Datasets:

---

ftopal

/

huggingface-models

like 0

[ "pyannote", "pyannote.audio", "pyannote-audio-pipeline", "audio", "voice", "speech", "speaker", "speaker-diarization", "speaker-change-detection", "voice-activity-detection", "overlapped-speech-detection", "automatic-speech-recognition", "region:us" ]

[]

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[ "TAGS\n#pyannote #pyannote.audio #pyannote-audio-pipeline #audio #voice #speech #speaker #speaker-diarization #speaker-change-detection #voice-activity-detection #overlapped-speech-detection #automatic-speech-recognition #region-us \n" ]

[ "text-generation", "en", "dataset:karpathy/tiny_shakespeare", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Bad GPT\n\nBased on the Let's build GPT video from Andrej Karpathy.\n\nThis is just an attempt to recreate the transformer Andrej made in his video with the goal of learning more about torch, transformers, and neural networks in general.\n\nTo run, make sure 'python' '3.10' and 'poetry' are installed. You can then run 'poetry install' to get the dependencies (it's just torch and numpy).\n\nFinally, you can run the code with 'poetry run python ./URL'\n\nNote that the first run will train the model and then save the trained weights to 'URL'. Subsequent runs will load these weights." ]

[ "TAGS\n#text-generation #en #dataset-karpathy/tiny_shakespeare #license-mit #region-us \n", "# Bad GPT\n\nBased on the Let's build GPT video from Andrej Karpathy.\n\nThis is just an attempt to recreate the transformer Andrej made in his video with the goal of learning more about torch, transformers, and neural networks in general.\n\nTo run, make sure 'python' '3.10' and 'poetry' are installed. You can then run 'poetry install' to get the dependencies (it's just torch and numpy).\n\nFinally, you can run the code with 'poetry run python ./URL'\n\nNote that the first run will train the model and then save the trained weights to 'URL'. Subsequent runs will load these weights." ]

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224 #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: 0.0003\n* train\\_batch\\_size: 16\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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #trl #sft #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" ]

[ "transformers", "safetensors", "xlm-roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #xlm-roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 500, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* TensorFlow 2.15.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 500, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* TensorFlow 2.15.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "pytorch", "safetensors", "text-classification", "dataset:Novora/CodeClassifier_v1", "license:apache-2.0", "region:eu" ]

[]

[]

[]

[ "TAGS\n#pytorch #safetensors #text-classification #dataset-Novora/CodeClassifier_v1 #license-apache-2.0 #region-eu \n" ]

[ "pretrained", "hubert", "RVC", "ai", "vits", "vc", "voice-cloning", "voice-conversion", "Voice2Voice", "audio-to-audio", "license:mit", "region:us" ]

[]

[]

[]

[ "TAGS\n#pretrained #hubert #RVC #ai #vits #vc #voice-cloning #voice-conversion #Voice2Voice #audio-to-audio #license-mit #region-us \n" ]

[ "transformers", "safetensors", "git", "text-generation", "generated_from_trainer", "base_model:microsoft/git-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 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: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #git #text-generation #generated_from_trainer #base_model-microsoft/git-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: 5e-05\n* train\\_batch\\_size: 16\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: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "fr", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]

[]

[ "fr" ]

[ "# Uploaded model\n\n- Developed by: yadz45\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #fr #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #4-bit #region-us \n", "# Uploaded model\n\n- Developed by: yadz45\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "transformers", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "# 20240429_115939\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 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: 1e-05\n- train_batch_size: 16\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: 4\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.3.0\n- Datasets 2.12.0\n- Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n", "# 20240429_115939\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 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: 1e-05\n- train_batch_size: 16\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: 4\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.3.0\n- Datasets 2.12.0\n- Tokenizers 0.15.1" ]

[ "transformers", "tensorboard", "safetensors", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:generated", "base_model:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

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[ "# layoutlmv3-finetuned-invoice\n\nThis model is a fine-tuned version of microsoft/layoutlmv3-base on the generated dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0133\n- Precision: 0.0\n- Recall: 0.0\n- F1: 0.0\n- Accuracy: 0.8751", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 20", "### Training results", "### Framework versions\n\n- Transformers 4.41.0.dev0\n- Pytorch 2.2.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-generated #base_model-microsoft/layoutlmv3-base #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "# layoutlmv3-finetuned-invoice\n\nThis model is a fine-tuned version of microsoft/layoutlmv3-base on the generated dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0133\n- Precision: 0.0\n- Recall: 0.0\n- F1: 0.0\n- Accuracy: 0.8751", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 20", "### Training results", "### Framework versions\n\n- Transformers 4.41.0.dev0\n- Pytorch 2.2.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #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" ]

[ "peft", "tensorboard", "safetensors", "llama", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "8-bit", "region:us" ]

[]

[]

[ "### 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* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\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\\_steps: 10\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]

[ "TAGS\n#peft #tensorboard #safetensors #llama #generated_from_trainer #base_model-meta-llama/Meta-Llama-3-8B #license-other #8-bit #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* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\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\\_steps: 10\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]

[ "arxiv:2102.02754", "region:us" ]

[ "2102.02754" ]

[]

[ "### Prerequisites\n\n\n* Linux or macOS\n* NVIDIA GPU + CUDA CuDNN (CPU may be possible with some modifications, but is not inherently supported)\n* Python 3", "### Installation\n\n\n* Dependencies: \n\nWe recommend running this repository using Anaconda.\nAll dependencies for defining the environment are provided in 'environment/sam\\_env.yaml'.\n\n\nPretrained Models\n-----------------\n\n\nPlease download the pretrained aging model from the following links.\n\n\n\nYou can run this to download it to the right place:\n\n\nIn addition, we provide various auxiliary models needed for training your own SAM model from scratch. \n\nThis includes the pretrained pSp encoder model for generating the encodings of the input image and the aging classifier\nused to compute the aging loss during training.\n\n\n\nBy default, we assume that all auxiliary models are downloaded and saved to the directory 'pretrained\\_models'.\nHowever, you may use your own paths by changing the necessary values in 'configs/path\\_configs.py'.\n\n\nTraining\n--------", "### Preparing your Data\n\n\nPlease refer to 'configs/paths\\_config.py' to define the necessary data paths and model paths for training and inference. \n\nThen, refer to 'configs/data\\_configs.py' to define the source/target data paths for the train and test sets as well as the\ntransforms to be used for training and inference.\n\n\nAs an example, we can first go to 'configs/paths\\_config.py' and define:\n\n\nThen, in 'configs/data\\_configs.py', we define:\n\n\nWhen defining the datasets for training and inference, we will use the values defined in the above dictionary.", "### Training SAM\n\n\nThe main training script can be found in 'scripts/URL'. \n\nIntermediate training results are saved to 'opts.exp\\_dir'. This includes checkpoints, train outputs, and test outputs. \n\nAdditionally, if you have tensorboard installed, you can visualize tensorboard logs in 'opts.exp\\_dir/logs'.\n\n\nTraining SAM with the settings used in the paper can be done by running the following command:", "### Additional Notes\n\n\n* See 'options/train\\_options.py' for all training-specific flags.\n* Note that using the flag '--start\\_from\\_encoded\\_w\\_plus' requires you to specify the path to the pretrained pSp encoder. \n\nBy default, this path is taken from 'configs.paths\\_config.model\\_paths['pretrained\\_psp']'.\n* If you wish to resume from a specific checkpoint (e.g. a pretrained SAM model), you may do so using '--checkpoint\\_path'.\n\n\nNotebooks\n---------", "### Inference Notebook\n\n\nTo help visualize the results of SAM we provide a Jupyter notebook found in 'notebooks/inference\\_playground.ipynb'. \n\nThe notebook will download the pretrained aging model and run inference on the images found in 'notebooks/images'.\n\n\nIn addition, Replicate have created a demo for SAM where you can easily upload an image and run SAM on a desired set of ages! Check\nout the demo here.", "### MP4 Notebook\n\n\nTo show full lifespan results using SAM we provide an additional notebook 'notebooks/animation\\_inference\\_playground.ipynb' that will\nrun aging on multiple ages between 0 and 100 and interpolate between the results to display full aging.\nThe results will be saved as an MP4 files in 'notebooks/animations' showing the aging and de-aging results.\n\n\nTesting\n-------", "### Inference\n\n\nHaving trained your model or if you're using a pretrained SAM model, you can use 'scripts/URL' to run inference\non a set of images. \n\nFor example,\n\n\nAdditional notes to consider:\n\n\n* During inference, the options used during training are loaded from the saved checkpoint and are then updated using the\ntest options passed to the inference script.\n* Adding the flag '--couple\\_outputs' will save an additional image containing the input and output images side-by-side in the sub-directory\n'inference\\_coupled'. Otherwise, only the output image is saved to the sub-directory 'inference\\_results'.\n* In the above example, we will run age transformation with target ages 0,10,...,80.\n\t+ The results of each target age are saved to the sub-directories 'inference\\_results/TARGET\\_AGE' and 'inference\\_coupled/TARGET\\_AGE'.\n* By default, the images will be saved at resolution of 1024x1024, the original output size of StyleGAN.\n\t+ If you wish to save outputs resized to resolutions of 256x256, you can do so by adding the flag '--resize\\_outputs'.", "### Side-by-Side Inference\n\n\nThe above inference script will save each aging result in a different sub-directory for each target age. Sometimes,\nhowever, it is more convenient to save all aging results of a given input side-by-side like the following:\n\n\n\n\n\n\n\nTo do so, we provide a script 'inference\\_side\\_by\\_side.py' that works in a similar manner as the regular inference script:\n\n\nHere, all aging results 0,10,...,80 will be save side-by-side with the original input image.", "### Reference-Guided Inference\n\n\nIn the paper, we demonstrated how one can perform style-mixing on the fine-level style inputs with a reference image\nto control global features such as hair color. For example,\n\n\n\n\n\n\n\nTo perform style mixing using reference images, we provide the script 'reference\\_guided\\_inference.py'. Here,\nwe first perform aging using the specified target age(s). Then, style mixing is performed using the specified\nreference images and the specified layers. For example, one can run:\n\n\nHere, the reference images should be specified in the file defined by '--ref\\_images\\_paths\\_file' and should have the\nfollowing format:\n\n\nIn the above example, we will aging using 4 different target ages. For each target age, we first transform the\ntest samples defined by '--data\\_path' and then perform style mixing on layers 8,9 defined by '--latent\\_mask'.\nThe results of each target age are saved in its own sub-directory.", "### Style Mixing\n\n\nInstead of performing style mixing using a reference image, you can perform style mixing using randomly generated\nw latent vectors by running the script 'style\\_mixing.py'. This script works in a similar manner to the reference\nguided inference except you do not need to specify the '--ref\\_images\\_paths\\_file' flag.\n\n\nRepository structure\n--------------------\n\n\n\nCredits\n-------\n\n\nStyleGAN2 model and implementation: \n\nURL \n\nCopyright (c) 2019 Kim Seonghyeon \n\nLicense (MIT) URL\n\n\nIR-SE50 model and implementations: \n\nURL \n\nCopyright (c) 2018 TreB1eN \n\nLicense (MIT) URL\n\n\nRanger optimizer implementation: \n\nURL \n\nLicense (Apache License 2.0) URL\n\n\nLPIPS model and implementation: \n\nURL \n\nCopyright (c) 2020, Sou Uchida \n\nLicense (BSD 2-Clause) URL\n\n\nDEX VGG model and implementation: \n\nURL \n\nCopyright (c) 2020, InterDigital R&D France \n\nURL\n\n\npSp model and implementation: \n\nURL \n\nCopyright (c) 2020 Elad Richardson, Yuval Alaluf \n\nURL\n\n\nAcknowledgments\n---------------\n\n\nThis code borrows heavily from pixel2style2pixel\n\n\nIf you use this code for your research, please cite our paper <a href=\"URL a Matter of Style: Age Transformation Using a Style-Based Regression Model:" ]

