模型性能](#-模型性能)|活字模型在主流评测任务上的性能| |[🗂 生成样例](#-生成样例)|活字模型实际生成效果样例| ## 💁🏻‍♂ 开源清单 ![](image/models-v3.png) - **活字 3.0**: [[模型权重](#-模型下载)] - 活字3.0为一个稀疏混合专家模型，支持32K上下文，具有丰富的中、英文知识和强大的数学推理、代码生成能力。活字3.0较旧版活字具有更强的指令遵循能力和安全性。 - **中文MT-Bench**: [[数据集](data/mt-bench-zh/)] - 本数据集是英文MT-Bench对话能力评测数据集的中文版。它包含了一系列多轮对话问题，每一组问题都经过了精心的人工校对，并为适应中文语境进行了必要的调整。 - **《ChatGPT 调研报告》**: [[PDF](https://github.com/HIT-SCIR/huozi/blob/main/pdf/chatgpt_book.pdf)] - 哈工大自然语言处理研究所组织多位老师和同学撰写了本调研报告，从技术原理、应用场景、未来发展等方面对ChatGPT进行了尽量详尽的介绍及总结。 - **活字 2.0**: [[模型权重](https://huggingface.co/HIT-SCIR/huozi-7b-rlhf)] [[RLHF数据](data/huozi-rlhf/huozi_rlhf_data.csv)] - 在活字1.0基础上，通过人类反馈的强化学习（RLHF）进一步优化了模型回复质量，使其更加符合人类偏好。相较于上一个版本平均长度明显提高，遵从指令的能力更强，逻辑更加清晰。 - 16.9k 人工标注的偏好数据，回复来自活字模型，可以用于训练奖励模型。 - **活字 1.0**: [[模型权重](https://huggingface.co/HIT-SCIR/huozi-7b-sft)] - 在Bloom模型的基础上，在大约 150 亿 tokens 上进行指令微调训练得到的模型，具有更强的指令遵循能力、更好的安全性。 ## 💡 模型介绍 大规模语言模型（LLM）在自然语言处理领域取得了显著的进展，并在广泛的应用场景中展现了其强大的潜力。这一技术不仅吸引了学术界的广泛关注，也成为了工业界的热点。在此背景下，哈尔滨工业大学社会计算与信息检索研究中心（HIT-SCIR）近期推出了最新成果——**活字3.0**，致力于为自然语言处理的研究和实际应用提供更多可能性和选择。 活字3.0是基于Chinese-Mixtral-8x7B，在大约30万行指令数据上微调得到的模型。该模型支持**32K上下文**，能够有效处理长文本。活字3.0继承了基座模型丰富的**中英文知识**，并在**数学推理**、**代码生成**等任务上具有强大性能。经过指令微调，活字3.0还在**指令遵循能力**和**安全性**方面实现了显著提升。 此外，我们开源了**中文MT-Bench数据集**。这是一个中文开放问题集，包括80组对话任务，用于评估模型的多轮对话和指令遵循能力。该数据集是根据原始MT-Bench翻译得来的，每组问题均经过人工校对和中文语境下的适当调整。我们还对原始MT-Bench中的部分错误答案进行了修正。 > [!IMPORTANT] > 活字系列模型仍然可能生成包含事实性错误的误导性回复或包含偏见/歧视的有害内容，请谨慎鉴别和使用生成的内容，请勿将生成的有害内容传播至互联网。 ### 模型结构 活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。 ![](image/smoe.png) ### 训练过程 由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为[Chinese-Mixtral-8x7B](https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B)，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。 ## 📥 模型下载 |模型名称|文件大小|下载地址|备注| |:---:|:---:|:---:|:---:| |huozi3|88GB|[🤗HuggingFace](https://huggingface.co/HIT-SCIR/huozi3)<br>[ModelScope](https://modelscope.cn/models/HIT-SCIR/huozi3/summary)|活字3.0 完整模型| |huozi3-gguf|25GB|[🤗HuggingFace](https://huggingface.co/HIT-SCIR/huozi3-gguf)<br>[ModelScope](https://modelscope.cn/models/HIT-SCIR/huozi3-gguf/summary)|活字3.0 GGUF版本，适用于llama.cpp等推理框架| |huozi3-awq|24GB|[🤗HuggingFace](https://huggingface.co/HIT-SCIR/huozi3-awq)<br>[ModelScope](https://modelscope.cn/models/HIT-SCIR/huozi3-awq/summary)|活字3.0 AWQ版本，适用于AutoAWQ等推理框架| 如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考[此处训练代码](https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B?tab=readme-ov-file#%E5%BE%AE%E8%B0%83)。 ## 💻 模型推理 ### Quick Start 活字3.0采用ChatML格式的prompt模板，格式为： ``` <|beginofutterance|>系统 {system prompt}<|endofutterance|> <|beginofutterance|>用户 {input}<|endofutterance|> <|beginofutterance|>助手 {output}<|endofutterance|> ``` 使用活字3.0进行推理的示例代码如下： ```python # quickstart.py import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "HIT-SCIR/huozi3" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", ) text = """<|beginofutterance|>系统 你是一个智能助手<|endofutterance|> <|beginofutterance|>用户 请你用python写一段快速排序的代码<|endofutterance|> <|beginofutterance|>助手 """ inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate( **inputs, eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) print(tokenizer.decode(outputs[0], skip_special_tokens=False)) ``` 活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、llama.cpp、AutoAWQ、Text generation web UI等框架。 如果您在下载模型时遇到网络问题，可以使用我们在[ModelScope](#modelscope-模型推理)上提供的检查点。 <details> <summary> #### Transformers 模型推理 + 流式生成 </summary> transformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下： ```python # example/transformers-stream/stream.py import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_id = "HIT-SCIR/huozi3" model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.chat_template = """{% for message in messages %}{{'<|beginofutterance|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|endofutterance|>' + '\n'}}{% endif %}{% endfor %} {% if add_generation_prompt and messages[-1]['role'] != '助手' %}{{ '<|beginofutterance|>助手\n' }}{% endif %}""" chat = [ {"role": "系统", "content": "你是一个智能助手"}, {"role": "用户", "content": "请你用python写一段快速排序的代码"}, ] inputs = tokenizer.apply_chat_template( chat, tokenize=True, add_generation_prompt=True, return_tensors="pt", ).to(0) stream_output = model.generate( inputs, streamer=TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True), eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) ``` </details> <details> <summary> #### ModelScope 模型推理 </summary> ModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可： ```diff # example/modelscope-generate/generate.py import torch - from transformers import AutoModelForCausalLM, AutoTokenizer + from modelscope import AutoTokenizer, AutoModelForCausalLM model_id = "HIT-SCIR/huozi3" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", ) text = """<|beginofutterance|>系统 你是一个智能助手<|endofutterance|> <|beginofutterance|>用户 请你用python写一段快速排序的代码<|endofutterance|> <|beginofutterance|>助手 """ inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate( **inputs, eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) print(tokenizer.decode(outputs[0], skip_special_tokens=False)) ``` </details> <details> <summary> #### vLLM 推理加速 </summary> 活字3.0支持通过vLLM实现推理加速，示例代码如下： ```python # example/vllm-generate/generate.py from vllm import LLM, SamplingParams prompts = [ """<|beginofutterance|>系统 你是一个智能助手<|endofutterance|> <|beginofutterance|>用户 请你用python写一段快速排序的代码<|endofutterance|> <|beginofutterance|>助手 """, ] sampling_params = SamplingParams( temperature=0.8, top_p=0.95, stop_token_ids=[57001], max_tokens=2048 ) llm = LLM( model="HIT-SCIR/huozi3", tensor_parallel_size=4, ) outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(generated_text) ``` </details> <details> <summary> #### 部署 OpenAI API Server </summary> 活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。 环境准备： ```shell $ pip install vllm openai ``` 启动服务： ```shell $ python -m vllm.entrypoints.openai.api_server --model /path/to/huozi3/checkpoint --served-model-name huozi --chat-template template.jinja --tensor-parallel-size 8 --response-role 助手 --max-model-len 2048 ``` 使用OpenAI API发送请求： ```python # example/openai-api/openai-client.py from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="huozi", messages=[ {"role": "系统", "content": "你是一个智能助手"}, {"role": "用户", "content": "请你用python写一段快速排序的代码"}, ], extra_body={"stop_token_ids": [57001]}, ) print("Chat response:", chat_response.choices[0].message.content) ``` 下面是一个使用OpenAI API + Gradio + 流式生成的示例代码： ```python # example/openai-api/openai-client-gradio.py from openai import OpenAI import gradio as gr openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) def predict(message, history): history_openai_format = [ {"role": "系统", "content": "你是一个智能助手"}, ] for human, assistant in history: history_openai_format.append({"role": "用户", "content": human}) history_openai_format.append({"role": "助手", "content": assistant}) history_openai_format.append({"role": "用户", "content": message}) models = client.models.list() stream = client.chat.completions.create( model=models.data[0].id, messages=history_openai_format, temperature=0.8, stream=True, extra_body={"repetition_penalty": 1, "stop_token_ids": [57001]}, ) partial_message = "" for chunk in stream: partial_message += chunk.choices[0].delta.content or "" yield partial_message gr.ChatInterface(predict).queue().launch() ``` </details> ### 量化推理 活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量： |量化方法|显存占用| |:---:|:---:| |无|95GB| |AWQ|32GB| |GGUF(q4_0)|28GB| |GGUF(q2_k)|18GB| |GGUF(q2_k, offload 16层)|9.6GB| <details> <summary> #### GGUF 格式 </summary> GGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了[GGUF格式的活字3.0](https://huggingface.co/HIT-SCIR/huozi3-gguf)。 您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。 ##### Step 1 环境准备 首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理： ```shell $ git clone --recurse-submodules https://github.com/HIT-SCIR/huozi $ cd examples/llama.cpp ``` 您也可以下载最新版本的llama.cpp源码： ```shell $ git clone https://github.com/ggerganov/llama.cpp.git $ cd llama.cpp ``` 然后需要进行编译。根据您的硬件平台，编译命令有细微差异： ```shell $ make # 用于纯CPU推理 $ make LLAMA_CUBLAS=1 # 用于GPU推理 $ LLAMA_METAL=1 make # 用于Apple Silicon，暂未经过测试 ``` ##### Step 2 格式转换（可选） 以下命令需要在`llama.cpp/`目录下： ```shell # 转换为GGUF格式 $ python convert.py --outfile /path/to/huozi-gguf/huozi3.gguf /path/to/huozi3 # 进行GGUF格式的q4_0量化 $ quantize /path/to/huozi-gguf/huozi3.gguf /path/to/huozi-gguf/huozi3-q4_0.gguf q4_0 ``` ##### Step 3 开始推理 以下命令需要在`llama.cpp/`目录下： ```shell $ main -m /path/to/huozi-gguf/huozi3-q4_0.gguf --color --interactive-first -c 2048 -t 6 --temp 0.2 --repeat_penalty 1.1 -ngl 999 --in-prefix "<|beginofutterance|>用户\n" --in-suffix "<|endofutterance|>\n<|beginofutterance|>助手" -r "<|endofutterance|>" ``` `-ngl`参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型： ```shell $ main -m /path/to/huozi-gguf/huozi3-q2_k.gguf --color --interactive-first -c 2048 -t 6 --temp 0.2 --repeat_penalty 1.1 -ngl 16 --in-prefix "<|beginofutterance|>用户\n" --in-suffix "<|endofutterance|>\n<|beginofutterance|>助手" -r "<|endofutterance|>" ``` 关于`main`的更多参数，可以参考llama.cpp的[官方文档](https://github.com/ggerganov/llama.cpp/tree/master/examples/main)。 </details> <details> <summary> #### AWQ 格式 </summary> AWQ是一种量化模型的存储格式。我们已经提供了[AWQ格式的活字3.0](https://huggingface.co/HIT-SCIR/huozi3-awq)，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。 ##### Step 1 格式转换（可选） ```python # example/autoawq-generate/quant.py from awq import AutoAWQForCausalLM from transformers import AutoTokenizer model_path = "/path/to/huozi3" quant_path = "/path/to/save/huozi3-awq" modules_to_not_convert = ["gate"] quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM", "modules_to_not_convert": modules_to_not_convert, } model = AutoAWQForCausalLM.from_pretrained( model_path, safetensors=True, **{"low_cpu_mem_usage": True}, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model.quantize( tokenizer, quant_config=quant_config, modules_to_not_convert=modules_to_not_convert, ) model.save_quantized(quant_path) tokenizer.save_pretrained(quant_path) print(f'Model is quantized and saved at "{quant_path}"') ``` ##### Step 2 开始推理 在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下： ```diff # example/autoawq-generate/generate.py import torch + from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer - model_id = "HIT-SCIR/huozi3" + model_id = "HIT-SCIR/huozi3-awq" # or model_id = "/path/to/saved/huozi3-awq" + model = AutoAWQForCausalLM.from_quantized(model_id, fuse_layers=True) - model = AutoModelForCausalLM.from_pretrained( - model_id, - attn_implementation="flash_attention_2", - torch_dtype=torch.bfloat16, - device_map="auto", - ) tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.chat_template = """{% for message in messages %}{{'<|beginofutterance|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|endofutterance|>' + '\n'}}{% endif %}{% endfor %} {% if add_generation_prompt and messages[-1]['role'] != '助手' %}{{ '<|beginofutterance|>助手\n' }}{% endif %}""" chat = [ {"role": "系统", "content": "你是一个智能助手"}, {"role": "用户", "content": "请你用python写一段快速排序的代码"}, ] inputs = tokenizer.apply_chat_template( chat, tokenize=True, add_generation_prompt=True, return_tensors="pt", ).to(0) stream_output = model.generate( inputs, streamer=TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True), eos_token_id=57001, temperature=0.8, top_p=0.9, max_new_tokens=2048, ) ``` </details> ## 📈 模型性能 ![](image/metric-v3-h.png) 针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测： - C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。 - CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。 - GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。 - MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。 - HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。 - GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。 - HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。 - MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。 - MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在[此处](data/mt-bench-zh/)开源MT-Bench-zh数据集。 - MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。 活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果： <!-- | 模型名称 | 模型结构 | C-Eval<br>(中文) | CMMLU<br>(中文) | GAOKAO<br>(中文) | MT-Bench-zh<br>(中文对话) | MT-Bench-safety<br>(中文安全) | MMLU<br>(英文) | HellaSwag<br>(英文) | MT-Bench<br>(英文对话) | GSM8K<br>(数学) | HumanEval<br>(代码) | |---------------------------------------------|---------|--------------|-------------|---------------|--------------------------|-----------------------------|------------|------------------|-----------------------|-------------|-----------------| | baichuan-inc/Baichuan2-13B-Chat v2 | Baichuan| 56.13 | 58.50 | 48.99 | 6.74 | 8.30 | 54.50 | 51.19 | 6.59 | 25.17 | 20.12 | | wangrongsheng/Aurora-Plus | Mixtral | 47.67 | 48.75 | 35.05 | 5.47 | 6.70 | 67.80 | 78.27 | 7.13 | 66.26 | 27.44 | | TigerResearch/tigerbot-13b-chat-v5 | LLaMA | 49.78 | 51.28 | 41.31 | 5.98 | 7.63 | 56.34 | 35.17 | 4.88 | 66.19 | 14.63 | | hfl/chinese-alpaca-2-13b | LLaMA | 43.47 | 44.53 | 25.94 | 5.77 | 8.13 | 53.05 | 56.85 | 6.24 | 32.75 | 14.02 | | 活字1.0 | BLOOM | 37.27 | 36.24 | 19.72 | 4.48 | 7.18 | 39.68 | 33.21 | 4.34 | 21.99 | 13.41 | | 活字2.0 | BLOOM | 32.05 | 34.68 | 22.97 | 5.08 | 6.68 | 38.04 | 33.34 | 4.79 | 19.86 | 6.71 | | **活字3.0（最新版本）** | Mixtral | 51.82 | 51.06 | 41.21 | 6.29 | 7.58 | 69.48 | 65.18 | 7.62 | 65.81 | 40.85 | --> ![](image/evaluation-v3.png) > 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。 > > 我们使用OpenCompass作为评测框架，commit hash为[4c87e77](https://github.com/open-compass/opencompass/tree/4c87e777d855636b9eda7ec87bcbbf12b62caed3)。评测代码位于[此处](./evaluate/)。 根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。 ## 🗂 生成样例 下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比： ![](image/examples/v3-case1.png) ![](image/examples/v3-case2.png) ![](image/examples/v3-case3.png) ![](image/examples/v3-case4.png) ![](image/examples/v3-case5.png) ## <img src="https://cdn.jsdelivr.net/gh/LightChen233/blog-img/folders.png" width="25" /> 开源协议 对本仓库源码的使用遵循开源许可协议 [Apache 2.0](https://github.com/HIT-SCIR/huozi/blob/main/LICENSE)。 活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：<[email protected]>。 ## <img src="https://cdn.jsdelivr.net/gh/LightChen233/blog-img/notes.png" width="25" /> Citation ### 活字大模型 ```latex @misc{huozi, author = {Huozi-Team}. title = {Huozi: Leveraging Large Language Models for Enhanced Open-Domain Chatting} year = {2024}, publisher = {GitHub}, journal = {GitHub repository} howpublished = {\url{https://github.com/HIT-SCIR/huozi}} } ``` ## <img src="https://cdn.jsdelivr.net/gh/LightChen233/blog-img/star.png" width="25" /> Star History [![Star History Chart](https://api.star-history.com/svg?repos=HIT-SCIR/huozi&type=Date)](https://star-history.com/#HIT-SCIR/huozi&Date)
{}
text-generation
HIT-SCIR/huozi3-awq
[ "transformers", "safetensors", "mixtral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T08:33:32+00:00
[]
[]
TAGS #transformers #safetensors #mixtral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
活字通用大模型 ========= 目录 -- ‍ 开源清单 ------ ![](image/URL) * 活字 3.0: [模型权重] + 活字3.0为一个稀疏混合专家模型，支持32K上下文，具有丰富的中、英文知识和强大的数学推理、代码生成能力。活字3.0较旧版活字具有更强的指令遵循能力和安全性。 * 中文MT-Bench: [数据集] + 本数据集是英文MT-Bench对话能力评测数据集的中文版。它包含了一系列多轮对话问题，每一组问题都经过了精心的人工校对，并为适应中文语境进行了必要的调整。 * 《ChatGPT 调研报告》: [PDF] + 哈工大自然语言处理研究所组织多位老师和同学撰写了本调研报告，从技术原理、应用场景、未来发展等方面对ChatGPT进行了尽量详尽的介绍及总结。 * 活字 2.0: [模型权重] [RLHF数据] + 在活字1.0基础上，通过人类反馈的强化学习（RLHF）进一步优化了模型回复质量，使其更加符合人类偏好。相较于上一个版本平均长度明显提高，遵从指令的能力更强，逻辑更加清晰。 + 16.9k 人工标注的偏好数据，回复来自活字模型，可以用于训练奖励模型。 * 活字 1.0: [模型权重] + 在Bloom模型的基础上，在大约 150 亿 tokens 上进行指令微调训练得到的模型，具有更强的指令遵循能力、更好的安全性。 模型介绍 ---- 大规模语言模型（LLM）在自然语言处理领域取得了显著的进展，并在广泛的应用场景中展现了其强大的潜力。这一技术不仅吸引了学术界的广泛关注，也成为了工业界的热点。在此背景下，哈尔滨工业大学社会计算与信息检索研究中心（HIT-SCIR）近期推出了最新成果——活字3.0，致力于为自然语言处理的研究和实际应用提供更多可能性和选择。 活字3.0是基于Chinese-Mixtral-8x7B，在大约30万行指令数据上微调得到的模型。该模型支持32K上下文，能够有效处理长文本。活字3.0继承了基座模型丰富的中英文知识，并在数学推理、代码生成等任务上具有强大性能。经过指令微调，活字3.0还在指令遵循能力和安全性方面实现了显著提升。 此外，我们开源了中文MT-Bench数据集。这是一个中文开放问题集，包括80组对话任务，用于评估模型的多轮对话和指令遵循能力。该数据集是根据原始MT-Bench翻译得来的，每组问题均经过人工校对和中文语境下的适当调整。我们还对原始MT-Bench中的部分错误答案进行了修正。 > > [!IMPORTANT] > 活字系列模型仍然可能生成包含事实性错误的误导性回复或包含偏见/歧视的有害内容，请谨慎鉴别和使用生成的内容，请勿将生成的有害内容传播至互联网。 > > > ### 模型结构 活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。 ![](image/URL) ### 训练过程 由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。 模型下载 ---- 如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。 模型推理 ---- ### Quick Start 活字3.0采用ChatML格式的prompt模板，格式为： 使用活字3.0进行推理的示例代码如下： 活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。 如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。 #### Transformers 模型推理 + 流式生成 transformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下： #### ModelScope 模型推理 ModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可： #### vLLM 推理加速 活字3.0支持通过vLLM实现推理加速，示例代码如下： #### 部署 OpenAI API Server 活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。 环境准备： 启动服务： 使用OpenAI API发送请求： 下面是一个使用OpenAI API + Gradio + 流式生成的示例代码： ### 量化推理 活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量： #### GGUF 格式 GGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。 您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。 ##### Step 1 环境准备 首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理： 您也可以下载最新版本的llama.cpp源码： 然后需要进行编译。根据您的硬件平台，编译命令有细微差异： ##### Step 2 格式转换（可选） 以下命令需要在'URL'目录下： ##### Step 3 开始推理 以下命令需要在'URL'目录下： '-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型： 关于'main'的更多参数，可以参考llama.cpp的官方文档。 #### AWQ 格式 AWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。 ##### Step 1 格式转换（可选） ##### Step 2 开始推理 在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下： 模型性能 ---- ![](image/URL) 针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测： * C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。 * CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。 * GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。 * MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。 * HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。 * GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。 * HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。 * MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。 * MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在此处开源MT-Bench-zh数据集。 * MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。 活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果： ![](image/URL) > > 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。 > > > 我们使用OpenCompass作为评测框架，commit hash为4c87e77。评测代码位于此处。 > > > 根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。 生成样例 ---- 下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比： ![](image/examples/URL) ![](image/examples/URL) ![](image/examples/URL) ![](image/examples/URL) ![](image/examples/URL) <img src="URL width="25" /> 开源协议 -------------------------------- 对本仓库源码的使用遵循开源许可协议 Apache 2.0。 活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：[jngao@URL](mailto:jngao@URL)。 <img src="URL width="25" /> Citation ------------------------------------ ### 活字大模型 <img src="URL width="25" /> Star History ---------------------------------------- ![Star History Chart](URL
