Datasets:

---

ftopal

/

huggingface-models

like 0

[ "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", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: WeOneGuy\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-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 #gguf #mistral #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: WeOneGuy\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-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", "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", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.001_4iters_bs256_nodpo_only4w_iter_1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets.", "## 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: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\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 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_4iters_bs256_nodpo_only4w_iter_1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets.", "## 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: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\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 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "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" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "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-mistralai/Mistral-7B-Instruct-v0.2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.0" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\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", "recurrent_gemma", "text-generation", "generated_from_trainer", "en", "dataset:BEE-spoke-data/fineweb-1M_en-med", "base_model:pszemraj/griffin-1024-llama3t-8layer-simplewiki-silu", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 80085\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07\n* lr\\_scheduler\\_type: inverse\\_sqrt\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.3.0+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #recurrent_gemma #text-generation #generated_from_trainer #en #dataset-BEE-spoke-data/fineweb-1M_en-med #base_model-pszemraj/griffin-1024-llama3t-8layer-simplewiki-silu #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: 2\n* eval\\_batch\\_size: 2\n* seed: 80085\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07\n* lr\\_scheduler\\_type: inverse\\_sqrt\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.3.0+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "transformers", "gguf", "en", "base_model:AiMavenAi/Herd-1", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #en #base_model-AiMavenAi/Herd-1 #endpoints_compatible #region-us \n" ]

[ "en", "arxiv:2309.10400", "license:cc-by-nc-4.0", "region:us" ]

[ "2309.10400" ]

[ "en" ]

[ "### Use with transformers\n\n\nSee the snippet below for usage with Transformers:", "### Use with 'llama3'\n\n\nPlease, follow the instructions in the repository.\n\n\nTo download Original checkpoints, see the example command below leveraging 'huggingface-cli':\n\n\nFor Hugging Face support, we recommend using transformers or TGI, but a similar command works.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.\n\n\nCarbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\n\nCO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.\n\n\nTraining Data\n-------------\n\n\nOverview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.\n\n\nData Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.\n\n\nBenchmarks\n----------\n\n\nIn this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.", "### 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\n\n\ninstructions\n\n\n@article{llama3modelcard,\n\n\ntitle={Llama 3 Model Card},\n\n\nauthor={AI@Meta},\n\n\nyear={2024},\n\n\nurl = {URL\n\n\n}\n\n\nContributors\n------------\n\n\nAaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos" ]

[ "TAGS\n#en #arxiv-2309.10400 #license-cc-by-nc-4.0 #region-us \n", "### Use with transformers\n\n\nSee the snippet below for usage with Transformers:", "### Use with 'llama3'\n\n\nPlease, follow the instructions in the repository.\n\n\nTo download Original checkpoints, see the example command below leveraging 'huggingface-cli':\n\n\nFor Hugging Face support, we recommend using transformers or TGI, but a similar command works.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.\n\n\nCarbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\n\nCO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.\n\n\nTraining Data\n-------------\n\n\nOverview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.\n\n\nData Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.\n\n\nBenchmarks\n----------\n\n\nIn this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.", "### 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\n\n\ninstructions\n\n\n@article{llama3modelcard,\n\n\ntitle={Llama 3 Model Card},\n\n\nauthor={AI@Meta},\n\n\nyear={2024},\n\n\nurl = {URL\n\n\n}\n\n\nContributors\n------------\n\n\nAaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos" ]

[ "mlx", "safetensors", "llama", "facebook", "meta", "pytorch", "llama-3", "text-generation", "conversational", "en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# mlx-community/Meta-Llama-3-8B-Instruct\nThis model was converted to MLX format from ['meta-llama/Meta-Llama-3-8B-Instruct']() using mlx-lm version 0.12.0.\nRefer to the original model card for more details on the model.", "## Use with mlx" ]

[ "TAGS\n#mlx #safetensors #llama #facebook #meta #pytorch #llama-3 #text-generation #conversational #en #license-other #region-us \n", "# mlx-community/Meta-Llama-3-8B-Instruct\nThis model was converted to MLX format from ['meta-llama/Meta-Llama-3-8B-Instruct']() using mlx-lm version 0.12.0.\nRefer to the original model card for more details on the model.", "## Use with mlx" ]

[ "transformers", "safetensors", "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: jjohnsondev\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 #safetensors #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: jjohnsondev\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", "llama", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us", "has_space" ]

[ "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 #unsloth #trl #sft #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us #has_space \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", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:tner/xlm-roberta-base-panx-dataset-ar", "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: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### 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 #xlm-roberta #token-classification #generated_from_trainer #base_model-tner/xlm-roberta-base-panx-dataset-ar #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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### 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", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.001_4iters_bs128_nodpo_only4w_userresponse_iter_2\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1 on the updated and the original datasets.", "## 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: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\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 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_4iters_bs128_nodpo_only4w_userresponse_iter_2\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1 on the updated and the original datasets.", "## 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: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\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 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