[ "TAGS\n#arxiv-2102.02754 #region-us \n", "### Prerequisites\n\n\n* Linux or macOS\n* NVIDIA GPU + CUDA CuDNN (CPU may be possible with some modifications, but is not inherently supported)\n* Python 3", "### Installation\n\n\n* Dependencies: \n\nWe recommend running this repository using Anaconda.\nAll dependencies for defining the environment are provided in 'environment/sam\\_env.yaml'.\n\n\nPretrained Models\n-----------------\n\n\nPlease download the pretrained aging model from the following links.\n\n\n\nYou can run this to download it to the right place:\n\n\nIn addition, we provide various auxiliary models needed for training your own SAM model from scratch. \n\nThis includes the pretrained pSp encoder model for generating the encodings of the input image and the aging classifier\nused to compute the aging loss during training.\n\n\n\nBy default, we assume that all auxiliary models are downloaded and saved to the directory 'pretrained\\_models'.\nHowever, you may use your own paths by changing the necessary values in 'configs/path\\_configs.py'.\n\n\nTraining\n--------", "### Preparing your Data\n\n\nPlease refer to 'configs/paths\\_config.py' to define the necessary data paths and model paths for training and inference. \n\nThen, refer to 'configs/data\\_configs.py' to define the source/target data paths for the train and test sets as well as the\ntransforms to be used for training and inference.\n\n\nAs an example, we can first go to 'configs/paths\\_config.py' and define:\n\n\nThen, in 'configs/data\\_configs.py', we define:\n\n\nWhen defining the datasets for training and inference, we will use the values defined in the above dictionary.", "### Training SAM\n\n\nThe main training script can be found in 'scripts/URL'. \n\nIntermediate training results are saved to 'opts.exp\\_dir'. This includes checkpoints, train outputs, and test outputs. \n\nAdditionally, if you have tensorboard installed, you can visualize tensorboard logs in 'opts.exp\\_dir/logs'.\n\n\nTraining SAM with the settings used in the paper can be done by running the following command:", "### Additional Notes\n\n\n* See 'options/train\\_options.py' for all training-specific flags.\n* Note that using the flag '--start\\_from\\_encoded\\_w\\_plus' requires you to specify the path to the pretrained pSp encoder. \n\nBy default, this path is taken from 'configs.paths\\_config.model\\_paths['pretrained\\_psp']'.\n* If you wish to resume from a specific checkpoint (e.g. a pretrained SAM model), you may do so using '--checkpoint\\_path'.\n\n\nNotebooks\n---------", "### Inference Notebook\n\n\nTo help visualize the results of SAM we provide a Jupyter notebook found in 'notebooks/inference\\_playground.ipynb'. \n\nThe notebook will download the pretrained aging model and run inference on the images found in 'notebooks/images'.\n\n\nIn addition, Replicate have created a demo for SAM where you can easily upload an image and run SAM on a desired set of ages! Check\nout the demo here.", "### MP4 Notebook\n\n\nTo show full lifespan results using SAM we provide an additional notebook 'notebooks/animation\\_inference\\_playground.ipynb' that will\nrun aging on multiple ages between 0 and 100 and interpolate between the results to display full aging.\nThe results will be saved as an MP4 files in 'notebooks/animations' showing the aging and de-aging results.\n\n\nTesting\n-------", "### Inference\n\n\nHaving trained your model or if you're using a pretrained SAM model, you can use 'scripts/URL' to run inference\non a set of images. \n\nFor example,\n\n\nAdditional notes to consider:\n\n\n* During inference, the options used during training are loaded from the saved checkpoint and are then updated using the\ntest options passed to the inference script.\n* Adding the flag '--couple\\_outputs' will save an additional image containing the input and output images side-by-side in the sub-directory\n'inference\\_coupled'. Otherwise, only the output image is saved to the sub-directory 'inference\\_results'.\n* In the above example, we will run age transformation with target ages 0,10,...,80.\n\t+ The results of each target age are saved to the sub-directories 'inference\\_results/TARGET\\_AGE' and 'inference\\_coupled/TARGET\\_AGE'.\n* By default, the images will be saved at resolution of 1024x1024, the original output size of StyleGAN.\n\t+ If you wish to save outputs resized to resolutions of 256x256, you can do so by adding the flag '--resize\\_outputs'.", "### Side-by-Side Inference\n\n\nThe above inference script will save each aging result in a different sub-directory for each target age. Sometimes,\nhowever, it is more convenient to save all aging results of a given input side-by-side like the following:\n\n\n\n\n\n\n\nTo do so, we provide a script 'inference\\_side\\_by\\_side.py' that works in a similar manner as the regular inference script:\n\n\nHere, all aging results 0,10,...,80 will be save side-by-side with the original input image.", "### Reference-Guided Inference\n\n\nIn the paper, we demonstrated how one can perform style-mixing on the fine-level style inputs with a reference image\nto control global features such as hair color. For example,\n\n\n\n\n\n\n\nTo perform style mixing using reference images, we provide the script 'reference\\_guided\\_inference.py'. Here,\nwe first perform aging using the specified target age(s). Then, style mixing is performed using the specified\nreference images and the specified layers. For example, one can run:\n\n\nHere, the reference images should be specified in the file defined by '--ref\\_images\\_paths\\_file' and should have the\nfollowing format:\n\n\nIn the above example, we will aging using 4 different target ages. For each target age, we first transform the\ntest samples defined by '--data\\_path' and then perform style mixing on layers 8,9 defined by '--latent\\_mask'.\nThe results of each target age are saved in its own sub-directory.", "### Style Mixing\n\n\nInstead of performing style mixing using a reference image, you can perform style mixing using randomly generated\nw latent vectors by running the script 'style\\_mixing.py'. This script works in a similar manner to the reference\nguided inference except you do not need to specify the '--ref\\_images\\_paths\\_file' flag.\n\n\nRepository structure\n--------------------\n\n\n\nCredits\n-------\n\n\nStyleGAN2 model and implementation: \n\nURL \n\nCopyright (c) 2019 Kim Seonghyeon \n\nLicense (MIT) URL\n\n\nIR-SE50 model and implementations: \n\nURL \n\nCopyright (c) 2018 TreB1eN \n\nLicense (MIT) URL\n\n\nRanger optimizer implementation: \n\nURL \n\nLicense (Apache License 2.0) URL\n\n\nLPIPS model and implementation: \n\nURL \n\nCopyright (c) 2020, Sou Uchida \n\nLicense (BSD 2-Clause) URL\n\n\nDEX VGG model and implementation: \n\nURL \n\nCopyright (c) 2020, InterDigital R&D France \n\nURL\n\n\npSp model and implementation: \n\nURL \n\nCopyright (c) 2020 Elad Richardson, Yuval Alaluf \n\nURL\n\n\nAcknowledgments\n---------------\n\n\nThis code borrows heavily from pixel2style2pixel\n\n\nIf you use this code for your research, please cite our paper <a href=\"URL a Matter of Style: Age Transformation Using a Style-Based Regression Model:" ]

[ "transformers", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:Minbyul/llama2-7b-wo-medication_qa-sft", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-Minbyul/llama2-7b-wo-medication_qa-sft #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "safetensors", "mistral", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #llama-factory #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" ]

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "arxiv:1910.09700", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "tensorboard", "safetensors", "legal", "code", "text-generation-inference", "art", "text-generation", "fr", "en", "dataset:cerebras/SlimPajama-627B", "dataset:uonlp/CulturaX", "dataset:pg19", "dataset:bigcode/starcoderdata", "dataset:croissantllm/croissant_dataset", "arxiv:2402.00786", "license:mit", "region:us" ]

[ "2402.00786" ]

[ "fr", "en" ]

[ "# CroissantLLM - All smaller checkpoints\n\nThese models are part of the CroissantLLM initiative, and correspond to the checkpoints after 100B tokens for smaller model sizes.\nThese are the models used for scaling laws.\n\n\nTo play with the final model, we recommend using the Chat version: URL\n\n\nURL", "## Abstract\nWe introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.\nTo that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.\nTo assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.\nThis work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.\n\nOur work can be cited as:", "## Usage\n\nThis model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies." ]

[ "TAGS\n#tensorboard #safetensors #legal #code #text-generation-inference #art #text-generation #fr #en #dataset-cerebras/SlimPajama-627B #dataset-uonlp/CulturaX #dataset-pg19 #dataset-bigcode/starcoderdata #dataset-croissantllm/croissant_dataset #arxiv-2402.00786 #license-mit #region-us \n", "# CroissantLLM - All smaller checkpoints\n\nThese models are part of the CroissantLLM initiative, and correspond to the checkpoints after 100B tokens for smaller model sizes.\nThese are the models used for scaling laws.\n\n\nTo play with the final model, we recommend using the Chat version: URL\n\n\nURL", "## Abstract\nWe introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.\nTo that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.\nTo assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.\nThis work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.\n\nOur work can be cited as:", "## Usage\n\nThis model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies." ]

[ "transformers", "pytorch", "llama", "text-generation", "art", "philosophy", "romance", "jokes", "advice", "code", "companionship", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #llama #text-generation #art #philosophy #romance #jokes #advice #code #companionship #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "dataset:conll2003", "base_model:mistralai/Mistral-7B-v0.1", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.0.0+cu117\n* Datasets 2.15.0\n* Tokenizers 0.19.1\n\n\nTraining procedure\n------------------", "### Framework versions\n\n\n* PEFT 0.6.2" ]

[ "TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #dataset-conll2003 #base_model-mistralai/Mistral-7B-v0.1 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.0.0+cu117\n* Datasets 2.15.0\n* Tokenizers 0.19.1\n\n\nTraining procedure\n------------------", "### Framework versions\n\n\n* PEFT 0.6.2" ]

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "pytorch", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: shubham11\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #pytorch #mistral #text-generation #text-generation-inference #unsloth #trl #sft #conversational #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: shubham11\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "transformers", "safetensors", "bert", "feature-extraction", "custom_code", "endpoints_compatible", "region:us" ]

[]

[]

[ "# medical-20-0-16-jinaai_jina-embeddings-v2-small-en-1000-gpt-4-turbo-01_9062874564", "## Model Description\n\nmedical-20-0-16-jinaai_jina-embeddings-v2-small-en-1000-gpt-4-turbo-01_9062874564 is a fine-tuned version of jinaai/jina-embeddings-v2-small-en designed for a specific domain.", "## Use Case\nThis model is designed to support various applications in natural language processing and understanding.", "## Associated Dataset\n\nThis the dataset for this model can be found here.", "## How to Use\n\nThis model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:" ]

[ "TAGS\n#transformers #safetensors #bert #feature-extraction #custom_code #endpoints_compatible #region-us \n", "# medical-20-0-16-jinaai_jina-embeddings-v2-small-en-1000-gpt-4-turbo-01_9062874564", "## Model Description\n\nmedical-20-0-16-jinaai_jina-embeddings-v2-small-en-1000-gpt-4-turbo-01_9062874564 is a fine-tuned version of jinaai/jina-embeddings-v2-small-en designed for a specific domain.", "## Use Case\nThis model is designed to support various applications in natural language processing and understanding.", "## Associated Dataset\n\nThis the dataset for this model can be found here.", "## How to Use\n\nThis model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:" ]

[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #llama-factory #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" ]

[ "pyannote", "pytorch", "pyannote.audio", "pyannote-audio-model", "audio", "voice", "speech", "speaker", "speaker-diarization", "speaker-change-detection", "speaker-segmentation", "voice-activity-detection", "overlapped-speech-detection", "resegmentation", "region:us" ]

[]

[]

[]

[ "TAGS\n#pyannote #pytorch #pyannote.audio #pyannote-audio-model #audio #voice #speech #speaker #speaker-diarization #speaker-change-detection #speaker-segmentation #voice-activity-detection #overlapped-speech-detection #resegmentation #region-us \n" ]