[ "### 模型结构\n\n\n活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。\n\n\n![](image/URL)", "### 训练过程\n\n\n由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。\n\n\n模型下载\n----\n\n\n\n如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。\n\n\n模型推理\n----", "### Quick Start\n\n\n活字3.0采用ChatML格式的prompt模板，格式为：\n\n\n使用活字3.0进行推理的示例代码如下：\n\n\n活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。\n\n\n如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。", "#### Transformers 模型推理 + 流式生成\n\n\n\ntransformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下：", "#### ModelScope 模型推理\n\n\n\nModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可：", "#### vLLM 推理加速\n\n\n\n活字3.0支持通过vLLM实现推理加速，示例代码如下：", "#### 部署 OpenAI API Server\n\n\n\n活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。\n\n\n环境准备：\n\n\n启动服务：\n\n\n使用OpenAI API发送请求：\n\n\n下面是一个使用OpenAI API + Gradio + 流式生成的示例代码：", "### 量化推理\n\n\n活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量：", "#### GGUF 格式\n\n\n\nGGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。\n\n\n您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。", "##### Step 1 环境准备\n\n\n首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理：\n\n\n您也可以下载最新版本的llama.cpp源码：\n\n\n然后需要进行编译。根据您的硬件平台，编译命令有细微差异：", "##### Step 2 格式转换（可选）\n\n\n以下命令需要在'URL'目录下：", "##### Step 3 开始推理\n\n\n以下命令需要在'URL'目录下：\n\n\n'-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型：\n\n\n关于'main'的更多参数，可以参考llama.cpp的官方文档。", "#### AWQ 格式\n\n\n\nAWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。", "##### Step 1 格式转换（可选）", "##### Step 2 开始推理\n\n\n在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下：\n\n\n\n模型性能\n----\n\n\n![](image/URL)\n\n\n针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测：\n\n\n* C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。\n* CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。\n* GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。\n* MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。\n* HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。\n* GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。\n* HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。\n* MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。\n* MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在此处开源MT-Bench-zh数据集。\n* MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。\n\n\n活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果：\n\n\n![](image/URL)\n\n\n\n> \n> 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。\n> \n> \n> 我们使用OpenCompass作为评测框架，commit hash为4c87e77。评测代码位于此处。\n> \n> \n> \n\n\n根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。\n\n\n生成样例\n----\n\n\n下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比：\n\n\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n\n\n<img src=\"URL width=\"25\" /> 开源协议\n--------------------------------\n\n\n对本仓库源码的使用遵循开源许可协议 Apache 2.0。\n\n\n活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：[jngao@URL](mailto:jngao@URL)。\n\n\n<img src=\"URL width=\"25\" /> Citation\n------------------------------------", "### 活字大模型\n\n\n<img src=\"URL width=\"25\" /> Star History\n----------------------------------------\n\n\n![Star History Chart](URL" ]
[ "TAGS\n#transformers #safetensors #mixtral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### 模型结构\n\n\n活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。\n\n\n![](image/URL)", "### 训练过程\n\n\n由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。\n\n\n模型下载\n----\n\n\n\n如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。\n\n\n模型推理\n----", "### Quick Start\n\n\n活字3.0采用ChatML格式的prompt模板，格式为：\n\n\n使用活字3.0进行推理的示例代码如下：\n\n\n活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。\n\n\n如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。", "#### Transformers 模型推理 + 流式生成\n\n\n\ntransformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下：", "#### ModelScope 模型推理\n\n\n\nModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可：", "#### vLLM 推理加速\n\n\n\n活字3.0支持通过vLLM实现推理加速，示例代码如下：", "#### 部署 OpenAI API Server\n\n\n\n活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。\n\n\n环境准备：\n\n\n启动服务：\n\n\n使用OpenAI API发送请求：\n\n\n下面是一个使用OpenAI API + Gradio + 流式生成的示例代码：", "### 量化推理\n\n\n活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量：", "#### GGUF 格式\n\n\n\nGGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。\n\n\n您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。", "##### Step 1 环境准备\n\n\n首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理：\n\n\n您也可以下载最新版本的llama.cpp源码：\n\n\n然后需要进行编译。根据您的硬件平台，编译命令有细微差异：", "##### Step 2 格式转换（可选）\n\n\n以下命令需要在'URL'目录下：", "##### Step 3 开始推理\n\n\n以下命令需要在'URL'目录下：\n\n\n'-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型：\n\n\n关于'main'的更多参数，可以参考llama.cpp的官方文档。", "#### AWQ 格式\n\n\n\nAWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。", "##### Step 1 格式转换（可选）", "##### Step 2 开始推理\n\n\n在获取到AWQ格式的模型权重后，可以使用AutoAWQForCausalLM代替AutoModelForCausalLM加载模型。示例代码如下：\n\n\n\n模型性能\n----\n\n\n![](image/URL)\n\n\n针对大模型综合能力评价，我们分别使用以下评测数据集对活字3.0进行评测：\n\n\n* C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。\n* CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。\n* GAOKAO：一个以中国高考题目为数据集，旨在提供和人类对齐的，直观，高效地测评大模型语言理解能力、逻辑推理能力的测评框架。\n* MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。\n* HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。\n* GSM8K：一个高质量的小学数学应用题的数据集，这些问题需要 2 到 8 个步骤来解决，解决方案主要涉及使用基本算术运算，可用于评价多步数学推理能力。\n* HumanEval：一个由 164 个原创编程问题组成的数据集，通过衡量从文档字符串生成程序的功能正确性，来够评估语言理解、算法和简单的数学能力。\n* MT-Bench：一个开放的英文问题集，包括80个多轮对话任务，用于评估聊天机器人的多轮对话和指令遵循能力，并通过大模型裁判（GPT-4）对模型回答进行打分。\n* MT-Bench-zh：我们根据MT-Bench翻译得来的中文问题集，每组问题均经过人工校对和中文语境下的适当调整。我们已在此处开源MT-Bench-zh数据集。\n* MT-Bench-safety：我们手工构造的安全数据集，包括暴力、色情、敏感等风险内容。该数据集为封闭数据集。\n\n\n活字3.0在推理时仅激活13B参数。下表为活字3.0与其他13B规模的中文模型以及旧版活字在各个评测数据集上的结果：\n\n\n![](image/URL)\n\n\n\n> \n> 我们在C-Eval、CMMLU、MMLU采用5-shot，GSM8K采用4-shot，HellaSwag、HumanEval采用0-shot，HumanEval采用pass@1指标。所有测试均采用greedy策略。\n> \n> \n> 我们使用OpenCompass作为评测框架，commit hash为4c87e77。评测代码位于此处。\n> \n> \n> \n\n\n根据上表中的测试结果，活字3.0较旧版活字取得了巨大的性能提升。在中文知识方面，活字3.0达到了与Tigerbot-13B-chat-v5相当的性能，并是在中文对话和指令遵循方面表现得更加优秀。在英文知识方面，得益于原版Mixtral-8x7B的强大性能，活字3.0超过了Baichuan2-13B-Chat v2和LLaMA系列的扩词表模型，并在英文对话和指令遵循能力上达到了较高水平。在数学推理和代码生成任务上，活字3.0均展现出强大的性能，这说明活字3.0对复杂问题的深层次理解、多步推理、以及结构化信息处理等方面具有较强水平。由于我们采用了较高质量的代码数据集，活字3.0的代码生成能力也超越了同为Mixtral结构的Aurora-Plus模型。\n\n\n生成样例\n----\n\n\n下面是活字3.0在MT-Bench-zh评测集上的生成效果展示，并与活字2.0（RLHF版本）进行对比：\n\n\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n![](image/examples/URL)\n\n\n<img src=\"URL width=\"25\" /> 开源协议\n--------------------------------\n\n\n对本仓库源码的使用遵循开源许可协议 Apache 2.0。\n\n\n活字支持商用。如果将活字模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱：[jngao@URL](mailto:jngao@URL)。\n\n\n<img src=\"URL width=\"25\" /> Citation\n------------------------------------", "### 活字大模型\n\n\n<img src=\"URL width=\"25\" /> Star History\n----------------------------------------\n\n\n![Star History Chart](URL" ]
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[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n### 模型结构\n\n\n活字3.0是一个稀疏混合专家模型（SMoE），使用了Mixtral-8x7B的模型结构。它区别于LLaMA、BLOOM等常见模型，活字3.0的每个前馈神经网络（FFN）层被替换为了“专家层”，该层包含8个FFN和一个“路由器”。这种设计使得模型在推理过程中，可以独立地将每个Token路由到最适合处理它的两个专家中。活字3.0共拥有46.7B个参数，但得益于其稀疏激活的特性，实际推理时仅需激活13B参数，有效提升了计算效率和处理速度。\n\n\n![](image/URL)### 训练过程\n\n\n由于Mixtral-8x7B词表不支持中文，因此对中文的编解码效率较低，限制了中文场景下的实用性。我们首先基于Mixtral-8x7B进行了中文扩词表增量预训练，显著提高了模型对中文的编解码效率，并使模型具备了强大的中文生成和理解能力。这项成果名为Chinese-Mixtral-8x7B，我们已于2024年1月18日开源了其模型权重和训练代码。基于此，我们进一步对模型进行指令微调，最终推出了活字3.0。这一版本的中文编码、指令遵循、安全回复等能力都有显著提升。\n\n\n模型下载\n----\n\n\n\n如果您希望微调活字3.0或Chinese-Mixtral-8x7B，请参考此处训练代码。\n\n\n模型推理\n----### Quick Start\n\n\n活字3.0采用ChatML格式的prompt模板，格式为：\n\n\n使用活字3.0进行推理的示例代码如下：\n\n\n活字3.0支持全部Mixtral模型生态，包括Transformers、vLLM、URL、AutoAWQ、Text generation web UI等框架。\n\n\n如果您在下载模型时遇到网络问题，可以使用我们在ModelScope上提供的检查点。", "passage: #### Transformers 模型推理 + 流式生成\n\n\n\ntransformers支持为tokenizer添加聊天模板，并支持流式生成。示例代码如下：#### ModelScope 模型推理\n\n\n\nModelScope的接口与Transformers非常相似，只需将transformers替换为modelscope即可：#### vLLM 推理加速\n\n\n\n活字3.0支持通过vLLM实现推理加速，示例代码如下：#### 部署 OpenAI API Server\n\n\n\n活字3.0可以部署为支持OpenAI API协议的服务，这使得活字3.0可以直接通过OpenAI API进行调用。\n\n\n环境准备：\n\n\n启动服务：\n\n\n使用OpenAI API发送请求：\n\n\n下面是一个使用OpenAI API + Gradio + 流式生成的示例代码：### 量化推理\n\n\n活字3.0支持量化推理，下表为活字3.0在各个量化框架下显存占用量：#### GGUF 格式\n\n\n\nGGUF格式旨在快速加载和保存模型，由llama.cpp团队推出。我们已经提供了GGUF格式的活字3.0。\n\n\n您也可以手动将HuggingFace格式的活字3.0转换到GGUF格式，以使用其他的量化方法。##### Step 1 环境准备\n\n\n首先需要下载llama.cpp的源码。我们在仓库中提供了llama.cpp的submodule，这个版本的llama.cpp已经过测试，可以成功进行推理：\n\n\n您也可以下载最新版本的llama.cpp源码：\n\n\n然后需要进行编译。根据您的硬件平台，编译命令有细微差异：##### Step 2 格式转换（可选）\n\n\n以下命令需要在'URL'目录下：##### Step 3 开始推理\n\n\n以下命令需要在'URL'目录下：\n\n\n'-ngl'参数表示向GPU中offload的层数，降低这个值可以缓解GPU显存压力。经过我们的实际测试，q2\\_k量化的模型offload 16层，显存占用可降低至9.6GB，可在消费级GPU上运行模型：\n\n\n关于'main'的更多参数，可以参考llama.cpp的官方文档。#### AWQ 格式\n\n\n\nAWQ是一种量化模型的存储格式。我们已经提供了AWQ格式的活字3.0，您也可以手动将HuggingFace格式的活字3.0转换到AWQ格式。##### Step 1 格式转换（可选）" ]
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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. --> # wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1376 - Accuracy: 0.8210 - F1: 0.8209 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.3731 | 0.99 | 35 | 1.3532 | 0.3767 | 0.2859 | | 1.3039 | 2.0 | 71 | 1.2740 | 0.4237 | 0.3434 | | 1.2185 | 2.99 | 106 | 1.1573 | 0.5020 | 0.4423 | | 1.0887 | 4.0 | 142 | 1.1107 | 0.5013 | 0.4389 | | 1.0183 | 4.99 | 177 | 1.0801 | 0.5610 | 0.5348 | | 0.8625 | 6.0 | 213 | 0.9364 | 0.6373 | 0.6285 | | 0.7487 | 6.99 | 248 | 0.9735 | 0.6048 | 0.5867 | | 0.6151 | 8.0 | 284 | 0.8946 | 0.6698 | 0.6735 | | 0.5081 | 8.99 | 319 | 0.8748 | 0.6797 | 0.6855 | | 0.4559 | 10.0 | 355 | 0.8701 | 0.6850 | 0.6832 | | 0.4347 | 10.99 | 390 | 0.8887 | 0.7003 | 0.7040 | | 0.2845 | 12.0 | 426 | 0.8715 | 0.7129 | 0.7145 | | 0.275 | 12.99 | 461 | 0.8846 | 0.7268 | 0.7263 | | 0.2301 | 14.0 | 497 | 0.8651 | 0.7261 | 0.7324 | | 0.1657 | 14.99 | 532 | 0.8573 | 0.7473 | 0.7473 | | 0.1593 | 16.0 | 568 | 0.8472 | 0.7420 | 0.7443 | | 0.1398 | 16.99 | 603 | 0.7433 | 0.7825 | 0.7829 | | 0.1318 | 18.0 | 639 | 0.7989 | 0.7739 | 0.7768 | | 0.1425 | 18.99 | 674 | 0.7967 | 0.7759 | 0.7788 | | 0.1116 | 20.0 | 710 | 0.8969 | 0.7659 | 0.7650 | | 0.0716 | 20.99 | 745 | 0.9783 | 0.7434 | 0.7480 | | 0.0909 | 22.0 | 781 | 0.9413 | 0.7593 | 0.7626 | | 0.0691 | 22.99 | 816 | 0.9298 | 0.7832 | 0.7832 | | 0.068 | 24.0 | 852 | 0.9522 | 0.7725 | 0.7744 | | 0.0416 | 24.99 | 887 | 0.9624 | 0.7686 | 0.7746 | | 0.0569 | 26.0 | 923 | 0.9376 | 0.7832 | 0.7832 | | 0.0369 | 26.99 | 958 | 1.0163 | 0.7845 | 0.7843 | | 0.0482 | 28.0 | 994 | 1.0013 | 0.7931 | 0.7895 | | 0.0497 | 28.99 | 1029 | 1.1005 | 0.7725 | 0.7713 | | 0.0427 | 30.0 | 1065 | 1.0346 | 0.7891 | 0.7901 | | 0.0252 | 30.99 | 1100 | 1.0611 | 0.7871 | 0.7883 | | 0.0268 | 32.0 | 1136 | 1.0436 | 0.7944 | 0.7962 | | 0.022 | 32.99 | 1171 | 1.0217 | 0.8031 | 0.8012 | | 0.0127 | 34.0 | 1207 | 1.0936 | 0.7971 | 0.7969 | | 0.0153 | 34.99 | 1242 | 1.0777 | 0.8097 | 0.8055 | | 0.0062 | 36.0 | 1278 | 1.2379 | 0.7699 | 0.7751 | | 0.0081 | 36.99 | 1313 | 1.0697 | 0.7977 | 0.7987 | | 0.0072 | 38.0 | 1349 | 1.1284 | 0.7997 | 0.8001 | | 0.0105 | 38.99 | 1384 | 1.0593 | 0.8137 | 0.8136 | | 0.0102 | 40.0 | 1420 | 1.0805 | 0.8130 | 0.8126 | | 0.0088 | 40.99 | 1455 | 1.1237 | 0.8110 | 0.8115 | | 0.0073 | 42.0 | 1491 | 1.0980 | 0.8170 | 0.8167 | | 0.0046 | 42.99 | 1526 | 1.1584 | 0.8044 | 0.8049 | | 0.0061 | 44.0 | 1562 | 1.1517 | 0.8110 | 0.8114 | | 0.0021 | 44.99 | 1597 | 1.1564 | 0.8064 | 0.8074 | | 0.0073 | 46.0 | 1633 | 1.1214 | 0.8183 | 0.8183 | | 0.002 | 46.99 | 1668 | 1.1376 | 0.8210 | 0.8209 | | 0.0064 | 48.0 | 1704 | 1.1283 | 0.8210 | 0.8208 | | 0.0072 | 48.99 | 1739 | 1.1271 | 0.8203 | 0.8201 | | 0.0019 | 49.3 | 1750 | 1.1273 | 0.8203 | 0.8201 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]}