[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "conversational", "base_model:shenzhi-wang/Llama3-8B-Chinese-Chat", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo shenzhi-wang/Llama3-8B-Chinese-Chat installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model shenzhi-wang/Llama3-8B-Chinese-Chat before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #pruna-ai #conversational #base_model-shenzhi-wang/Llama3-8B-Chinese-Chat #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo shenzhi-wang/Llama3-8B-Chinese-Chat installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model shenzhi-wang/Llama3-8B-Chinese-Chat before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #bart #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "gguf", "text-generation", "en", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "license:other", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#gguf #text-generation #en #base_model-meta-llama/Meta-Llama-3-70B-Instruct #license-other #region-us \n" ]

[ "music", "region:us" ]

[]

[]

[]

[ "TAGS\n#music #region-us \n" ]

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2212.04089" ]

[]

[ "# tmp\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using ./evol_merge_storage/input_models/Mistral-7B-Instruct-v0.2_674785087 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* ./evol_merge_storage/input_models/OpenMath-Mistral-7B-v0.1-hf_3930120330\n* ./evol_merge_storage/input_models/RakutenAI-7B-chat_2028928689", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #arxiv-2212.04089 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# tmp\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using ./evol_merge_storage/input_models/Mistral-7B-Instruct-v0.2_674785087 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* ./evol_merge_storage/input_models/OpenMath-Mistral-7B-v0.1-hf_3930120330\n* ./evol_merge_storage/input_models/RakutenAI-7B-chat_2028928689", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]

[]

[]

[ "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]

[ "TAGS\n#transformers #tensorboard #safetensors #autotrain #text-generation-inference #text-generation #peft #conversational #license-other #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "axolotl", "text-generation", "en", "arxiv:2309.10400", "base_model:winglian/Llama-3-8b-64k-PoSE", "region:us" ]

[ "2309.10400" ]

[ "en" ]

[ "### Base pretrained models", "### Instruction tuned models", "### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.", "#### Llama 3-Instruct\n\n\nAs outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.\n\n\nSafety\n\n\nFor our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.\n\n\nRefusals\n\n\nIn addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.\n\n\nWe built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.", "#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL", "#### Critical risks\n\n\nCBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)\n\n\nWe have conducted a two fold assessment of the safety of the model in this area:\n\n\n* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.\n* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).", "### Cyber Security\n\n\nWe have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.", "### Child Safety\n\n\nChild Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.", "### Community\n\n\nGenerative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.\n\n\nFinally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL" ]

[ "TAGS\n#gguf #facebook #meta #pytorch #llama #llama-3 #axolotl #text-generation #en #arxiv-2309.10400 #base_model-winglian/Llama-3-8b-64k-PoSE #region-us \n", "### Base pretrained models", "### Instruction tuned models", "### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.", "#### Llama 3-Instruct\n\n\nAs outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.\n\n\nSafety\n\n\nFor our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.\n\n\nRefusals\n\n\nIn addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.\n\n\nWe built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.", "#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL", "#### Critical risks\n\n\nCBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)\n\n\nWe have conducted a two fold assessment of the safety of the model in this area:\n\n\n* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.\n* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).", "### Cyber Security\n\n\nWe have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.", "### Child Safety\n\n\nChild Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.", "### Community\n\n\nGenerative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.\n\n\nFinally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL" ]

[ "license:mit", "region:us" ]

[]

[]

[ "# Table of Contents\n\n* FunSearch\n * FunSearch\n * make\\_request\\_for\\_prompt\n * request\\_samplers\n * get\\_next\\_state\n * set\\_up\\_flow\\_state\n * save\\_message\\_to\\_state\n * rename\\_key\\_message\\_in\\_state\n * message\\_in\\_state\n * get\\_message\\_from\\_state\n * pop\\_message\\_from\\_state\n * merge\\_message\\_request\\_state\n * register\\_data\\_to\\_state\n * call\\_program\\_db\n * call\\_evaluator\n * call\\_sampler\n * generate\\_reply\n * run\n* ProgramDBFlowModule\n* ProgramDBFlowModule.ProgramDBFlow\n * ProgramDBFlow\n * set\\_up\\_flow\\_state\n * get\\_prompt\n * reset\\_islands\n * register\\_program\n * get\\_best\\_programs\n * run\n* SamplerFlowModule\n* SamplerFlowModule.SamplerFlow\n * SamplerFlow\n * run\n* EvaluatorFlowModule\n* EvaluatorFlowModule.EvaluatorFlow\n * EvaluatorFlow\n * load\\_functions\n * run\\_function\\_with\\_timeout\n * evaluate\\_program\n * analyse\n * run\n\n<a id=\"FunSearch\"></a>", "# FunSearch\n\n<a id=\"FunSearch.FunSearch\"></a>", "## FunSearch Objects\n\n\n\nThis class implements FunSearch. This code is an implementation of Funsearch (URL and is heavily inspired by the original code (URL . It's a Flow in charge of starting, stopping and managing (passing around messages) the FunSearch process. It passes messages around to the following subflows:\n\n- ProgramDBFlow: which is in charge of storing and retrieving programs.\n- SamplerFlow: which is in charge of sampling programs.\n- EvaluatorFlow: which is in charge of evaluating programs.\n\n*Configuration Parameters*:\n\n- 'name' (str): The name of the flow. Default: \"FunSearchFlow\".\n- 'description' (str): The description of the flow. Default: \"A flow implementing FunSearch\"\n- 'subflows_config' (Dict[str,Any]): A dictionary of subflows configurations. Default:\n - 'ProgramDBFlow': By default, it uses the 'ProgramDBFlow' class and uses its default parameters.\n - 'SamplerFlow': By default, it uses the 'SamplerFlow' class and uses its default parameters.\n - 'EvaluatorFlow': By default, it uses the 'EvaluatorFlow' class and uses its default parameters.\n\nInput Interface:\n\n- 'from' (str): The flow from which the message is coming from. It can be one of the following: \"FunSearch\", \"SamplerFlow\", \"EvaluatorFlow\", \"ProgramDBFlow\".\n- 'operation' (str): The operation to perform. It can be one of the following: \"start\", \"stop\", \"get_prompt\", \"get_best_programs_per_island\", \"register_program\".\n- 'content' (Dict[str,Any]): The content associated to an operation. Here is the expected content for each operation:\n - \"start\":\n - 'num_samplers' (int): The number of samplers to start up. Note that it's still restricted by the number of workers available. Default: 1.\n - \"stop\":\n - No content. Pass either an empty dictionary or None. Works also with no content.\n - \"get_prompt\":\n - No content. Pass either an empty dictionary or None. Works also with no content.\n - \"get_best_programs_per_island\":\n - No content. Pass either an empty dictionary or None. Works also with no content.\n\nOutput Interface:\n\n- 'retrieved' (Dict[str,Any]): The retrieved data.\n\nCitation:\n\n@Article{FunSearch2023,\n author = {Romera-Paredes, Bernardino and Barekatain, Mohammadamin and Novikov, Alexander and Balog, Matej and Kumar, M. Pawan and Dupont, Emilien and Ruiz, Francisco J. R. and Ellenberg, Jordan and Wang, Pengming and Fawzi, Omar and Kohli, Pushmeet and Fawzi, Alhussein},\n journal = {Nature},\n title = {Mathematical discoveries from program search with large language models},\n year = {2023},\n doi = {10.1038/s41586-023-06924-6}\n}\n\n<a id=\"FunSearch.FunSearch.make_request_for_prompt\"></a>", "#### make\\_request\\_for\\_prompt\n\n\n\nThis method makes a request for a prompt. It sends a message to itself with the operation \"get_prompt\" which will trigger the flow to call the 'ProgramDBFlow' to get a prompt.\n\n<a id=\"FunSearch.FunSearch.request_samplers\"></a>", "#### request\\_samplers\n\n\n\nThis method requests samplers. It sends a message to itself with the operation \"get_prompt\" which will trigger the flow to call the 'ProgramDBFlow' to get a prompt.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message that triggered the request for samplers.\n\n<a id=\"FunSearch.FunSearch.get_next_state\"></a>", "#### get\\_next\\_state\n\n\n\nThis method determines the next state of the flow based on the input message. It will return the next state based on the current state and the message received.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message that triggered the request for the next state.\n\nReturns:\n\n'str': The next state of the flow.\n\n<a id=\"FunSearch.FunSearch.set_up_flow_state\"></a>", "#### set\\_up\\_flow\\_state\n\n\n\nThis method sets up the state of the flow. It's called at the beginning of the flow.\n\n<a id=\"FunSearch.FunSearch.save_message_to_state\"></a>", "#### save\\_message\\_to\\_state\n\n\n\nThis method saves a message to the state of the flow. It's used to keep track of state on a per message basis (i.e., state of the flow depending on the message received and id).\n\nArguments:\n\n- 'msg_id' ('str'): The id of the message to save.\n- 'message' ('FlowMessage'): The message to save.\n\n<a id=\"FunSearch.FunSearch.rename_key_message_in_state\"></a>", "#### rename\\_key\\_message\\_in\\_state\n\n\n\nThis method renames a key in the state of the flow in the \"msg_requests\" dictonary. It's used to rename a key in the state of the flow (i.e., rename a message id).\n\nArguments:\n\n- 'old_key' ('str'): The old key to rename.\n- 'new_key' ('str'): The new key to rename to.\n\n<a id=\"FunSearch.FunSearch.message_in_state\"></a>", "#### message\\_in\\_state\n\n\n\nThis method checks if a message is in the state of the flow (in \"msg_requests\" dictionary). It returns True if the message is in the state, otherwise it returns False.\n\nArguments:\n\n- 'msg_id' ('str'): The id of the message to check if it's in the state.\n\nReturns:\n\n'bool': True if the message is in the state, otherwise False.\n\n<a id=\"FunSearch.FunSearch.get_message_from_state\"></a>", "#### get\\_message\\_from\\_state\n\n\n\nThis method returns the state associated with a message id in the state of the flow (in \"msg_requests\" dictionary).\n\nArguments:\n\n- 'msg_id' ('str'): The id of the message to get the state from.\n\nReturns:\n\n'Dict[str,Any]': The state associated with the message id.\n\n<a id=\"FunSearch.FunSearch.pop_message_from_state\"></a>", "#### pop\\_message\\_from\\_state\n\n\n\nThis method pops a message from the state of the flow (in \"msg_requests\" dictionary). It the state associate to a message and removes it from the state.\n\nArguments:\n\n- 'msg_id' ('str'): The id of the message to pop from the state.\n\nReturns:\n\n'Dict[str,Any]': The state associated with the message id.\n\n<a id=\"FunSearch.FunSearch.merge_message_request_state\"></a>", "#### merge\\_message\\_request\\_state\n\n\n\nThis method merges new states to a message in the state of the flow (in \"msg_requests\" dictionary). It merges new states to a message in the state.\n\nArguments:\n\n- 'id' ('str'): The id of the message to merge new states to.\n- 'new_states' ('Dict[str,Any]'): The new states to merge to the message.\n\n<a id=\"FunSearch.FunSearch.register_data_to_state\"></a>", "#### register\\_data\\_to\\_state\n\n\n\nThis method registers the input message data to the flow state. It's called everytime a new input message is received.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message\n\n<a id=\"FunSearch.FunSearch.call_program_db\"></a>", "#### call\\_program\\_db\n\n\n\nThis method calls the ProgramDBFlow. It sends a message to the ProgramDBFlow with the data of the input message.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message to send to the ProgramDBFlow.\n\n<a id=\"FunSearch.FunSearch.call_evaluator\"></a>", "#### call\\_evaluator\n\n\n\nThis method calls the EvaluatorFlow. It sends a message to the EvaluatorFlow with the data of the input message.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message to send to the EvaluatorFlow.\n\n<a id=\"FunSearch.FunSearch.call_sampler\"></a>", "#### call\\_sampler\n\n\n\nThis method calls the SamplerFlow. It sends a message to the SamplerFlow with the data of the input message.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message to send to the SamplerFlow.\n\n<a id=\"FunSearch.FunSearch.generate_reply\"></a>", "#### generate\\_reply\n\n\n\nThis method generates a reply to a message sent to user. It packages the output message and sends it.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message to generate a reply to.\n\n<a id=\"URL\"></a>", "#### run\n\n\n\nThis method runs the flow. It's the main method of the flow. It's called when the flow is executed.\n\n<a id=\"ProgramDBFlowModule\"></a>", "# ProgramDBFlowModule\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow\"></a>", "## ProgramDBFlow Objects\n\n\n\nThis class implements a ProgramDBFlow. It's a flow that stores programs and their scores in a database. It can also query the database for the best programs or generate a prompt containing stored programs in order to evolve them with a SamplerFlow. This code is an implementation of Funsearch (URL and is heavily inspired by the original code (URL\n\nConfiguration Parameters:\n\n- 'name' (str): The name of the flow. Default: \"ProgramDBFlow\"\n- 'description' (str): A description of the flow. This description is used to generate the help message of the flow. Default: \" A flow that saves programs in a database of islands\"\n- 'artifact_to_evolve_name' (str): The name of the artifact/program to evolve. Default: \"solve_function\"\n- 'evaluate_function' (str): The function used to evaluate the program. No Default value. This MUST be passed as a parameter.\n- 'evaluate_file_full_content' (str): The full content of the file containing the evaluation function. No Default value. This MUST be passed as a parameter.\n- 'num_islands': The number of islands to use. Default: 3\n- 'reset_period': The period in seconds to reset the islands. Default: 3600\n- 'artifacts_per_prompt': The number of previous artifacts/programs to include in a prompt. Default: 2\n- 'temperature': The temperature of the island. Default: 0.1\n- 'temperature_period': The period in seconds to change the temperature. Default: 30000\n- 'sample_with_replacement': Whether to sample with replacement. Default: False\n- 'portion_of_islands_to_reset': The portion of islands to reset. Default: 0.5\n- 'template' (dict): The template to use for a program. Default: {\"preface\": \"\"}\n\nInput Interface:\n\n- 'operation' (str): The operation to perform. It can be one of the following: [\"register_program\",\"get_prompt\",\"get_best_programs_per_island\"]\n\nOutput Interface:\n\n- 'retrieved' (Any): The retrieved data. It can be one of the following:\n - If the operation is \"get_prompt\", it can be a dictionary with the following keys\n - 'code' (str): The code of the prompt\n - 'version_generated' (int): The version of the prompt generated\n - 'island_id' (int): The id of the island that generated the prompt\n - 'header' (str): The header of the prompt\n - If the operation is \"register_program\", it can be a string with the message \"Program registered\" or \"Program failed to register\"\n - If the operation is \"get_best_programs_per_island\", it can be a dictionary with the following keys:\n - 'best_island_programs' (List[Dict[str,Any]]): A list of dictionaries with the following keys:\n - 'rank' (int): The rank of the program (1 is the best)\n - 'score' (float): The score of the program\n - 'program' (str): The program\n - 'island_id' (int): The id of the island that generated the program\n\nCitation:\n\n@Article{FunSearch2023,\n author = {Romera-Paredes, Bernardino and Barekatain, Mohammadamin and Novikov, Alexander and Balog, Matej and Kumar, M. Pawan and Dupont, Emilien and Ruiz, Francisco J. R. and Ellenberg, Jordan and Wang, Pengming and Fawzi, Omar and Kohli, Pushmeet and Fawzi, Alhussein},\n journal = {Nature},\n title = {Mathematical discoveries from program search with large language models},\n year = {2023},\n doi = {10.1038/s41586-023-06924-6}\n}\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.set_up_flow_state\"></a>", "#### set\\_up\\_flow\\_state\n\n\n\nThis method sets up the state of the flow and clears the previous messages.\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.get_prompt\"></a>", "#### get\\_prompt\n\n\n\nThis method gets a prompt from an island. It returns the code, the version generated and the island id.\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.reset_islands\"></a>", "#### reset\\_islands\n\n\n\nThis method resets the islands. It resets the worst islands and copies the best programs to the worst islands.\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.register_program\"></a>", "#### register\\_program\n\n\n\nThis method registers a program in an island. It also updates the best program if needed.\n\nArguments:\n\n- 'program' ('AbstractArtifact'): The program to register\n- 'island_id' ('int'): The id of the island to register the program\n- 'scores_per_test' ('ScoresPerTest'): The scores per test of the program\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.get_best_programs\"></a>", "#### get\\_best\\_programs\n\n\n\nThis method returns the best programs per island.\n\n<a id=\"URL\"></a>", "#### run\n\n\n\nThis method runs the flow. It performs the operation requested in the input message.\n\n<a id=\"SamplerFlowModule\"></a>", "# SamplerFlowModule\n\n<a id=\"SamplerFlowModule.SamplerFlow\"></a>", "# SamplerFlowModule.SamplerFlow\n\n<a id=\"SamplerFlowModule.SamplerFlow.SamplerFlow\"></a>", "## SamplerFlow Objects\n\n\n\nThis class implements a SamplerFlow. It is a flow that queries a LLM to generate a response to a given input. This class is a child of ChatAtomicFlow.\nand expects the same parameters as ChatAtomicFlow (see URL\n\nConfiguration Parameters:\n- 'name' (str): The name of the flow. Default: \"SamplerFlowModule\"\n- 'description' (str): A description of the flow. Default: \"A flow that queries an LLM model to generate prompts for the Sampler flow\"\n- 'backend' Dict[str,Any]: The backend of the flow. Used to call models via an API.\nSee litellm's supported models and APIs here: URL\nThe default parameters of the backend are all defined at aiflows.backends.llm_lite.LiteLLMBackend\n(also see the defaul parameters of litellm's completion parameters: URL\nExcept for the following parameters who are overwritten by the ChatAtomicFlow in URL:\n - 'model_name' (Union[Dict[str,str],str]): The name of the model to use. Default: \"gpt-4\"\n When using multiple API providers, the model_name can be a dictionary of the form \n {\"provider_name\": \"model_name\"}. E.g. {\"openai\": \"gpt-3.5-turbo\", \"azure\": \"azure/gpt-3.5-turbo\"}\n Default: \"gpt-3.5-turbo\" (the name needs to follow the name of the model in litellm URL\n - 'n' (int) : The number of answers to generate. Default: 1\n - 'max_tokens' (int): The maximum number of tokens to generate. Default: 2000\n - 'temperature' float: The temperature of the generation. Default: 1.0\n - 'top_p' float: An alternative to sampling with temperature. It instructs the model to consider the results of\n the tokens with top_p probability. Default: 0.4\n - 'frequency_penalty' (number): It is used to penalize new tokens based on their frequency in the text so far. Default: 0.0\n - 'presence_penalty' (number): It is used to penalize new tokens based on their existence in the text so far. Default: 0.0\n - 'stream' (bool): Whether to stream the response or not. Default: false\n- 'system_message_prompt_template' (Dict[str,Any]): The template of the system message. It is used to generate the system message. Default: See URL for default.\n- 'init_human_message_prompt_template' (Dict[str,Any]): The prompt template of the human/user message used to initialize the conversation\n(first time in). It is used to generate the human message. It's passed as the user message to the LLM. Default: See URL for default.\n- 'human_message_prompt_template' (Dict[str,Any]): The prompt template of the human/user message (message used everytime the except the first time in).\nIt's passed as the user message to the LLM. Default: See URL for default.\n- 'previous_messages' (Dict[str,Any]): Defines which previous messages to include in the input of the LLM. Note that if 'first_k'and 'last_k' are both none,\nall the messages of the flows's history are added to the input of the LLM. Default:\n - 'first_k' (int): If defined, adds the first_k earliest messages of the flow's chat history to the input of the LLM. Default: 1\n - 'last_k' (int): If defined, adds the last_k latest messages of the flow's chat history to the input of the LLM. Default: 1\n\n*Input Interface Initialized (Expected input the first time in flow)*:\n\n- 'header' (str): A header message to include in prompt\n- 'code' (str): The \"example\" samples to generate our new sample from.\n\n*Input Interface (Expected input the after the first time in flow)*:\n\n- 'header' (str): A header message to include in prompt\n- 'code' (str): The \"example\" samples to generate our new sample from.\n\n*Output Interface*:\n\n- 'api_output' (str): The output of the API call. It is the response of the LLM to the input.\n- 'from' (str): The name of the flow that generated the output. It's always \"SamplerFlow\"\n\n\nCitation:\n\n@Article{FunSearch2023,\n author = {Romera-Paredes, Bernardino and Barekatain, Mohammadamin and Novikov, Alexander and Balog, Matej and Kumar, M. Pawan and Dupont, Emilien and Ruiz, Francisco J. R. and Ellenberg, Jordan and Wang, Pengming and Fawzi, Omar and Kohli, Pushmeet and Fawzi, Alhussein},\n journal = {Nature},\n title = {Mathematical discoveries from program search with large language models},\n year = {2023},\n doi = {10.1038/s41586-023-06924-6}\n}\n\n<a id=\"URL\"></a>", "#### run\n\n\n\nThis method calls the backend of the flow (so queries the LLM). It calls the backend with the previous messages of the flow.\n\nReturns:\n\n'Any': The output of the backend.\n\n<a id=\"EvaluatorFlowModule\"></a>", "# EvaluatorFlowModule\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow\"></a>", "# EvaluatorFlowModule.EvaluatorFlow\n\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow\"></a>", "## EvaluatorFlow Objects\n\n\n\nThis class implements an EvaluatorFlow. It is a flow that evaluates a program (python code) using a given evaluator function. This code is an implementation of Funsearch (URL and is heavily inspired by the original code (URL\n\nConfiguration Parameters:\n\n- 'name' (str): The name of the flow. Default: \"EvaluatorFlow\"\n- 'description' (str): A description of the flow. This description is used to generate the help message of the flow. Default: \"A flow that evaluates code on tests\"\n- 'py_file' (str): The python code containing the evaluation function. No default value. This MUST be passed as a parameter.\n- 'function_to_run_name' (str): The name of the function to run (the evaluation function) in the evaluator file. No default value. This MUST be passed as a parameter.\n- 'test_inputs' (Dict[str,Any]): A dictionary of test inputs to evaluate the program. Default: {\"test1\": None, \"test2\": None}\n- 'timeout_seconds' (int): The maximum number of seconds to run the evaluation function before returning an error. Default: 10\n- 'run_error_score' (int): The score to return if the evaluation function fails to run. Default: -100\n- 'use_test_input_as_key' (bool): Whether to use the test input parameters as the key in the output dictionary. Default: False\n\nInput Interface:\n\n- 'artifact' (str): The program/artifact to evaluate.\n\nOutput Interface:\n\n- 'scores_per_test' (Dict[str, Dict[str, Any]]): A dictionary of scores per test input.\n\nCitation:\n\n@Article{FunSearch2023,\n author = {Romera-Paredes, Bernardino and Barekatain, Mohammadamin and Novikov, Alexander and Balog, Matej and Kumar, M. Pawan and Dupont, Emilien and Ruiz, Francisco J. R. and Ellenberg, Jordan and Wang, Pengming and Fawzi, Omar and Kohli, Pushmeet and Fawzi, Alhussein},\n journal = {Nature},\n title = {Mathematical discoveries from program search with large language models},\n year = {2023},\n doi = {10.1038/s41586-023-06924-6}\n}\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow.load_functions\"></a>", "#### load\\_functions\n\n\n\nLoad the functions from the evaluator py file with ast parsing\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow.run_function_with_timeout\"></a>", "#### run\\_function\\_with\\_timeout\n\n\n\nRun the evaluation function with a timeout\n\nArguments:\n\n- 'program' ('str'): The program to evaluate\n- 'kwargs' ('Dict[str, Any]'): The keyword arguments to pass to the evaluation function\n\nReturns:\n\n'Tuple[bool, Any]': A tuple (bool, result) where bool is True if the function ran successfully and result is the output of the function\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow.evaluate_program\"></a>", "#### evaluate\\_program\n\n\n\nEvaluate the program using the evaluation function\n\nArguments:\n\n- 'program' ('str'): The program to evaluate\n- 'kwargs' ('Dict[str, Any]'): The keyword arguments to pass to the evaluation function\n\nReturns:\n\n'Tuple[bool, Any]': A tuple (bool, result) where bool is True if the function ran successfully and result is the output of the function\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow.analyse\"></a>", "#### analyse\n\n\n\nAnalyse the program on the test inputs\n\nArguments:\n\n- 'program' ('str'): The program to evaluate\n\nReturns:\n\n'Dict[str, Dict[str, Any]]': A dictionary of scores per test input\n\n<a id=\"URL\"></a>", "#### run\n\n\n\nThis method runs the flow. It's the main method of the flow.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message" ]