audio-classification
Rest970828/wav2vec2-base-finetuned-ks
[ "transformers", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-08T08:33:35+00:00
[]
[]
TAGS #transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us
wav2vec2-base-finetuned-ks ========================== This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.1376 * Accuracy: 0.8210 * F1: 0.8209 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: 32 * eval\_batch\_size: 32 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.1.1+cu121 * Datasets 2.16.1 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-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: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]
[ 62, 144, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-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: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-squad-model1 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) 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: 64 - eval_batch_size: 16 - seed: 62 - 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": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-squad-model1", "results": []}]}
question-answering
varun-v-rao/roberta-base-squad-model1
[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2024-02-08T08:37:53+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #endpoints_compatible #region-us
# roberta-base-squad-model1 This model is a fine-tuned version of roberta-base 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: 64 - eval_batch_size: 16 - seed: 62 - 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
[ "# roberta-base-squad-model1\n\nThis model is a fine-tuned version of roberta-base 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: 64\n- eval_batch_size: 16\n- seed: 62\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 #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #endpoints_compatible #region-us \n", "# roberta-base-squad-model1\n\nThis model is a fine-tuned version of roberta-base 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: 64\n- eval_batch_size: 16\n- seed: 62\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" ]
[ 69, 32, 6, 12, 8, 3, 90, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #endpoints_compatible #region-us \n# roberta-base-squad-model1\n\nThis model is a fine-tuned version of roberta-base 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: 64\n- eval_batch_size: 16\n- seed: 62\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
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": "roberta-large"}
null
shahzebnaveed/roberta-large-lora-token-cls
[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:roberta-large", "region:us" ]
2024-02-08T08:41:41+00:00
[ "1910.09700" ]
[]
TAGS #peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-roberta-large #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 #tensorboard #safetensors #arxiv-1910.09700 #base_model-roberta-large #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 #tensorboard #safetensors #arxiv-1910.09700 #base_model-roberta-large #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
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. --> # dit-base-finetuned-rvlcdip-finetuned-custom-first This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0567 - Accuracy: 0.9949 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3686 | 1.0 | 79 | 0.2356 | 0.9746 | | 0.0891 | 2.0 | 158 | 0.0792 | 0.9936 | | 0.0652 | 3.0 | 237 | 0.0567 | 0.9949 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/dit-base-finetuned-rvlcdip", "model-index": [{"name": "dit-base-finetuned-rvlcdip-finetuned-custom-first", "results": []}]}
image-classification
stray-light/dit-base-finetuned-rvlcdip-finetuned-custom-first
[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "base_model:microsoft/dit-base-finetuned-rvlcdip", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T08:43:48+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #beit #image-classification #generated_from_trainer #base_model-microsoft/dit-base-finetuned-rvlcdip #autotrain_compatible #endpoints_compatible #region-us
dit-base-finetuned-rvlcdip-finetuned-custom-first ================================================= This model is a fine-tuned version of microsoft/dit-base-finetuned-rvlcdip on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0567 * Accuracy: 0.9949 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #beit #image-classification #generated_from_trainer #base_model-microsoft/dit-base-finetuned-rvlcdip #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 68, 144, 4, 38 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #beit #image-classification #generated_from_trainer #base_model-microsoft/dit-base-finetuned-rvlcdip #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\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
inStryde/mask2former-swin-large-ade-semantic-instryde-foot
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T08:46:55+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" ]
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[ "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
# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone). | ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) | |:--:| | <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>| ## TL;DR Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract: *Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.* ## Usage You can use this model for conditional and un-conditional image captioning ### Using the Pytorch model #### Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning inputs = processor(raw_image, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> #### Running the model on GPU ##### In full precision <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </details> ## BibTex and citation info ``` @misc{https://doi.org/10.48550/arxiv.2201.12086, doi = {10.48550/ARXIV.2201.12086}, url = {https://arxiv.org/abs/2201.12086}, author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
{"license": "bsd-3-clause", "tags": ["image-captioning"], "pipeline_tag": "image-to-text", "languages": ["en"]}
image-to-text
gizmo-ai/blip-image-captioning-large
[ "transformers", "pytorch", "tf", "safetensors", "blip", "text2text-generation", "image-captioning", "image-to-text", "arxiv:2201.12086", "license:bsd-3-clause", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T08:47:59+00:00
[ "2201.12086" ]
[]
TAGS #transformers #pytorch #tf #safetensors #blip #text2text-generation #image-captioning #image-to-text #arxiv-2201.12086 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation ======================================================================================================== Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone). TL;DR ----- Authors from the paper write in the abstract: *Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.* Usage ----- You can use this model for conditional and un-conditional image captioning ### Using the Pytorch model #### Running the model on CPU Click to expand #### Running the model on GPU ##### In full precision Click to expand ##### In half precision ('float16') Click to expand BibTex and citation info ------------------------
[ "### Using the Pytorch model", "#### Running the model on CPU\n\n\n\n Click to expand", "#### Running the model on GPU", "##### In full precision\n\n\n\n Click to expand", "##### In half precision ('float16')\n\n\n\n Click to expand \n\nBibTex and citation info\n------------------------" ]
[ "TAGS\n#transformers #pytorch #tf #safetensors #blip #text2text-generation #image-captioning #image-to-text #arxiv-2201.12086 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us \n", "### Using the Pytorch model", "#### Running the model on CPU\n\n\n\n Click to expand", "#### Running the model on GPU", "##### In full precision\n\n\n\n Click to expand", "##### In half precision ('float16')\n\n\n\n Click to expand \n\nBibTex and citation info\n------------------------" ]
[ 78, 9, 11, 8, 9, 27 ]
[ "passage: TAGS\n#transformers #pytorch #tf #safetensors #blip #text2text-generation #image-captioning #image-to-text #arxiv-2201.12086 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us \n### Using the Pytorch model#### Running the model on CPU\n\n\n\n Click to expand#### Running the model on GPU##### In full precision\n\n\n\n Click to expand##### In half precision ('float16')\n\n\n\n Click to expand \n\nBibTex and citation info\n------------------------" ]
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null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [Laurie] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [En] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
null
Laurie/phi2_DPO