[ "TAGS\n#license-mit #region-us \n", "# Table of Contents\n\n* FunSearch\n * FunSearch\n * make\\_request\\_for\\_prompt\n * request\\_samplers\n * get\\_next\\_state\n * set\\_up\\_flow\\_state\n * save\\_message\\_to\\_state\n * rename\\_key\\_message\\_in\\_state\n * message\\_in\\_state\n * get\\_message\\_from\\_state\n * pop\\_message\\_from\\_state\n * merge\\_message\\_request\\_state\n * register\\_data\\_to\\_state\n * call\\_program\\_db\n * call\\_evaluator\n * call\\_sampler\n * generate\\_reply\n * run\n* ProgramDBFlowModule\n* ProgramDBFlowModule.ProgramDBFlow\n * ProgramDBFlow\n * set\\_up\\_flow\\_state\n * get\\_prompt\n * reset\\_islands\n * register\\_program\n * get\\_best\\_programs\n * run\n* SamplerFlowModule\n* SamplerFlowModule.SamplerFlow\n * SamplerFlow\n * run\n* EvaluatorFlowModule\n* EvaluatorFlowModule.EvaluatorFlow\n * EvaluatorFlow\n * load\\_functions\n * run\\_function\\_with\\_timeout\n * evaluate\\_program\n * analyse\n * run\n\n<a id=\"FunSearch\"></a>", "# FunSearch\n\n<a id=\"FunSearch.FunSearch\"></a>", "## FunSearch Objects\n\n\n\nThis class implements FunSearch. This code is an implementation of Funsearch (URL and is heavily inspired by the original code (URL . It's a Flow in charge of starting, stopping and managing (passing around messages) the FunSearch process. It passes messages around to the following subflows:\n\n- ProgramDBFlow: which is in charge of storing and retrieving programs.\n- SamplerFlow: which is in charge of sampling programs.\n- EvaluatorFlow: which is in charge of evaluating programs.\n\n*Configuration Parameters*:\n\n- 'name' (str): The name of the flow. Default: \"FunSearchFlow\".\n- 'description' (str): The description of the flow. Default: \"A flow implementing FunSearch\"\n- 'subflows_config' (Dict[str,Any]): A dictionary of subflows configurations. Default:\n - 'ProgramDBFlow': By default, it uses the 'ProgramDBFlow' class and uses its default parameters.\n - 'SamplerFlow': By default, it uses the 'SamplerFlow' class and uses its default parameters.\n - 'EvaluatorFlow': By default, it uses the 'EvaluatorFlow' class and uses its default parameters.\n\nInput Interface:\n\n- 'from' (str): The flow from which the message is coming from. It can be one of the following: \"FunSearch\", \"SamplerFlow\", \"EvaluatorFlow\", \"ProgramDBFlow\".\n- 'operation' (str): The operation to perform. It can be one of the following: \"start\", \"stop\", \"get_prompt\", \"get_best_programs_per_island\", \"register_program\".\n- 'content' (Dict[str,Any]): The content associated to an operation. Here is the expected content for each operation:\n - \"start\":\n - 'num_samplers' (int): The number of samplers to start up. Note that it's still restricted by the number of workers available. Default: 1.\n - \"stop\":\n - No content. Pass either an empty dictionary or None. Works also with no content.\n - \"get_prompt\":\n - No content. Pass either an empty dictionary or None. Works also with no content.\n - \"get_best_programs_per_island\":\n - No content. Pass either an empty dictionary or None. Works also with no content.\n\nOutput Interface:\n\n- 'retrieved' (Dict[str,Any]): The retrieved data.\n\nCitation:\n\n@Article{FunSearch2023,\n author = {Romera-Paredes, Bernardino and Barekatain, Mohammadamin and Novikov, Alexander and Balog, Matej and Kumar, M. Pawan and Dupont, Emilien and Ruiz, Francisco J. R. and Ellenberg, Jordan and Wang, Pengming and Fawzi, Omar and Kohli, Pushmeet and Fawzi, Alhussein},\n journal = {Nature},\n title = {Mathematical discoveries from program search with large language models},\n year = {2023},\n doi = {10.1038/s41586-023-06924-6}\n}\n\n<a id=\"FunSearch.FunSearch.make_request_for_prompt\"></a>", "#### make\\_request\\_for\\_prompt\n\n\n\nThis method makes a request for a prompt. It sends a message to itself with the operation \"get_prompt\" which will trigger the flow to call the 'ProgramDBFlow' to get a prompt.\n\n<a id=\"FunSearch.FunSearch.request_samplers\"></a>", "#### request\\_samplers\n\n\n\nThis method requests samplers. It sends a message to itself with the operation \"get_prompt\" which will trigger the flow to call the 'ProgramDBFlow' to get a prompt.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message that triggered the request for samplers.\n\n<a id=\"FunSearch.FunSearch.get_next_state\"></a>", "#### get\\_next\\_state\n\n\n\nThis method determines the next state of the flow based on the input message. It will return the next state based on the current state and the message received.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message that triggered the request for the next state.\n\nReturns:\n\n'str': The next state of the flow.\n\n<a id=\"FunSearch.FunSearch.set_up_flow_state\"></a>", "#### set\\_up\\_flow\\_state\n\n\n\nThis method sets up the state of the flow. It's called at the beginning of the flow.\n\n<a id=\"FunSearch.FunSearch.save_message_to_state\"></a>", "#### save\\_message\\_to\\_state\n\n\n\nThis method saves a message to the state of the flow. It's used to keep track of state on a per message basis (i.e., state of the flow depending on the message received and id).\n\nArguments:\n\n- 'msg_id' ('str'): The id of the message to save.\n- 'message' ('FlowMessage'): The message to save.\n\n<a id=\"FunSearch.FunSearch.rename_key_message_in_state\"></a>", "#### rename\\_key\\_message\\_in\\_state\n\n\n\nThis method renames a key in the state of the flow in the \"msg_requests\" dictonary. It's used to rename a key in the state of the flow (i.e., rename a message id).\n\nArguments:\n\n- 'old_key' ('str'): The old key to rename.\n- 'new_key' ('str'): The new key to rename to.\n\n<a id=\"FunSearch.FunSearch.message_in_state\"></a>", "#### message\\_in\\_state\n\n\n\nThis method checks if a message is in the state of the flow (in \"msg_requests\" dictionary). It returns True if the message is in the state, otherwise it returns False.\n\nArguments:\n\n- 'msg_id' ('str'): The id of the message to check if it's in the state.\n\nReturns:\n\n'bool': True if the message is in the state, otherwise False.\n\n<a id=\"FunSearch.FunSearch.get_message_from_state\"></a>", "#### get\\_message\\_from\\_state\n\n\n\nThis method returns the state associated with a message id in the state of the flow (in \"msg_requests\" dictionary).\n\nArguments:\n\n- 'msg_id' ('str'): The id of the message to get the state from.\n\nReturns:\n\n'Dict[str,Any]': The state associated with the message id.\n\n<a id=\"FunSearch.FunSearch.pop_message_from_state\"></a>", "#### pop\\_message\\_from\\_state\n\n\n\nThis method pops a message from the state of the flow (in \"msg_requests\" dictionary). It the state associate to a message and removes it from the state.\n\nArguments:\n\n- 'msg_id' ('str'): The id of the message to pop from the state.\n\nReturns:\n\n'Dict[str,Any]': The state associated with the message id.\n\n<a id=\"FunSearch.FunSearch.merge_message_request_state\"></a>", "#### merge\\_message\\_request\\_state\n\n\n\nThis method merges new states to a message in the state of the flow (in \"msg_requests\" dictionary). It merges new states to a message in the state.\n\nArguments:\n\n- 'id' ('str'): The id of the message to merge new states to.\n- 'new_states' ('Dict[str,Any]'): The new states to merge to the message.\n\n<a id=\"FunSearch.FunSearch.register_data_to_state\"></a>", "#### register\\_data\\_to\\_state\n\n\n\nThis method registers the input message data to the flow state. It's called everytime a new input message is received.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message\n\n<a id=\"FunSearch.FunSearch.call_program_db\"></a>", "#### call\\_program\\_db\n\n\n\nThis method calls the ProgramDBFlow. It sends a message to the ProgramDBFlow with the data of the input message.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message to send to the ProgramDBFlow.\n\n<a id=\"FunSearch.FunSearch.call_evaluator\"></a>", "#### call\\_evaluator\n\n\n\nThis method calls the EvaluatorFlow. It sends a message to the EvaluatorFlow with the data of the input message.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message to send to the EvaluatorFlow.\n\n<a id=\"FunSearch.FunSearch.call_sampler\"></a>", "#### call\\_sampler\n\n\n\nThis method calls the SamplerFlow. It sends a message to the SamplerFlow with the data of the input message.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message to send to the SamplerFlow.\n\n<a id=\"FunSearch.FunSearch.generate_reply\"></a>", "#### generate\\_reply\n\n\n\nThis method generates a reply to a message sent to user. It packages the output message and sends it.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message to generate a reply to.\n\n<a id=\"URL\"></a>", "#### run\n\n\n\nThis method runs the flow. It's the main method of the flow. It's called when the flow is executed.\n\n<a id=\"ProgramDBFlowModule\"></a>", "# ProgramDBFlowModule\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow\"></a>", "## ProgramDBFlow Objects\n\n\n\nThis class implements a ProgramDBFlow. It's a flow that stores programs and their scores in a database. It can also query the database for the best programs or generate a prompt containing stored programs in order to evolve them with a SamplerFlow. This code is an implementation of Funsearch (URL and is heavily inspired by the original code (URL\n\nConfiguration Parameters:\n\n- 'name' (str): The name of the flow. Default: \"ProgramDBFlow\"\n- 'description' (str): A description of the flow. This description is used to generate the help message of the flow. Default: \" A flow that saves programs in a database of islands\"\n- 'artifact_to_evolve_name' (str): The name of the artifact/program to evolve. Default: \"solve_function\"\n- 'evaluate_function' (str): The function used to evaluate the program. No Default value. This MUST be passed as a parameter.\n- 'evaluate_file_full_content' (str): The full content of the file containing the evaluation function. No Default value. This MUST be passed as a parameter.\n- 'num_islands': The number of islands to use. Default: 3\n- 'reset_period': The period in seconds to reset the islands. Default: 3600\n- 'artifacts_per_prompt': The number of previous artifacts/programs to include in a prompt. Default: 2\n- 'temperature': The temperature of the island. Default: 0.1\n- 'temperature_period': The period in seconds to change the temperature. Default: 30000\n- 'sample_with_replacement': Whether to sample with replacement. Default: False\n- 'portion_of_islands_to_reset': The portion of islands to reset. Default: 0.5\n- 'template' (dict): The template to use for a program. Default: {\"preface\": \"\"}\n\nInput Interface:\n\n- 'operation' (str): The operation to perform. It can be one of the following: [\"register_program\",\"get_prompt\",\"get_best_programs_per_island\"]\n\nOutput Interface:\n\n- 'retrieved' (Any): The retrieved data. It can be one of the following:\n - If the operation is \"get_prompt\", it can be a dictionary with the following keys\n - 'code' (str): The code of the prompt\n - 'version_generated' (int): The version of the prompt generated\n - 'island_id' (int): The id of the island that generated the prompt\n - 'header' (str): The header of the prompt\n - If the operation is \"register_program\", it can be a string with the message \"Program registered\" or \"Program failed to register\"\n - If the operation is \"get_best_programs_per_island\", it can be a dictionary with the following keys:\n - 'best_island_programs' (List[Dict[str,Any]]): A list of dictionaries with the following keys:\n - 'rank' (int): The rank of the program (1 is the best)\n - 'score' (float): The score of the program\n - 'program' (str): The program\n - 'island_id' (int): The id of the island that generated the program\n\nCitation:\n\n@Article{FunSearch2023,\n author = {Romera-Paredes, Bernardino and Barekatain, Mohammadamin and Novikov, Alexander and Balog, Matej and Kumar, M. Pawan and Dupont, Emilien and Ruiz, Francisco J. R. and Ellenberg, Jordan and Wang, Pengming and Fawzi, Omar and Kohli, Pushmeet and Fawzi, Alhussein},\n journal = {Nature},\n title = {Mathematical discoveries from program search with large language models},\n year = {2023},\n doi = {10.1038/s41586-023-06924-6}\n}\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.set_up_flow_state\"></a>", "#### set\\_up\\_flow\\_state\n\n\n\nThis method sets up the state of the flow and clears the previous messages.\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.get_prompt\"></a>", "#### get\\_prompt\n\n\n\nThis method gets a prompt from an island. It returns the code, the version generated and the island id.\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.reset_islands\"></a>", "#### reset\\_islands\n\n\n\nThis method resets the islands. It resets the worst islands and copies the best programs to the worst islands.\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.register_program\"></a>", "#### register\\_program\n\n\n\nThis method registers a program in an island. It also updates the best program if needed.\n\nArguments:\n\n- 'program' ('AbstractArtifact'): The program to register\n- 'island_id' ('int'): The id of the island to register the program\n- 'scores_per_test' ('ScoresPerTest'): The scores per test of the program\n\n<a id=\"ProgramDBFlowModule.ProgramDBFlow.ProgramDBFlow.get_best_programs\"></a>", "#### get\\_best\\_programs\n\n\n\nThis method returns the best programs per island.\n\n<a id=\"URL\"></a>", "#### run\n\n\n\nThis method runs the flow. It performs the operation requested in the input message.\n\n<a id=\"SamplerFlowModule\"></a>", "# SamplerFlowModule\n\n<a id=\"SamplerFlowModule.SamplerFlow\"></a>", "# SamplerFlowModule.SamplerFlow\n\n<a id=\"SamplerFlowModule.SamplerFlow.SamplerFlow\"></a>", "## SamplerFlow Objects\n\n\n\nThis class implements a SamplerFlow. It is a flow that queries a LLM to generate a response to a given input. This class is a child of ChatAtomicFlow.\nand expects the same parameters as ChatAtomicFlow (see URL\n\nConfiguration Parameters:\n- 'name' (str): The name of the flow. Default: \"SamplerFlowModule\"\n- 'description' (str): A description of the flow. Default: \"A flow that queries an LLM model to generate prompts for the Sampler flow\"\n- 'backend' Dict[str,Any]: The backend of the flow. Used to call models via an API.\nSee litellm's supported models and APIs here: URL\nThe default parameters of the backend are all defined at aiflows.backends.llm_lite.LiteLLMBackend\n(also see the defaul parameters of litellm's completion parameters: URL\nExcept for the following parameters who are overwritten by the ChatAtomicFlow in URL:\n - 'model_name' (Union[Dict[str,str],str]): The name of the model to use. Default: \"gpt-4\"\n When using multiple API providers, the model_name can be a dictionary of the form \n {\"provider_name\": \"model_name\"}. E.g. {\"openai\": \"gpt-3.5-turbo\", \"azure\": \"azure/gpt-3.5-turbo\"}\n Default: \"gpt-3.5-turbo\" (the name needs to follow the name of the model in litellm URL\n - 'n' (int) : The number of answers to generate. Default: 1\n - 'max_tokens' (int): The maximum number of tokens to generate. Default: 2000\n - 'temperature' float: The temperature of the generation. Default: 1.0\n - 'top_p' float: An alternative to sampling with temperature. It instructs the model to consider the results of\n the tokens with top_p probability. Default: 0.4\n - 'frequency_penalty' (number): It is used to penalize new tokens based on their frequency in the text so far. Default: 0.0\n - 'presence_penalty' (number): It is used to penalize new tokens based on their existence in the text so far. Default: 0.0\n - 'stream' (bool): Whether to stream the response or not. Default: false\n- 'system_message_prompt_template' (Dict[str,Any]): The template of the system message. It is used to generate the system message. Default: See URL for default.\n- 'init_human_message_prompt_template' (Dict[str,Any]): The prompt template of the human/user message used to initialize the conversation\n(first time in). It is used to generate the human message. It's passed as the user message to the LLM. Default: See URL for default.\n- 'human_message_prompt_template' (Dict[str,Any]): The prompt template of the human/user message (message used everytime the except the first time in).\nIt's passed as the user message to the LLM. Default: See URL for default.\n- 'previous_messages' (Dict[str,Any]): Defines which previous messages to include in the input of the LLM. Note that if 'first_k'and 'last_k' are both none,\nall the messages of the flows's history are added to the input of the LLM. Default:\n - 'first_k' (int): If defined, adds the first_k earliest messages of the flow's chat history to the input of the LLM. Default: 1\n - 'last_k' (int): If defined, adds the last_k latest messages of the flow's chat history to the input of the LLM. Default: 1\n\n*Input Interface Initialized (Expected input the first time in flow)*:\n\n- 'header' (str): A header message to include in prompt\n- 'code' (str): The \"example\" samples to generate our new sample from.\n\n*Input Interface (Expected input the after the first time in flow)*:\n\n- 'header' (str): A header message to include in prompt\n- 'code' (str): The \"example\" samples to generate our new sample from.\n\n*Output Interface*:\n\n- 'api_output' (str): The output of the API call. It is the response of the LLM to the input.\n- 'from' (str): The name of the flow that generated the output. It's always \"SamplerFlow\"\n\n\nCitation:\n\n@Article{FunSearch2023,\n author = {Romera-Paredes, Bernardino and Barekatain, Mohammadamin and Novikov, Alexander and Balog, Matej and Kumar, M. Pawan and Dupont, Emilien and Ruiz, Francisco J. R. and Ellenberg, Jordan and Wang, Pengming and Fawzi, Omar and Kohli, Pushmeet and Fawzi, Alhussein},\n journal = {Nature},\n title = {Mathematical discoveries from program search with large language models},\n year = {2023},\n doi = {10.1038/s41586-023-06924-6}\n}\n\n<a id=\"URL\"></a>", "#### run\n\n\n\nThis method calls the backend of the flow (so queries the LLM). It calls the backend with the previous messages of the flow.\n\nReturns:\n\n'Any': The output of the backend.\n\n<a id=\"EvaluatorFlowModule\"></a>", "# EvaluatorFlowModule\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow\"></a>", "# EvaluatorFlowModule.EvaluatorFlow\n\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow\"></a>", "## EvaluatorFlow Objects\n\n\n\nThis class implements an EvaluatorFlow. It is a flow that evaluates a program (python code) using a given evaluator function. This code is an implementation of Funsearch (URL and is heavily inspired by the original code (URL\n\nConfiguration Parameters:\n\n- 'name' (str): The name of the flow. Default: \"EvaluatorFlow\"\n- 'description' (str): A description of the flow. This description is used to generate the help message of the flow. Default: \"A flow that evaluates code on tests\"\n- 'py_file' (str): The python code containing the evaluation function. No default value. This MUST be passed as a parameter.\n- 'function_to_run_name' (str): The name of the function to run (the evaluation function) in the evaluator file. No default value. This MUST be passed as a parameter.\n- 'test_inputs' (Dict[str,Any]): A dictionary of test inputs to evaluate the program. Default: {\"test1\": None, \"test2\": None}\n- 'timeout_seconds' (int): The maximum number of seconds to run the evaluation function before returning an error. Default: 10\n- 'run_error_score' (int): The score to return if the evaluation function fails to run. Default: -100\n- 'use_test_input_as_key' (bool): Whether to use the test input parameters as the key in the output dictionary. Default: False\n\nInput Interface:\n\n- 'artifact' (str): The program/artifact to evaluate.\n\nOutput Interface:\n\n- 'scores_per_test' (Dict[str, Dict[str, Any]]): A dictionary of scores per test input.\n\nCitation:\n\n@Article{FunSearch2023,\n author = {Romera-Paredes, Bernardino and Barekatain, Mohammadamin and Novikov, Alexander and Balog, Matej and Kumar, M. Pawan and Dupont, Emilien and Ruiz, Francisco J. R. and Ellenberg, Jordan and Wang, Pengming and Fawzi, Omar and Kohli, Pushmeet and Fawzi, Alhussein},\n journal = {Nature},\n title = {Mathematical discoveries from program search with large language models},\n year = {2023},\n doi = {10.1038/s41586-023-06924-6}\n}\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow.load_functions\"></a>", "#### load\\_functions\n\n\n\nLoad the functions from the evaluator py file with ast parsing\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow.run_function_with_timeout\"></a>", "#### run\\_function\\_with\\_timeout\n\n\n\nRun the evaluation function with a timeout\n\nArguments:\n\n- 'program' ('str'): The program to evaluate\n- 'kwargs' ('Dict[str, Any]'): The keyword arguments to pass to the evaluation function\n\nReturns:\n\n'Tuple[bool, Any]': A tuple (bool, result) where bool is True if the function ran successfully and result is the output of the function\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow.evaluate_program\"></a>", "#### evaluate\\_program\n\n\n\nEvaluate the program using the evaluation function\n\nArguments:\n\n- 'program' ('str'): The program to evaluate\n- 'kwargs' ('Dict[str, Any]'): The keyword arguments to pass to the evaluation function\n\nReturns:\n\n'Tuple[bool, Any]': A tuple (bool, result) where bool is True if the function ran successfully and result is the output of the function\n\n<a id=\"EvaluatorFlowModule.EvaluatorFlow.EvaluatorFlow.analyse\"></a>", "#### analyse\n\n\n\nAnalyse the program on the test inputs\n\nArguments:\n\n- 'program' ('str'): The program to evaluate\n\nReturns:\n\n'Dict[str, Dict[str, Any]]': A dictionary of scores per test input\n\n<a id=\"URL\"></a>", "#### run\n\n\n\nThis method runs the flow. It's the main method of the flow.\n\nArguments:\n\n- 'input_message' ('FlowMessage'): The input message" ]

[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "base_model:NousResearch/Meta-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #pruna-ai #base_model-NousResearch/Meta-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:meta-llama/Meta-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo meta-llama/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model meta-llama/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #llama #text-generation #pruna-ai #base_model-meta-llama/Meta-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo meta-llama/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model meta-llama/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "llama", "text-generation", "pruna-ai", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo meta-llama/Meta-Llama-3-8B-Instruct installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model meta-llama/Meta-Llama-3-8B-Instruct before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #llama #text-generation #pruna-ai #conversational #base_model-meta-llama/Meta-Llama-3-8B-Instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo meta-llama/Meta-Llama-3-8B-Instruct installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model meta-llama/Meta-Llama-3-8B-Instruct before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "conversational", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Meta-Llama-3-8B-Instruct installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Meta-Llama-3-8B-Instruct before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #pruna-ai #conversational #base_model-NousResearch/Meta-Llama-3-8B-Instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Meta-Llama-3-8B-Instruct installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Meta-Llama-3-8B-Instruct before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "llama", "text-generation", "pruna-ai", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo meta-llama/Meta-Llama-3-8B-Instruct installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model meta-llama/Meta-Llama-3-8B-Instruct before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #llama #text-generation #pruna-ai #conversational #base_model-meta-llama/Meta-Llama-3-8B-Instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo meta-llama/Meta-Llama-3-8B-Instruct installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model meta-llama/Meta-Llama-3-8B-Instruct before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:meta-llama/Meta-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo meta-llama/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model meta-llama/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #llama #text-generation #pruna-ai #base_model-meta-llama/Meta-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo meta-llama/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model meta-llama/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "peft", "safetensors", "mistral", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:hugodk-sch/aftonposten_title_sft", "base_model:norallm/normistral-7b-warm", "license:apache-2.0", "4-bit", "region:us" ]