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-08T08:48:32+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: [Laurie] - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): [En] - 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: [Laurie]\n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): [En]\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: [Laurie]\n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): [En]\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: [Laurie]\n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): [En]\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
# Full Parameter Finetuning Qwen1.5-0.5B 16384 context length on Malaysian instructions dataset README at https://github.com/mesolitica/malaya/tree/5.1/session/qwen2 We use exact Qwen1.5 Instruct chat template. WandB, https://wandb.ai/huseinzol05/Qwen1.5-0.5B-4096-fpf-instructions-16k?workspace=user-huseinzol05 ## how-to ```python from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch TORCH_DTYPE = 'bfloat16' nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type='nf4', bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE) ) tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-Qwen1.5-0.5B-16k-instructions') model = AutoModelForCausalLM.from_pretrained( 'mesolitica/malaysian-Qwen1.5-0.5B-16k-instructions', use_flash_attention_2 = True, quantization_config = nf4_config ) messages = [ {'role': 'user', 'content': 'KWSP tu apa'} ] prompt = tokenizer.apply_chat_template(messages, tokenize = False) inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda') generate_kwargs = dict( inputs, max_new_tokens=1024, top_p=0.95, top_k=50, temperature=0.9, do_sample=True, num_beams=1, ) r = model.generate(**generate_kwargs) tokenizer.decode(r[0]) ``` ```text <|im_start|>user KWSP tu apa<|im_end|> <|im_start|>assistant KWSP merujuk kepada Skim Simpanan Wang Persaraan (KWSP), iaitu skim simpanan untuk ahli kumpulan berumur 20 tahun ke atas. KWSP menawarkan beberapa faedah, termasuk: 1. Akaun Simpanan Wajib - Ahli boleh menyumbang kepada KWSP melalui akaun simpanan wajib. 2. Akaun Simpanan Amanah - Ahli boleh menyumbang kepada KWSP melalui akaun amanah. 3. Akaun Simpanan Pelaburan - Ahli boleh menyumbang kepada KWSP melalui pelaburan dalam skim pelaburan KWSP atau pelaburan bersama. 4. Perolehan - Ahli boleh mendapatkan pelbagai perkhidmatan dan ganjaran daripada KWSP. 5. Akaun Simpanan untuk Penyelamat - Ahli boleh menyumbang kepada KWSP melalui akaun simpanan untuk penyelamat, yang boleh digunakan untuk bantuan kewangan atau pemberhentian pasaran kewangan. KWSP menawarkan faedah dan perlindungan yang komprehensif untuk ahli, termasuk: 1. Akaun Simpanan Amanah - Akaun ini menyediakan faedah dan ganjaran sebelum dan selepas persaraan. 2. Akaun Simpanan - Akaun simpanan menawarkan kadar faedah yang lebih rendah daripada akaun simpanan biasa. 3. Akaun Simpanan Pelaburan - Akaun ini menawarkan kadar faedah yang lebih rendah dan pelbagai ganjaran. 4. Akaun Simpanan untuk Penyelamat - Akaun ini menawarkan kadar faedah yang lebih rendah daripada akaun simpanan biasa dan pelbagai ganjaran. 5. Perolehan - Akaun ini menyediakan faedah dan ganjaran sebelum dan selepas persaraan. KWSP juga menawarkan pelbagai perkhidmatan dan ganjaran lain, termasuk: 1. Program Pekerjaan - KWSP menyediakan pelbagai skim pekerjaan, termasuk Skim Pencen, Skim Pekerjaan, dan Skim Penginapan. 2. Program Perubatan - KWSP menyediakan pelbagai skim perubatan, termasuk Skim Cukai Perubatan. 3. Program Keselamatan Sosial - KWSP menyediakan pelbagai program keselamatan sosial, termasuk Skim Simpanan 401 (KWSP), Skim Simpanan Amanah (SW401) dan Skim Simpanan Perumahan (RSP) untuk pemastautin berdaftar. 4. Program Penjagaan Kesihatan - KWSP menyediakan pelbagai skim penjagaan kesihatan, termasuk Skim Insurans Kesihatan Swasta (SIC), Skim Insurans Kesihatan Kumpulan Swasta (KSMC), dan Skim Insurans Kesihatan Kumpulan Swasta 1Malaysia (SIC 1M). 5. Program Pemindahan - KWSP menyediakan pelbagai skim pemindahan, termasuk Skim Pemindahan Amanah, Skim Pemindahan KWSP, dan Skim Pemindahan Persekutuan. KWSP menawarkan pelbagai pilihan dan perlindungan untuk ahli yang ingin menyumbang kepada KWSP untuk persaraan atau bantuan kewangan atau pemberhentian pasaran kewangan.<|im_end|> ``` ```python messages = [ {'role': 'user', 'content': 'macam mana nak kod websocket guna react, contoh kod sekali'} ] prompt = tokenizer.apply_chat_template(messages, tokenize = False) inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda') generate_kwargs = dict( inputs, max_new_tokens=1024, top_p=0.95, top_k=50, temperature=0.9, do_sample=True, num_beams=1, ) r = model.generate(**generate_kwargs) tokenizer.decode(r[0]) ``` ```text <|im_start|>user macam mana nak kod websocket guna react, contoh kod sekali<|im_end|> <|im_start|>assistant Untuk menggunakan websocket pada React untuk mencipta pelanggan untuk menghantar dan menerima pesanan, anda boleh mengikuti langkah-langkah berikut: 1. Mulakan dengan membuat fail baru, contohnya `WebsocketClient.js`, dan perpustakaan yang diperlukan seperti `ws` untuk Node.js, dan `axios` untuk JavaScript untuk membuat permintaan HTTP ke websocket. Pastikan anda telah mengimport perpustakaan dengan betul. 2. Dalam fail `WebsocketClient.js`, cipta kelas baru dengan nama `WebsocketClient`. Anda boleh menggunakan konstruktor dengan parameter seperti nama aplikasi dan senarai objek pesanan. Contoh: ``` ```javascript import { WebSocketClient, Client } from 'ws'; import axios from 'axios'; class WebsocketClient { constructor(appName, socket) { this.appName = appName; this.socket = socket; } connect() { this.socket.onopen = () => { console.log('Connection successful'); }; this.socket.onclose = () => { console.log('Connection lost'); }; this.socket.onmessage = (message) => { // Code to handle incoming message }; } emit(data) { this.socket.onmessage(data); } disconnect() { this.socket.offopen(); } } // Contoh penggunaan const client = new WebsocketClient('api', axios({ port: 123 })); client.connect(); client.emit('message', { type: 'info', content: 'Hello, server! }); client.emit('message', { type: 'warning', content: 'Goodbye!'}); client.disconnect(); ``` ``` 3. Dalam fail `WebSocketClient.js`, cipta kelas dengan nama `Client` dengan parameter seperti aplikasi dan `websocket` dari perpustakaan axios. Contoh: ``` ```javascript class Client { constructor(appName, websocket) { this.appName = appName; this.websocket = websocket; } connect() { this.websocket.onopen = () => { console.log('Connection successful'); }; this.websocket.onclose = () => { console.log('Connection lost'); }; this.websocket.onmessage = (message) => { // Code to handle incoming message }; } disconnect() { this.websocket.offopen(); } } // Contoh penggunaan const client = new Client('api', axios({ port: 123 })); client.connect(); client.emit('message', { type: 'info', content: 'Hello, server! }); client.emit('message', { type: 'warning', content: 'Goodbye!'}); client.disconnect(); ``` ``` Dalam contoh di atas, kami menggunakan konstruktor yang mengambil nama aplikasi dan websocket sebagai parameter untuk memulakan dan menggunakan fungsi `connect` untuk menghubungi websocket dengan ID sesi yang diinginkan. Kemudian, kami menggunakan fungsi `onmessage` untuk mengendalikan respons websocket dan fungsi `onclose` untuk menutup websocket. Apabila kami menghantar permintaan ke websocket, kami menggunakan kaedah `onmessage` untuk memproses respons tersebut. Apabila kami membuka websocket, kami menggunakan kaedah `onclose` untuk menutup websocket dan menggunakan kaedah `disconnect` untuk membunuhnya. Dengan menggunakan kod di atas dan menggunakannya pada projek React anda, anda boleh mencipta websocket yang boleh menghantar dan menerima pesanan untuk aplikasi anda.<|im_end|> ```
{"language": ["ms"]}
text-generation
mesolitica/malaysian-Qwen1.5-0.5B-16k-instructions
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "ms", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T08:52:44+00:00
[]
[ "ms" ]
TAGS #transformers #safetensors #qwen2 #text-generation #conversational #ms #autotrain_compatible #endpoints_compatible #region-us
# Full Parameter Finetuning Qwen1.5-0.5B 16384 context length on Malaysian instructions dataset README at URL We use exact Qwen1.5 Instruct chat template. WandB, URL ## how-to
[ "# Full Parameter Finetuning Qwen1.5-0.5B 16384 context length on Malaysian instructions dataset\n\nREADME at URL\n\nWe use exact Qwen1.5 Instruct chat template.\n\nWandB, URL", "## how-to" ]
[ "TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #ms #autotrain_compatible #endpoints_compatible #region-us \n", "# Full Parameter Finetuning Qwen1.5-0.5B 16384 context length on Malaysian instructions dataset\n\nREADME at URL\n\nWe use exact Qwen1.5 Instruct chat template.\n\nWandB, URL", "## how-to" ]
[ 45, 42, 4 ]
[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #ms #autotrain_compatible #endpoints_compatible #region-us \n# Full Parameter Finetuning Qwen1.5-0.5B 16384 context length on Malaysian instructions dataset\n\nREADME at URL\n\nWe use exact Qwen1.5 Instruct chat template.\n\nWandB, URL## how-to" ]
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null
null
stable-baselines3
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Yukino666 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Yukino666 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Yukino666 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 50000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 50000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "651.00 +/- 257.73", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
Yukino666/dqn-SpaceInvadersNoFrameskip-v4
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-08T08:54:59+00:00
[]
[]
TAGS #stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: URL SB3: URL SB3 Contrib: URL Install the RL Zoo (with SB3 and SB3-Contrib): If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters # Environment Arguments
[ "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]
[ "TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]
[ 43, 90, 73, 9, 5, 7 ]
[ "passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