[]

[]

[ "### 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* distributed\\_type: multi-GPU\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\\_ratio: 0.1\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.1" ]

[ "TAGS\n#peft #safetensors #mistral #alignment-handbook #trl #sft #generated_from_trainer #dataset-hugodk-sch/aftonposten_title_sft #base_model-norallm/normistral-7b-warm #license-apache-2.0 #4-bit #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* distributed\\_type: multi-GPU\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\\_ratio: 0.1\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.1" ]

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert/distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-small", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\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 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-small #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: 3\n* eval\\_batch\\_size: 3\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 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \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" ]

[]

[]

[ "### 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 4.40.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.18.0\n* Tokenizers 0.19.1" ]

[ "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: 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 4.40.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.18.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "bert", "text-classification", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "zh" ]

[ "## TextAttack Model Card\n\n This 'bert' model was fine-tuned using TextAttack. The model was fine-tuned\n for 3 epochs with a batch size of 8,\n a maximum sequence length of 512, and an initial learning rate of 3e-05.\n Since this was a classification task, the model was trained with a cross-entropy loss function.\n The best score the model achieved on this task was 0.9656666666666667, as measured by the\n eval set accuracy, found after 3 epochs.\n\n For more information, check out TextAttack on Github." ]

[ "TAGS\n#transformers #safetensors #bert #text-classification #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "## TextAttack Model Card\n\n This 'bert' model was fine-tuned using TextAttack. The model was fine-tuned\n for 3 epochs with a batch size of 8,\n a maximum sequence length of 512, and an initial learning rate of 3e-05.\n Since this was a classification task, the model was trained with a cross-entropy loss function.\n The best score the model achieved on this task was 0.9656666666666667, as measured by the\n eval set accuracy, found after 3 epochs.\n\n For more information, check out TextAttack on Github." ]

[ "peft", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "dataset:updated", "dataset:original", "base_model:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_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\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #dpo #dataset-updated #dataset-original #base_model-alignment-handbook/zephyr-7b-sft-full #license-apache-2.0 #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: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_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\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "base_model:aaditya/Llama3-OpenBioLLM-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo aaditya/Llama3-OpenBioLLM-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model aaditya/Llama3-OpenBioLLM-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #pruna-ai #base_model-aaditya/Llama3-OpenBioLLM-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo aaditya/Llama3-OpenBioLLM-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model aaditya/Llama3-OpenBioLLM-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "tensorboard", "safetensors", "segformer", "image-classification", "autotrain", "dataset:as-cle-bert/breastcanc-ultrasound-class", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# Model Trained Using AutoTrain\n\n- Problem type: Image Classification", "## Validation Metrics\nNo validation metrics available" ]

[ "TAGS\n#transformers #tensorboard #safetensors #segformer #image-classification #autotrain #dataset-as-cle-bert/breastcanc-ultrasound-class #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\n- Problem type: Image Classification", "## Validation Metrics\nNo validation metrics available" ]

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #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: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "transformers", "gemma", "text-generation", "pruna-ai", "base_model:google/gemma-2b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-2b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-2b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #gemma #text-generation #pruna-ai #base_model-google/gemma-2b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-2b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-2b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "gemma", "text-generation", "pruna-ai", "base_model:google/gemma-2b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-2b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-2b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #gemma #text-generation #pruna-ai #base_model-google/gemma-2b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-2b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-2b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "tf", "distilbert", "token-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 636, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* TensorFlow 2.15.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tf #distilbert #token-classification #generated_from_keras_callback #base_model-distilbert/distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 636, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* TensorFlow 2.15.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "it", "dataset:seeweb/Seeweb-it-292-forLLM", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "it" ]

[]

[ "TAGS\n#transformers #safetensors #phi3 #text-generation #conversational #custom_code #it #dataset-seeweb/Seeweb-it-292-forLLM #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:Minbyul/meditron-7b-wo-medication_qa-sft", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# meditron-7b-dpo-full-sft-wo-medication_qa\n\nThis model is a fine-tuned version of Minbyul/meditron-7b-wo-medication_qa-sft on the HuggingFaceH4/ultrafeedback_binarized dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6347\n- Rewards/chosen: -0.0755\n- Rewards/rejected: -0.3042\n- Rewards/accuracies: 0.7812\n- Rewards/margins: 0.2287\n- Logps/rejected: -613.1549\n- Logps/chosen: -395.2336\n- Logits/rejected: -1.0640\n- Logits/chosen: -1.1745", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 64\n- total_eval_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.39.0.dev0\n- Pytorch 2.1.2\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-Minbyul/meditron-7b-wo-medication_qa-sft #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# meditron-7b-dpo-full-sft-wo-medication_qa\n\nThis model is a fine-tuned version of Minbyul/meditron-7b-wo-medication_qa-sft on the HuggingFaceH4/ultrafeedback_binarized dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6347\n- Rewards/chosen: -0.0755\n- Rewards/rejected: -0.3042\n- Rewards/accuracies: 0.7812\n- Rewards/margins: 0.2287\n- Logps/rejected: -613.1549\n- Logps/chosen: -395.2336\n- Logits/rejected: -1.0640\n- Logits/chosen: -1.1745", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 64\n- total_eval_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.39.0.dev0\n- Pytorch 2.1.2\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "pytorch", "llama", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #pytorch #llama #text-generation #unsloth #trl #sft #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" ]

[ "peft", "pytorch", "tensorboard", "safetensors", "llama", "axolotl", "dpo", "trl", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-hf", "license:llama2", "4-bit", "region:us" ]

[]

[]

[ "# modeltest1-dpo\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-hf on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- training_steps: 222", "### Training results", "### Framework versions\n\n- PEFT 0.10.1.dev0\n- Transformers 4.40.0.dev0\n- Pytorch 2.1.2+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]

[ "TAGS\n#peft #pytorch #tensorboard #safetensors #llama #axolotl #dpo #trl #generated_from_trainer #base_model-codellama/CodeLlama-7b-hf #license-llama2 #4-bit #region-us \n", "# modeltest1-dpo\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-hf on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- training_steps: 222", "### Training results", "### Framework versions\n\n- PEFT 0.10.1.dev0\n- Transformers 4.40.0.dev0\n- Pytorch 2.1.2+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]

[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:Jacaranda/kiswahili_pretrained_no_ext", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\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 0.10.1.dev0" ]

[ "TAGS\n#peft #safetensors #llama #arxiv-1910.09700 #base_model-Jacaranda/kiswahili_pretrained_no_ext #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\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 0.10.1.dev0" ]

[ "transformers", "gemma", "text-generation", "pruna-ai", "base_model:google/gemma-7b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-7b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-7b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #gemma #text-generation #pruna-ai #base_model-google/gemma-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-7b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-7b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "merge", "mergekit", "lazymergekit", "automerger", "license:apache-2.0", "region:us" ]

[]

[]

[ "# Strangemerges_32Neuralsynthesis-7B\n\nStrangemerges_32Neuralsynthesis-7B is an automated merge created by Maxime Labonne using the following configuration.", "## Configuration", "## Usage" ]

[ "TAGS\n#merge #mergekit #lazymergekit #automerger #license-apache-2.0 #region-us \n", "# Strangemerges_32Neuralsynthesis-7B\n\nStrangemerges_32Neuralsynthesis-7B is an automated merge created by Maxime Labonne using the following configuration.", "## Configuration", "## Usage" ]

[ "transformers", "gemma", "text-generation", "pruna-ai", "base_model:google/gemma-7b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-7b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-7b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #gemma #text-generation #pruna-ai #base_model-google/gemma-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-7b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-7b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "gemma", "text-generation", "pruna-ai", "base_model:google/gemma-7b-it", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-7b-it installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-7b-it before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #gemma #text-generation #pruna-ai #base_model-google/gemma-7b-it #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-7b-it installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-7b-it before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:NousResearch/Meta-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #llama #text-generation #pruna-ai #base_model-NousResearch/Meta-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "gemma", "text-generation", "pruna-ai", "base_model:google/gemma-7b-it", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-7b-it installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-7b-it before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #gemma #text-generation #pruna-ai #base_model-google/gemma-7b-it #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-7b-it installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-7b-it before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:NousResearch/Meta-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #llama #text-generation #pruna-ai #base_model-NousResearch/Meta-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Meta-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "license:creativeml-openrail-m", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "license:gemma", "region:us" ]

[]

[]

[ "# gemma-2b-sft\n\nThis model is a fine-tuned version of google/gemma-2b on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-google/gemma-2b #license-gemma #region-us \n", "# gemma-2b-sft\n\nThis model is a fine-tuned version of google/gemma-2b on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.2" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/phi-2", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-microsoft/phi-2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.0" ]

[ "transformers", "gemma", "text-generation", "pruna-ai", "base_model:google/gemma-2b-it", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-2b-it installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-2b-it before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #gemma #text-generation #pruna-ai #base_model-google/gemma-2b-it #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-2b-it installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-2b-it before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "gemma", "text-generation", "pruna-ai", "conversational", "base_model:google/gemma-2b-it", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-2b-it installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-2b-it before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #gemma #text-generation #pruna-ai #conversational #base_model-google/gemma-2b-it #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/gemma-2b-it installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/gemma-2b-it before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### 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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google-t5/t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: ayushgs\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: ayushgs\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "license:mit", "region:us" ]

[]

[]

[ "# Phi2SFT\n\nThis model is a fine-tuned version of microsoft/phi-2 on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\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: cosine\n- lr_scheduler_warmup_steps: 100\n- training_steps: 50", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-microsoft/phi-2 #license-mit #region-us \n", "# Phi2SFT\n\nThis model is a fine-tuned version of microsoft/phi-2 on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\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: cosine\n- lr_scheduler_warmup_steps: 100\n- training_steps: 50", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "gguf", "generated_from_trainer", "axolotl", "GGUF", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:microsoft/orca-math-word-problems-200k", "dataset:abacusai/SystemChat-1.1", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "region:us" ]

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[ "# dolphin-2.9-llama3-8b-GGUF\n- Original model: dolphin-2.9-llama3-8b", "## Description\n\nThis repo contains GGUF format model files for dolphin-2.9-llama3-8b.", "### About GGUF\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n* URL. This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.\n* text-generation-webui, Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.\n* Ollama Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications​\n* KoboldCpp, A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.\n* GPT4All, This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.\n* LM Studio An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.\n* LoLLMS Web UI. A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.\n* URL, An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.\n* llama-cpp-python, A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.\n* candle, A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.\n* ctransformers, A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.\n* localGPT An open-source initiative enabling private conversations with documents.", "## Explanation of quantisation methods\n<details>\n <summary>Click to see details</summary>\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.\n</details>", "## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL", "### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: LiteLLMs/dolphin-2.9-llama3-8b-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-URL.\n\nThen click Download.", "### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>", "## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 8192' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation", "## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.", "## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.", "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code", "## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers", "# Original model card: dolphin-2.9-llama3-8b", "# Dolphin 2.9 Llama 3 8b \n\nCurated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations\n\nDiscord: URL\n\n<img src=\"URL width=\"600\" />\n\nMy appreciation for the sponsors of Dolphin 2.9:\n- Crusoe Cloud - provided excellent on-demand 10xL40S node\n\nThis model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT\n\nThe base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.\n\nIt took 2.5 days on 8x L40S provided by Crusoe Cloud\n\nThis model was trained FFT on all parameters, using ChatML prompt template format.\n\nexample:\n\n\n\nDolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.\n\nDolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.\n\nDolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>\n<details><summary>See axolotl config</summary>\n\naxolotl version: '0.4.0'\n\n\n</details><br>", "## Quants\n\nGGUF : URL\n\nGGUF with imatrix: URL\n\nExllamav2: URL", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 3\n- eval_batch_size: 3\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 96\n- total_eval_batch_size: 24\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 7\n- num_epochs: 3", "### Training results\n\n| Training Loss | Epoch | Step | Validation Loss |\n| :-: | :--: | :-: |\n| 1.146 | 0.0005 | 1 | 1.1064 |\n| 0.6962 | 0.2501 | 555 | 0.6636 |\n| 0.6857 | 0.5001 | 1110 | 0.6503 |\n| 0.6592 | 0.7502 | 1665 | 0.6419 |\n| 0.6465 | 1.0002 | 2220 | 0.6317 |\n| 0.5295 | 1.2395 | 2775 | 0.6408 |\n| 0.5302 | 1.4895 | 3330 | 0.6351 |\n| 0.5188 | 1.7396 | 3885 | 0.6227 |\n| 0.521 | 1.9896 | 4440 | 0.6168 |\n| 0.3968 | 2.2289 | 4995 | 0.6646 |\n| 0.3776 | 2.4789 | 5550 | 0.6619 |\n| 0.3983 | 2.7290 | 6105 | 0.6602 |", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#gguf #generated_from_trainer #axolotl #GGUF #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us \n", "# dolphin-2.9-llama3-8b-GGUF\n- Original model: dolphin-2.9-llama3-8b", "## Description\n\nThis repo contains GGUF format model files for dolphin-2.9-llama3-8b.", "### About GGUF\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n* URL. This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.\n* text-generation-webui, Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.\n* Ollama Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications​\n* KoboldCpp, A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.\n* GPT4All, This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.\n* LM Studio An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.\n* LoLLMS Web UI. A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.\n* URL, An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.\n* llama-cpp-python, A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.\n* candle, A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.\n* ctransformers, A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.\n* localGPT An open-source initiative enabling private conversations with documents.", "## Explanation of quantisation methods\n<details>\n <summary>Click to see details</summary>\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.\n</details>", "## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL", "### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: LiteLLMs/dolphin-2.9-llama3-8b-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-URL.\n\nThen click Download.", "### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>", "## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 8192' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation", "## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.", "## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.", "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code", "## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers", "# Original model card: dolphin-2.9-llama3-8b", "# Dolphin 2.9 Llama 3 8b \n\nCurated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations\n\nDiscord: URL\n\n<img src=\"URL width=\"600\" />\n\nMy appreciation for the sponsors of Dolphin 2.9:\n- Crusoe Cloud - provided excellent on-demand 10xL40S node\n\nThis model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT\n\nThe base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.\n\nIt took 2.5 days on 8x L40S provided by Crusoe Cloud\n\nThis model was trained FFT on all parameters, using ChatML prompt template format.\n\nexample:\n\n\n\nDolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.\n\nDolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.\n\nDolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>\n<details><summary>See axolotl config</summary>\n\naxolotl version: '0.4.0'\n\n\n</details><br>", "## Quants\n\nGGUF : URL\n\nGGUF with imatrix: URL\n\nExllamav2: URL", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 3\n- eval_batch_size: 3\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 96\n- total_eval_batch_size: 24\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 7\n- num_epochs: 3", "### Training results\n\n| Training Loss | Epoch | Step | Validation Loss |\n| :-: | :--: | :-: |\n| 1.146 | 0.0005 | 1 | 1.1064 |\n| 0.6962 | 0.2501 | 555 | 0.6636 |\n| 0.6857 | 0.5001 | 1110 | 0.6503 |\n| 0.6592 | 0.7502 | 1665 | 0.6419 |\n| 0.6465 | 1.0002 | 2220 | 0.6317 |\n| 0.5295 | 1.2395 | 2775 | 0.6408 |\n| 0.5302 | 1.4895 | 3330 | 0.6351 |\n| 0.5188 | 1.7396 | 3885 | 0.6227 |\n| 0.521 | 1.9896 | 4440 | 0.6168 |\n| 0.3968 | 2.2289 | 4995 | 0.6646 |\n| 0.3776 | 2.4789 | 5550 | 0.6619 |\n| 0.3983 | 2.7290 | 6105 | 0.6602 |", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00025\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 300\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #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: 0.00025\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 300\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]

[ "transformers", "safetensors", "deformable_detr", "object-detection", "generated_from_trainer", "base_model:SenseTime/deformable-detr", "license:apache-2.0", "endpoints_compatible", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 1337\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: linear\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 1.13.0+cu117\n* Datasets 2.18.0\n* Tokenizers 0.19.0" ]

[ "TAGS\n#transformers #safetensors #deformable_detr #object-detection #generated_from_trainer #base_model-SenseTime/deformable-detr #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: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 1337\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: linear\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 1.13.0+cu117\n* Datasets 2.18.0\n* Tokenizers 0.19.0" ]

[ "pyannote", "pytorch", "pyannote.audio", "pyannote-audio-model", "audio", "voice", "speech", "speaker", "speaker-diarization", "speaker-change-detection", "speaker-segmentation", "voice-activity-detection", "overlapped-speech-detection", "resegmentation", "speaker-recognition", "speaker-verification", "speaker-identification", "speaker-embedding", "PyTorch", "wespeaker", "region:us" ]

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[ "TAGS\n#pyannote #pytorch #pyannote.audio #pyannote-audio-model #audio #voice #speech #speaker #speaker-diarization #speaker-change-detection #speaker-segmentation #voice-activity-detection #overlapped-speech-detection #resegmentation #speaker-recognition #speaker-verification #speaker-identification #speaker-embedding #PyTorch #wespeaker #region-us \n" ]

[ "transformers", "gguf", "mr", "en", "dataset:smallstepai/marathi-instruction-tuning-alpaca", "dataset:ai4bharat/indic-align", "license:llama3", "endpoints_compatible", "region:us" ]

[]

[ "mr", "en" ]

[ "# Model Card for Model ID", "## Model Details\nShivneri Marathi LLM is being built with the wish to bring the benefits of Generative AI to non-English (especially Marathi) speaking population of India.\nMarathi has the third largest number of native speakers in India, after Hindi and Bengali. \nAlmost 83 million people speak the language. \nThis is a preliminary version of our Marathi LLM (Large Language Model)!\nBuilt on the mighty Llama3 8B instruct model, Shivneri LLM can generate creative and informative text in both Marathi and English. This is just the beginning – we're constantly improving Shivneri, and even more exciting features are on the horizon!", "### 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: Amit Ghadge\n- Funded by [optional]: \n- Shared by [optional]: [Amit Ghadge]\n- Model type: [ Decoder-only large language model (LLM) with a transformer architecture]\n- Language(s) (NLP): [Marathi, English]\n- License: \n- Finetuned from model [optional]: [Meta-Llama-3-8B-Instruct]", "### Model Sources [optional]\n\n\n\n- Repository: [URL\n- Paper [optional]: [URL\n- Demo [optional]: [Coming soon]", "## Uses\n\n\nThis is a very preliminary version. Please use with caution. Would suggest to more updates and final models to try out.", "## Training Details", "### Training Data\n\n\n\n[SFT with Lora on mentioned datasets above]", "### Training Procedure\n\n\nSFT with Lora", "### Model Architecture and Objective\n\n[ Decoder-only large language model (LLM) with a transformer architecture]", "### Compute Infrastructure\n\n[A100 80 GB]", "## Meet the Developers\n\nGet to know the creators behind this innovative model and follow their contributions to the field:\n\n- Amit Ghadge", "## Model Release Date May 1st, 2024.\n\nStatus This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.", "## License\nThe model inherits the license from meta-llama3.", "## How to use \nUse pretty much remains the same as original Meta-Llama-3-8B-Instruct model. Visit its page for more details.\nWith this model you can now use Marathi prompts and build conversational apps using it.\n\n[optional]\n\nIf you use this model in your research, please cite:\n\n\n\nWe hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Marathi language." ]

[ "TAGS\n#transformers #gguf #mr #en #dataset-smallstepai/marathi-instruction-tuning-alpaca #dataset-ai4bharat/indic-align #license-llama3 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details\nShivneri Marathi LLM is being built with the wish to bring the benefits of Generative AI to non-English (especially Marathi) speaking population of India.\nMarathi has the third largest number of native speakers in India, after Hindi and Bengali. \nAlmost 83 million people speak the language. \nThis is a preliminary version of our Marathi LLM (Large Language Model)!\nBuilt on the mighty Llama3 8B instruct model, Shivneri LLM can generate creative and informative text in both Marathi and English. This is just the beginning – we're constantly improving Shivneri, and even more exciting features are on the horizon!", "### 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: Amit Ghadge\n- Funded by [optional]: \n- Shared by [optional]: [Amit Ghadge]\n- Model type: [ Decoder-only large language model (LLM) with a transformer architecture]\n- Language(s) (NLP): [Marathi, English]\n- License: \n- Finetuned from model [optional]: [Meta-Llama-3-8B-Instruct]", "### Model Sources [optional]\n\n\n\n- Repository: [URL\n- Paper [optional]: [URL\n- Demo [optional]: [Coming soon]", "## Uses\n\n\nThis is a very preliminary version. Please use with caution. Would suggest to more updates and final models to try out.", "## Training Details", "### Training Data\n\n\n\n[SFT with Lora on mentioned datasets above]", "### Training Procedure\n\n\nSFT with Lora", "### Model Architecture and Objective\n\n[ Decoder-only large language model (LLM) with a transformer architecture]", "### Compute Infrastructure\n\n[A100 80 GB]", "## Meet the Developers\n\nGet to know the creators behind this innovative model and follow their contributions to the field:\n\n- Amit Ghadge", "## Model Release Date May 1st, 2024.\n\nStatus This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.", "## License\nThe model inherits the license from meta-llama3.", "## How to use \nUse pretty much remains the same as original Meta-Llama-3-8B-Instruct model. Visit its page for more details.\nWith this model you can now use Marathi prompts and build conversational apps using it.\n\n[optional]\n\nIf you use this model in your research, please cite:\n\n\n\nWe hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Marathi language." ]

[ "transformers", "gemma", "text-generation", "pruna-ai", "base_model:google/codegemma-2b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/codegemma-2b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/codegemma-2b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "TAGS\n#transformers #gemma #text-generation #pruna-ai #base_model-google/codegemma-2b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo google/codegemma-2b installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model google/codegemma-2b before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# GreenBit LLMs\n\nThis is GreenBitAI's pretrained low-bit LLMs with extreme compression yet still strong performance.\n\nPlease refer to our Github page for the code to run the model and more information." ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# GreenBit LLMs\n\nThis is GreenBitAI's pretrained low-bit LLMs with extreme compression yet still strong performance.\n\nPlease refer to our Github page for the code to run the model and more information." ]