unsloth/yi-34b-chat-bnb-4bit
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T08:55:23+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #conversational #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 #conversational #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 #conversational #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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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
YashRawal225/New-3-7b-chat-finetune-german500-GGUF
[ "transformers", "gguf", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T08:57:46+00:00
[ "1910.09700" ]
[]
TAGS #transformers #gguf #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 #gguf #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 #gguf #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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# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Gangster with guns in hand --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{}
null
issuez/ishan
[ "arxiv:1910.09700", "region:us" ]
2024-02-08T09:00:23+00:00
[ "1910.09700" ]
[]
TAGS #arxiv-1910.09700 #region-us
# Model Card for Model ID This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#arxiv-1910.09700 #region-us \n", "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 15, 29, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#arxiv-1910.09700 #region-us \n# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# zephyr-infoNCA-preference This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset. It achieves the following results on the evaluation set: - Loss: 0.4575 - Rewards/chosen: -0.8931 - Rewards/rejected: -2.0138 - Rewards/accuracies: 0.7745 - Rewards/margins: 1.1206 - Verify/constant 1: 1.0 - Verify/constant 1len: 1000.0 - Logps/rejected: -434.5525 - Logps/chosen: -364.4662 - Verify/bz: 1.0 - Verify/gather Bz: 2.0 - Regularization/forward Kl: 2.0564 - Regularization/reverse Kl: 1.0252 - Regularization/policy Data Loss: 3.8558 - Regularization/reference Data Loss: 1.3337 - Regularization/policy Ref Data Loss Gap: 2.5221 - Mask/mask Ratio: 0.4809 ## 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-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Verify/constant 1 | Verify/constant 1len | Logps/rejected | Logps/chosen | Verify/bz | Verify/gather Bz | Regularization/forward Kl | Regularization/reverse Kl | Regularization/policy Data Loss | Regularization/reference Data Loss | Regularization/policy Ref Data Loss Gap | Mask/mask Ratio | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:-----------------:|:--------------------:|:--------------:|:------------:|:---------:|:----------------:|:-------------------------:|:-------------------------:|:-------------------------------:|:----------------------------------:|:---------------------------------------:|:---------------:| | 0.6892 | 0.05 | 100 | 0.6881 | 0.0039 | -0.0063 | 0.7145 | 0.0102 | 1.0 | 1000.0 | -233.8040 | -274.7605 | 1.0 | 2.0 | 0.0009 | 0.0009 | 1.3405 | 1.3337 | 0.0068 | 0.4809 | | 0.6259 | 0.1 | 200 | 0.6258 | -0.1279 | -0.2905 | 0.7145 | 0.1627 | 1.0 | 1000.0 | -262.2266 | -287.9373 | 1.0 | 2.0 | 0.1727 | 0.1289 | 1.6331 | 1.3337 | 0.2994 | 0.4809 | | 0.5436 | 0.15 | 300 | 0.5495 | -0.4736 | -0.9395 | 0.7415 | 0.4659 | 1.0 | 1000.0 | -327.1224 | -322.5125 | 1.0 | 2.0 | 0.6904 | 0.3995 | 2.2940 | 1.3337 | 0.9603 | 0.4809 | | 0.5492 | 0.21 | 400 | 0.5161 | -0.5783 | -1.2015 | 0.7545 | 0.6232 | 1.0 | 1000.0 | -353.3223 | -332.9807 | 1.0 | 2.0 | 0.9794 | 0.5146 | 2.7574 | 1.3337 | 1.4237 | 0.4809 | | 0.521 | 0.26 | 500 | 0.4982 | -0.7257 | -1.5000 | 0.7595 | 0.7743 | 1.0 | 1000.0 | -383.1716 | -347.7220 | 1.0 | 2.0 | 1.2016 | 0.5622 | 3.0006 | 1.3337 | 1.6669 | 0.4809 | | 0.5152 | 0.31 | 600 | 0.4887 | -0.6594 | -1.4497 | 0.7685 | 0.7903 | 1.0 | 1000.0 | -378.1454 | -341.0961 | 1.0 | 2.0 | 1.2196 | 0.6044 | 3.0235 | 1.3337 | 1.6897 | 0.4809 | | 0.4862 | 0.36 | 700 | 0.4857 | -0.7064 | -1.5442 | 0.7655 | 0.8378 | 1.0 | 1000.0 | -387.5948 | -345.7939 | 1.0 | 2.0 | 1.2568 | 0.6231 | 3.2214 | 1.3337 | 1.8877 | 0.4809 | | 0.4632 | 0.41 | 800 | 0.4803 | -0.6298 | -1.4654 | 0.7755 | 0.8356 | 1.0 | 1000.0 | -379.7145 | -338.1303 | 1.0 | 2.0 | 1.3128 | 0.7041 | 2.8330 | 1.3337 | 1.4993 | 0.4809 | | 0.4912 | 0.46 | 900 | 0.4707 | -0.7165 | -1.6486 | 0.7750 | 0.9321 | 1.0 | 1000.0 | -398.0345 | -346.8000 | 1.0 | 2.0 | 1.4120 | 0.7160 | 3.0682 | 1.3337 | 1.7345 | 0.4809 | | 0.4588 | 0.52 | 1000 | 0.4680 | -0.8531 | -1.8542 | 0.7690 | 1.0011 | 1.0 | 1000.0 | -418.5936 | -360.4624 | 1.0 | 2.0 | 1.6382 | 0.8346 | 3.5448 | 1.3337 | 2.2111 | 0.4809 | | 0.4956 | 0.57 | 1100 | 0.4650 | -0.7990 | -1.7772 | 0.7790 | 0.9781 | 1.0 | 1000.0 | -410.8913 | -355.0567 | 1.0 | 2.0 | 1.6270 | 0.8004 | 3.5035 | 1.3337 | 2.1698 | 0.4809 | | 0.4738 | 0.62 | 1200 | 0.4629 | -0.8068 | -1.8169 | 0.7705 | 1.0102 | 1.0 | 1000.0 | -414.8670 | -355.8280 | 1.0 | 2.0 | 1.7938 | 0.8907 | 3.6708 | 1.3337 | 2.3371 | 0.4809 | | 0.4657 | 0.67 | 1300 | 0.4622 | -0.8659 | -1.9282 | 0.7655 | 1.0623 | 1.0 | 1000.0 | -425.9926 | -361.7412 | 1.0 | 2.0 | 1.9375 | 0.9455 | 3.7639 | 1.3337 | 2.4301 | 0.4809 | | 0.4938 | 0.72 | 1400 | 0.4586 | -0.8258 | -1.9093 | 0.7745 | 1.0834 | 1.0 | 1000.0 | -424.0995 | -357.7357 | 1.0 | 2.0 | 1.8620 | 0.9612 | 3.5611 | 1.3337 | 2.2274 | 0.4809 | | 0.4511 | 0.77 | 1500 | 0.4580 | -0.8174 | -1.8815 | 0.7765 | 1.0641 | 1.0 | 1000.0 | -421.3289 | -356.8928 | 1.0 | 2.0 | 1.8762 | 0.9513 | 3.6341 | 1.3337 | 2.3003 | 0.4809 | | 0.4724 | 0.83 | 1600 | 0.4573 | -0.8790 | -1.9952 | 0.7735 | 1.1162 | 1.0 | 1000.0 | -432.6913 | -363.0503 | 1.0 | 2.0 | 2.0060 | 1.0139 | 3.7650 | 1.3337 | 2.4312 | 0.4809 | | 0.5045 | 0.88 | 1700 | 0.4572 | -0.8903 | -2.0141 | 0.7725 | 1.1238 | 1.0 | 1000.0 | -434.5795 | -364.1794 | 1.0 | 2.0 | 2.0502 | 1.0267 | 3.8128 | 1.3337 | 2.4790 | 0.4809 | | 0.5007 | 0.93 | 1800 | 0.4577 | -0.9008 | -2.0247 | 0.7715 | 1.1239 | 1.0 | 1000.0 | -435.6480 | -365.2350 | 1.0 | 2.0 | 2.0707 | 1.0309 | 3.8706 | 1.3337 | 2.5369 | 0.4809 | | 0.4747 | 0.98 | 1900 | 0.4576 | -0.8929 | -2.0129 | 0.7735 | 1.1200 | 1.0 | 1000.0 | -434.4668 | -364.4426 | 1.0 | 2.0 | 2.0555 | 1.0247 | 3.8552 | 1.3337 | 2.5215 | 0.4809 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1
{}
text-generation
ChenDRAG/zephyr-infoNCA-preference
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T09:01:01+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
zephyr-infoNCA-preference ========================= This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the openbmb/UltraFeedback dataset. It achieves the following results on the evaluation set: * Loss: 0.4575 * Rewards/chosen: -0.8931 * Rewards/rejected: -2.0138 * Rewards/accuracies: 0.7745 * Rewards/margins: 1.1206 * Verify/constant 1: 1.0 * Verify/constant 1len: 1000.0 * Logps/rejected: -434.5525 * Logps/chosen: -364.4662 * Verify/bz: 1.0 * Verify/gather Bz: 2.0 * Regularization/forward Kl: 2.0564 * Regularization/reverse Kl: 1.0252 * Regularization/policy Data Loss: 3.8558 * Regularization/reference Data Loss: 1.3337 * Regularization/policy Ref Data Loss Gap: 2.5221 * Mask/mask Ratio: 0.4809 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-06 * train\_batch\_size: 1 * eval\_batch\_size: 1 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 2 * gradient\_accumulation\_steps: 16 * total\_train\_batch\_size: 32 * total\_eval\_batch\_size: 2 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.1.0 * Datasets 2.14.6 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 2\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: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 2\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: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ 54, 179, 4, 30 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 2\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: 1### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
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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. --> # bert-base-cased-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.9775 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.1964 | 1.0 | 1171 | 7.0937 | | 7.0407 | 2.0 | 2342 | 7.0175 | | 6.954 | 3.0 | 3513 | 6.9555 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]}
fill-mask
GavinQiangLi/bert-base-cased-wikitext2
[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-08T09:05:34+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
bert-base-cased-wikitext2 ========================= This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set: * Loss: 6.9775 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 67, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{}
null
milk639/Goddess
[ "arxiv:1910.09700", "region:us" ]
2024-02-08T09:09:17+00:00
[ "1910.09700" ]
[]
TAGS #arxiv-1910.09700 #region-us
# Model Card for Model ID This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#arxiv-1910.09700 #region-us \n", "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 15, 29, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#arxiv-1910.09700 #region-us \n# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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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-cased-lora-591K-squad-model1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 64 - eval_batch_size: 16 - seed: 51 - 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": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-base-cased-lora-591K-squad-model1", "results": []}]}
question-answering
varun-v-rao/bert-base-cased-lora-591K-squad-model1
[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-08T09:10:35+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us
# bert-base-cased-lora-591K-squad-model1 This model is a fine-tuned version of bert-base-cased 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: 64 - eval_batch_size: 16 - seed: 51 - 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
[ "# bert-base-cased-lora-591K-squad-model1\n\nThis model is a fine-tuned version of bert-base-cased 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: 64\n- eval_batch_size: 16\n- seed: 51\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 #bert #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n", "# bert-base-cased-lora-591K-squad-model1\n\nThis model is a fine-tuned version of bert-base-cased 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: 64\n- eval_batch_size: 16\n- seed: 51\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" ]
[ 73, 44, 6, 12, 8, 3, 90, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n# bert-base-cased-lora-591K-squad-model1\n\nThis model is a fine-tuned version of bert-base-cased 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: 64\n- eval_batch_size: 16\n- seed: 51\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
null
transformers
# zephyr-NCA-preference This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset. It achieves the following results on the evaluation set: - Loss: 1.3030 - Rewards/chosen: 0.0489 - Rewards/rejected: -0.5399 - Rewards/accuracies: 0.7820 - Rewards/margins: 0.5888 - Verify/constant 1: 1.0 - Verify/constant 1len: 1000.0 - Logps/rejected: -287.1594 - Logps/chosen: -270.2584 - Verify/bz: 1.0 - Verify/gather Bz: 2.0 - Regularization/forward Kl: 0.6109 - Regularization/reverse Kl: 0.4631 - Regularization/policy Data Loss: 1.8007 - Regularization/reference Data Loss: 1.3337 - Regularization/policy Ref Data Loss Gap: 0.4670 - Mask/mask Ratio: 0.4809 ## 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-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Verify/constant 1 | Verify/constant 1len | Logps/rejected | Logps/chosen | Verify/bz | Verify/gather Bz | Regularization/forward Kl | Regularization/reverse Kl | Regularization/policy Data Loss | Regularization/reference Data Loss | Regularization/policy Ref Data Loss Gap | Mask/mask Ratio | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:-----------------:|:--------------------:|:--------------:|:------------:|:---------:|:----------------:|:-------------------------:|:-------------------------:|:-------------------------------:|:----------------------------------:|:---------------------------------------:|:---------------:| | 1.3844 | 0.05 | 100 | 1.3839 | 0.0037 | -0.0061 | 0.7075 | 0.0098 | 1.0 | 1000.0 | -233.7844 | -274.7838 | 1.0 | 2.0 | 0.0009 | 0.0009 | 1.3404 | 1.3337 | 0.0067 | 0.4809 | | 1.3593 | 0.1 | 200 | 1.3605 | -0.0445 | -0.1811 | 0.7320 | 0.1366 | 1.0 | 1000.0 | -251.2808 | -279.5988 | 1.0 | 2.0 | 0.1063 | 0.0867 | 1.4942 | 1.3337 | 0.1604 | 0.4809 | | 1.3432 | 0.15 | 300 | 1.3399 | -0.0181 | -0.2809 | 0.7695 | 0.2628 | 1.0 | 1000.0 | -261.2633 | -276.9577 | 1.0 | 2.0 | 0.2787 | 0.2104 | 1.5199 | 1.3337 | 0.1862 | 0.4809 | | 1.3404 | 0.21 | 400 | 1.3251 | 0.0042 | -0.3854 | 0.7720 | 0.3896 | 1.0 | 1000.0 | -271.7116 | -274.7323 | 1.0 | 2.0 | 0.5454 | 0.4274 | 1.5819 | 1.3337 | 0.2481 | 0.4809 | | 1.3295 | 0.26 | 500 | 1.3173 | 0.0213 | -0.4300 | 0.7770 | 0.4513 | 1.0 | 1000.0 | -276.1767 | -273.0250 | 1.0 | 2.0 | 0.5684 | 0.4290 | 1.6808 | 1.3337 | 0.3471 | 0.4809 | | 1.3187 | 0.31 | 600 | 1.3122 | 0.0267 | -0.4649 | 0.7790 | 0.4917 | 1.0 | 1000.0 | -279.6683 | -272.4786 | 1.0 | 2.0 | 0.5839 | 0.4556 | 1.7090 | 1.3337 | 0.3753 | 0.4809 | | 1.3105 | 0.36 | 700 | 1.3106 | 0.0180 | -0.5079 | 0.7685 | 0.5259 | 1.0 | 1000.0 | -283.9655 | -273.3516 | 1.0 | 2.0 | 0.5818 | 0.4701 | 1.8137 | 1.3337 | 0.4800 | 0.4809 | | 1.3086 | 0.41 | 800 | 1.3094 | 0.0287 | -0.5003 | 0.7820 | 0.5290 | 1.0 | 1000.0 | -283.2076 | -272.2820 | 1.0 | 2.0 | 0.5724 | 0.4410 | 1.7950 | 1.3337 | 0.4613 | 0.4809 | | 1.3164 | 0.46 | 900 | 1.3071 | 0.0494 | -0.4863 | 0.7865 | 0.5356 | 1.0 | 1000.0 | -281.7993 | -270.2156 | 1.0 | 2.0 | 0.5937 | 0.4471 | 1.6937 | 1.3337 | 0.3599 | 0.4809 | | 1.3065 | 0.52 | 1000 | 1.3058 | 0.0442 | -0.5122 | 0.7875 | 0.5564 | 1.0 | 1000.0 | -284.3954 | -270.7371 | 1.0 | 2.0 | 0.6214 | 0.4609 | 1.7262 | 1.3337 | 0.3925 | 0.4809 | | 1.3274 | 0.57 | 1100 | 1.3097 | 0.0187 | -0.5605 | 0.7765 | 0.5792 | 1.0 | 1000.0 | -289.2202 | -273.2801 | 1.0 | 2.0 | 0.6048 | 0.4467 | 1.9267 | 1.3337 | 0.5930 | 0.4809 | | 1.3128 | 0.62 | 1200 | 1.3053 | 0.0391 | -0.5393 | 0.7795 | 0.5784 | 1.0 | 1000.0 | -287.1077 | -271.2448 | 1.0 | 2.0 | 0.5974 | 0.4596 | 1.8496 | 1.3337 | 0.5159 | 0.4809 | | 1.3018 | 0.67 | 1300 | 1.3043 | 0.0370 | -0.5532 | 0.7765 | 0.5902 | 1.0 | 1000.0 | -288.4903 | -271.4501 | 1.0 | 2.0 | 0.6164 | 0.4737 | 1.8233 | 1.3337 | 0.4896 | 0.4809 | | 1.3137 | 0.72 | 1400 | 1.3040 | 0.0532 | -0.5183 | 0.7790 | 0.5715 | 1.0 | 1000.0 | -285.0031 | -269.8345 | 1.0 | 2.0 | 0.5985 | 0.4642 | 1.7409 | 1.3337 | 0.4072 | 0.4809 | | 1.304 | 0.77 | 1500 | 1.3034 | 0.0489 | -0.5344 | 0.7815 | 0.5833 | 1.0 | 1000.0 | -286.6187 | -270.2639 | 1.0 | 2.0 | 0.6056 | 0.4668 | 1.7960 | 1.3337 | 0.4623 | 0.4809 | | 1.3194 | 0.83 | 1600 | 1.3033 | 0.0496 | -0.5367 | 0.7770 | 0.5864 | 1.0 | 1000.0 | -286.8489 | -270.1884 | 1.0 | 2.0 | 0.6093 | 0.4660 | 1.7863 | 1.3337 | 0.4526 | 0.4809 | | 1.3194 | 0.88 | 1700 | 1.3030 | 0.0498 | -0.5367 | 0.7820 | 0.5865 | 1.0 | 1000.0 | -286.8430 | -270.1689 | 1.0 | 2.0 | 0.6106 | 0.4640 | 1.7905 | 1.3337 | 0.4568 | 0.4809 | | 1.32 | 0.93 | 1800 | 1.3031 | 0.0475 | -0.5425 | 0.7815 | 0.5901 | 1.0 | 1000.0 | -287.4280 | -270.3985 | 1.0 | 2.0 | 0.6118 | 0.4635 | 1.8042 | 1.3337 | 0.4705 | 0.4809 | | 1.3119 | 0.98 | 1900 | 1.3030 | 0.0490 | -0.5398 | 0.7810 | 0.5888 | 1.0 | 1000.0 | -287.1560 | -270.2523 | 1.0 | 2.0 | 0.6107 | 0.4630 | 1.8007 | 1.3337 | 0.4670 | 0.4809 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1
{}
text-generation
ChenDRAG/zephyr-NCA-preference
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T09:12:01+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
zephyr-NCA-preference ===================== This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the openbmb/UltraFeedback dataset. It achieves the following results on the evaluation set: * Loss: 1.3030 * Rewards/chosen: 0.0489 * Rewards/rejected: -0.5399 * Rewards/accuracies: 0.7820 * Rewards/margins: 0.5888 * Verify/constant 1: 1.0 * Verify/constant 1len: 1000.0 * Logps/rejected: -287.1594 * Logps/chosen: -270.2584 * Verify/bz: 1.0 * Verify/gather Bz: 2.0 * Regularization/forward Kl: 0.6109 * Regularization/reverse Kl: 0.4631 * Regularization/policy Data Loss: 1.8007 * Regularization/reference Data Loss: 1.3337 * Regularization/policy Ref Data Loss Gap: 0.4670 * Mask/mask Ratio: 0.4809 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-06 * train\_batch\_size: 1 * eval\_batch\_size: 1 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 2 * gradient\_accumulation\_steps: 16 * total\_train\_batch\_size: 32 * total\_eval\_batch\_size: 2 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.1.0 * Datasets 2.14.6 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 2\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: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 2\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: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ 54, 179, 4, 30 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 2\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: 1### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
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null
null
transformers
# zephyr-NCA-reward This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset. It achieves the following results on the evaluation set: - Loss: 1.3007 - Loss/mini Gap Loss: 1.3007 - Loss/ori Loss: 1.3007 - Loss/reward Entrophy: 0.0 - Regularization/forward Kl: 0.5698 - Regularization/reverse Kl: 0.4143 - Regularization/policy Data Loss: 1.6956 - Regularization/reference Data Loss: 1.2661 - Regularization/policy Ref Data Loss Gap: 0.4295 - Mask/mask Ratio: 0.4577 - Reward/reward A0: -0.0038 - Reward/reward A1: -0.1788 - Reward/reward A2: -0.3592 - Reward/reward A3: -0.6457 - Rewards/chosen: -0.0038 - Rewards/rejected: -0.3945 - Rewards/margins: 0.3908 - Reward/a01 Acc: 0.6449 - Reward/a02 Acc: 0.7396 - Reward/a03 Acc: 0.8344 - Rewards/accuracies: 0.7396 ## 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-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Loss/mini Gap Loss | Loss/ori Loss | Loss/reward Entrophy | Regularization/forward Kl | Regularization/reverse Kl | Regularization/policy Data Loss | Regularization/reference Data Loss | Regularization/policy Ref Data Loss Gap | Mask/mask Ratio | Reward/reward A0 | Reward/reward A1 | Reward/reward A2 | Reward/reward A3 | Rewards/chosen | Rewards/rejected | Rewards/margins | Reward/a01 Acc | Reward/a02 Acc | Reward/a03 Acc | Rewards/accuracies | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:-------------:|:--------------------:|:-------------------------:|:-------------------------:|:-------------------------------:|:----------------------------------:|:---------------------------------------:|:---------------:|:----------------:|:----------------:|:----------------:|:----------------:|:--------------:|:----------------:|:---------------:|:--------------:|:--------------:|:--------------:|:------------------:| | 1.3845 | 0.05 | 100 | 1.3843 | 1.3843 | 1.3843 | 0.0 | 0.0006 | 0.0006 | 1.2682 | 1.2661 | 0.0022 | 0.4577 | 0.0030 | -0.0001 | -0.0023 | -0.0049 | 0.0030 | -0.0024 | 0.0054 | 0.5932 | 0.6579 | 0.7117 | 0.6542 | | 1.3641 | 0.11 | 200 | 1.3632 | 1.3632 | 1.3632 | 0.0 | 0.0688 | 0.0617 | 1.3653 | 1.2661 | 0.0992 | 0.4577 | -0.0453 | -0.0905 | -0.1223 | -0.1596 | -0.0453 | -0.1241 | 0.0788 | 0.6082 | 0.6791 | 0.7396 | 0.6756 | | 1.3464 | 0.16 | 300 | 1.3430 | 1.3430 | 1.3430 | 0.0 | 0.2320 | 0.1950 | 1.3931 | 1.2661 | 0.1270 | 0.4577 | -0.0499 | -0.1410 | -0.2129 | -0.3031 | -0.0499 | -0.2190 | 0.1691 | 0.6304 | 0.6988 | 0.7671 | 0.6988 | | 1.3387 | 0.21 | 400 | 1.3285 | 1.3285 | 1.3285 | 0.0 | 0.4617 | 0.3766 | 1.4589 | 1.2661 | 0.1928 | 0.4577 | -0.0167 | -0.1373 | -0.2414 | -0.3912 | -0.0167 | -0.2566 | 0.2399 | 0.6356 | 0.7076 | 0.7930 | 0.7120 | | 1.3309 | 0.27 | 500 | 1.3204 | 1.3204 | 1.3204 | 0.0 | 0.4646 | 0.3825 | 1.4782 | 1.2661 | 0.2121 | 0.4577 | -0.0003 | -0.1341 | -0.2534 | -0.4304 | -0.0003 | -0.2727 | 0.2723 | 0.6372 | 0.7107 | 0.8100 | 0.7193 | | 1.325 | 0.32 | 600 | 1.3164 | 1.3164 | 1.3164 | 0.0 | 0.5434 | 0.4317 | 1.5453 | 1.2661 | 0.2792 | 0.4577 | -0.0366 | -0.1874 | -0.3337 | -0.5403 | -0.0366 | -0.3538 | 0.3172 | 0.6335 | 0.7205 | 0.8100 | 0.7214 | | 1.3311 | 0.37 | 700 | 1.3122 | 1.3122 | 1.3122 | 0.0 | 0.5382 | 0.4264 | 1.5599 | 1.2661 | 0.2938 | 0.4577 | -0.0042 | -0.1527 | -0.2999 | -0.5274 | -0.0042 | -0.3267 | 0.3224 | 0.6413 | 0.7200 | 0.8245 | 0.7286 | | 1.3112 | 0.42 | 800 | 1.3086 | 1.3086 | 1.3086 | 0.0 | 0.5743 | 0.4255 | 1.6721 | 1.2661 | 0.4060 | 0.4577 | -0.0112 | -0.1685 | -0.3250 | -0.5754 | -0.0112 | -0.3563 | 0.3451 | 0.6449 | 0.7334 | 0.8287 | 0.7357 | | 1.3156 | 0.48 | 900 | 1.3082 | 1.3082 | 1.3082 | 0.0 | 0.5717 | 0.4240 | 1.6341 | 1.2661 | 0.3680 | 0.4577 | -0.0214 | -0.1861 | -0.3578 | -0.6112 | -0.0214 | -0.3850 | 0.3637 | 0.6460 | 0.7360 | 0.8261 | 0.7360 | | 1.3131 | 0.53 | 1000 | 1.3066 | 1.3066 | 1.3066 | 0.0 | 0.5842 | 0.4200 | 1.7286 | 1.2661 | 0.4626 | 0.4577 | -0.0454 | -0.2257 | -0.4053 | -0.6707 | -0.0454 | -0.4339 | 0.3885 | 0.6506 | 0.7422 | 0.8328 | 0.7419 | | 1.3092 | 0.58 | 1100 | 1.3040 | 1.3040 | 1.3040 | 0.0 | 0.5668 | 0.4164 | 1.6753 | 1.2661 | 0.4092 | 0.4577 | -0.0194 | -0.1939 | -0.3686 | -0.6412 | -0.0194 | -0.4012 | 0.3818 | 0.6460 | 0.7428 | 0.8349 | 0.7412 | | 1.3097 | 0.64 | 1200 | 1.3027 | 1.3028 | 1.3028 | 0.0 | 0.5639 | 0.4199 | 1.6401 | 1.2661 | 0.3740 | 0.4577 | -0.0002 | -0.1708 | -0.3436 | -0.6201 | -0.0002 | -0.3782 | 0.3780 | 0.6444 | 0.7422 | 0.8395 | 0.7421 | | 1.2929 | 0.69 | 1300 | 1.3019 | 1.3019 | 1.3019 | 0.0 | 0.5674 | 0.4188 | 1.6644 | 1.2661 | 0.3983 | 0.4577 | -0.0039 | -0.1761 | -0.3536 | -0.6335 | -0.0039 | -0.3877 | 0.3838 | 0.6470 | 0.7417 | 0.8354 | 0.7414 | | 1.3107 | 0.74 | 1400 | 1.3017 | 1.3017 | 1.3017 | 0.0 | 0.5596 | 0.4140 | 1.6506 | 1.2661 | 0.3845 | 0.4577 | 0.0060 | -0.1611 | -0.3364 | -0.6151 | 0.0060 | -0.3708 | 0.3768 | 0.6444 | 0.7422 | 0.8333 | 0.7400 | | 1.296 | 0.8 | 1500 | 1.3013 | 1.3013 | 1.3013 | 0.0 | 0.5751 | 0.4164 | 1.7004 | 1.2661 | 0.4343 | 0.4577 | -0.0053 | -0.1799 | -0.3600 | -0.6481 | -0.0053 | -0.3960 | 0.3907 | 0.6465 | 0.7422 | 0.8349 | 0.7412 | | 1.304 | 0.85 | 1600 | 1.3007 | 1.3007 | 1.3007 | 0.0 | 0.5724 | 0.4169 | 1.6883 | 1.2661 | 0.4222 | 0.4577 | -0.0015 | -0.1760 | -0.3549 | -0.6421 | -0.0015 | -0.3910 | 0.3895 | 0.6434 | 0.7407 | 0.8370 | 0.7403 | | 1.3101 | 0.9 | 1700 | 1.3006 | 1.3006 | 1.3006 | 0.0 | 0.5671 | 0.4145 | 1.6800 | 1.2661 | 0.4139 | 0.4577 | 0.0013 | -0.1716 | -0.3500 | -0.6354 | 0.0013 | -0.3857 | 0.3870 | 0.6423 | 0.7396 | 0.8359 | 0.7393 | | 1.2987 | 0.96 | 1800 | 1.3007 | 1.3008 | 1.3008 | 0.0 | 0.5698 | 0.4143 | 1.6954 | 1.2661 | 0.4293 | 0.4577 | -0.0038 | -0.1785 | -0.3590 | -0.6456 | -0.0038 | -0.3944 | 0.3906 | 0.6449 | 0.7391 | 0.8349 | 0.7396 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
{}
text-generation
ChenDRAG/zephyr-NCA-reward
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-08T09:13:06+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
zephyr-NCA-reward ================= This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the openbmb/UltraFeedback dataset. It achieves the following results on the evaluation set: * Loss: 1.3007 * Loss/mini Gap Loss: 1.3007 * Loss/ori Loss: 1.3007 * Loss/reward Entrophy: 0.0 * Regularization/forward Kl: 0.5698 * Regularization/reverse Kl: 0.4143 * Regularization/policy Data Loss: 1.6956 * Regularization/reference Data Loss: 1.2661 * Regularization/policy Ref Data Loss Gap: 0.4295 * Mask/mask Ratio: 0.4577 * Reward/reward A0: -0.0038 * Reward/reward A1: -0.1788 * Reward/reward A2: -0.3592 * Reward/reward A3: -0.6457 * Rewards/chosen: -0.0038 * Rewards/rejected: -0.3945 * Rewards/margins: 0.3908 * Reward/a01 Acc: 0.6449 * Reward/a02 Acc: 0.7396 * Reward/a03 Acc: 0.8344 * Rewards/accuracies: 0.7396 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-06 * train\_batch\_size: 1 * eval\_batch\_size: 1 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 4 * gradient\_accumulation\_steps: 8 * total\_train\_batch\_size: 32 * total\_eval\_batch\_size: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.0.1+cu117 * Datasets 2.14.6 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 4\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: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 4\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: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ 54, 179, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 4\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: 1### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
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null
null
peft
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results_packing This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.5551 ## 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: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8415 | 0.08 | 50 | 0.6991 | | 0.6472 | 0.17 | 100 | 0.6063 | | 0.5802 | 0.25 | 150 | 0.5596 | | 0.5499 | 0.33 | 200 | 0.5551 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "model-index": [{"name": "results_packing", "results": []}]}
null
ananyarn/results_packing
[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]
2024-02-08T09:17:22+00:00
[]
[]
TAGS #peft #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #region-us
results\_packing ================ This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on the generator dataset. It achieves the following results on the evaluation set: * Loss: 0.5551 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: constant * lr\_scheduler\_warmup\_ratio: 0.03 * training\_steps: 200 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.2.0 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 200", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 200", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 64, 144, 4, 36 ]
[ "passage: TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 200### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0\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. --> # 1000_STEPS_5e7 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6782 - Rewards/chosen: -0.0504 - Rewards/rejected: -0.0840 - Rewards/accuracies: 0.5297 - Rewards/margins: 0.0336 - Logps/rejected: -15.9795 - Logps/chosen: -14.6212 - Logits/rejected: -0.0566 - Logits/chosen: -0.0565 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6932 | 0.1 | 50 | 0.6927 | 0.0015 | 0.0005 | 0.4242 | 0.0010 | -15.1347 | -14.1020 | -0.0215 | -0.0215 | | 0.6901 | 0.2 | 100 | 0.6901 | -0.0121 | -0.0185 | 0.4835 | 0.0063 | -15.3239 | -14.2383 | -0.0268 | -0.0268 | | 0.6826 | 0.29 | 150 | 0.6841 | -0.0153 | -0.0346 | 0.5143 | 0.0193 | -15.4851 | -14.2699 | -0.0332 | -0.0331 | | 0.6776 | 0.39 | 200 | 0.6813 | -0.0237 | -0.0492 | 0.5209 | 0.0255 | -15.6318 | -14.3538 | -0.0437 | -0.0436 | | 0.678 | 0.49 | 250 | 0.6794 | -0.0441 | -0.0745 | 0.5209 | 0.0304 | -15.8841 | -14.5577 | -0.0475 | -0.0473 | | 0.6715 | 0.59 | 300 | 0.6786 | -0.0529 | -0.0857 | 0.5253 | 0.0328 | -15.9964 | -14.6462 | -0.0538 | -0.0536 | | 0.6846 | 0.68 | 350 | 0.6782 | -0.0478 | -0.0812 | 0.5165 | 0.0334 | -15.9511 | -14.5949 | -0.0559 | -0.0557 | | 0.6846 | 0.78 | 400 | 0.6781 | -0.0501 | -0.0839 | 0.5231 | 0.0338 | -15.9784 | -14.6180 | -0.0567 | -0.0565 | | 0.6755 | 0.88 | 450 | 0.6783 | -0.0498 | -0.0833 | 0.5209 | 0.0335 | -15.9725 | -14.6153 | -0.0564 | -0.0562 | | 0.665 | 0.98 | 500 | 0.6782 | -0.0504 | -0.0840 | 0.5297 | 0.0336 | -15.9795 | -14.6212 | -0.0566 | -0.0565 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.0+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1
{"tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "1000_STEPS_5e7", "results": []}]}
text-generation
tsavage68/500STEPS_5e7_0.1beta_DPO_zeroshot
[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-08T09:19:30+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
1000\_STEPS\_5e7 ================ This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.6782 * Rewards/chosen: -0.0504 * Rewards/rejected: -0.0840 * Rewards/accuracies: 0.5297 * Rewards/margins: 0.0336 * Logps/rejected: -15.9795 * Logps/chosen: -14.6212 * Logits/rejected: -0.0566 * Logits/chosen: -0.0565 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-07 * train\_batch\_size: 4 * eval\_batch\_size: 1 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 8 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 100 * training\_steps: 500 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.0.0+cu117 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #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: 5e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 78, 145, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #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: 5e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\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 0.7.1
{"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"}
null
NikitaZagainov/notebook-segmentation-llama-2-13b-1ep
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-13b-chat-hf", "region:us" ]
2024-02-08T09:20:29+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #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 #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #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" ]
[ 43, 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-meta-llama/Llama-2-13b-chat-hf #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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