[ "transformers", "gguf", "llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "base_model:aaditya/OpenBioLLM-Llama3-70B", "license:llama3", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #llama-3 #llama #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #base_model-aaditya/OpenBioLLM-Llama3-70B #license-llama3 #endpoints_compatible #region-us \n" ]

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

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 6", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.2+cpu\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract #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: 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: 6", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.2+cpu\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "transformers", "gguf", "agents", "agent", "llm", "llama", "llama-cpp", "gguf-my-repo", "en", "dataset:McGill-NLP/WebLINX", "license:llama3", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# xhluca/Llama-3-8B-Web-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'McGill-NLP/Llama-3-8B-Web' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "TAGS\n#transformers #gguf #agents #agent #llm #llama #llama-cpp #gguf-my-repo #en #dataset-McGill-NLP/WebLINX #license-llama3 #endpoints_compatible #region-us \n", "# xhluca/Llama-3-8B-Web-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'McGill-NLP/Llama-3-8B-Web' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "transformers", "llamafile", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "base_model:CohereForAI/c4ai-command-r-plus", "license:other", "endpoints_compatible", "region:us" ]

[]

[ "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar" ]

[ "### Tool use & multihop capabilities:\n\n\nCommand R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.\n\n\nCommand R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once.\n\n\nThe model has been trained to recognise a special 'directly\\_answer' tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions.\nWe recommend including the 'directly\\_answer' tool, but it can be removed or renamed if required.\n\n\nComprehensive documentation for working with command R+'s tool use prompt template can be found here.\n\n\nThe code snippet below shows a minimal working example on how to render a prompt.\n\n\n\n**Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]** \n\n\n**Example Rendered Tool Use Prompt [CLICK TO EXPAND]**\npython\ndef internet\\_search(query: str) -> List[Dict]:\n\"\"\"Returns a list of relevant document snippets for a textual query retrieved from the internet\n\n\n\n```\nArgs:\n query (str): Query to search the internet with\n\"\"\"\npass\n\n```\n\npython\ndef directly\\_answer() -> List[Dict]:\n\"\"\"Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history\n\"\"\"\npass\njson\n[\n{\n\"tool\\_name\": title of the tool in the specification,\n\"parameters\": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters\n}\n]'\n\n\n\n\n**Example Rendered Tool Use Completion [CLICK TO EXPAND]**\njson\n[\n{\n\"tool\\_name\": \"internet\\_search\",\n\"parameters\": {\n\"query\": \"biggest penguin in the world\"\n}\n}\n]\n'", "### Grounded Generation and RAG Capabilities:\n\n\nCommand R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.\n\n\nCommand R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.\n\n\nBy default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as 'accurate' grounded generation.\n\n\nThe model is trained with a number of other answering modes, which can be selected by prompt changes. A 'fast' citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.\n\n\nComprehensive documentation for working with Command R+'s grounded generation prompt template can be found here.\n\n\nThe code snippet below shows a minimal working example on how to render a prompt.\n\n\n\n **Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]** \n'\n\n\n\n\n**Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]**\n'\n\n\n\n\n**Example Rendered Grounded Generation Completion [CLICK TO EXPAND]**\n'", "### Code Capabilities:\n\n\nCommand R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.", "### Model Card Contact\n\n\nFor errors or additional questions about details in this model card, contact info@URL.", "### Terms of Use:\n\n\nWe hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 104 billion parameter model to researchers all over the world. This model is governed by a CC-BY-NC License with an acceptable use addendum, and also requires adhering to C4AI's Acceptable Use Policy.", "### Try Chat:\n\n\nYou can try Command R+ chat in the playground here. You can also use it in our dedicated Hugging Face Space here." ]

[ "TAGS\n#transformers #llamafile #text-generation #en #fr #de #es #it #pt #ja #ko #zh #ar #base_model-CohereForAI/c4ai-command-r-plus #license-other #endpoints_compatible #region-us \n", "### Tool use & multihop capabilities:\n\n\nCommand R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.\n\n\nCommand R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once.\n\n\nThe model has been trained to recognise a special 'directly\\_answer' tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions.\nWe recommend including the 'directly\\_answer' tool, but it can be removed or renamed if required.\n\n\nComprehensive documentation for working with command R+'s tool use prompt template can be found here.\n\n\nThe code snippet below shows a minimal working example on how to render a prompt.\n\n\n\n**Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]** \n\n\n**Example Rendered Tool Use Prompt [CLICK TO EXPAND]**\npython\ndef internet\\_search(query: str) -> List[Dict]:\n\"\"\"Returns a list of relevant document snippets for a textual query retrieved from the internet\n\n\n\n```\nArgs:\n query (str): Query to search the internet with\n\"\"\"\npass\n\n```\n\npython\ndef directly\\_answer() -> List[Dict]:\n\"\"\"Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history\n\"\"\"\npass\njson\n[\n{\n\"tool\\_name\": title of the tool in the specification,\n\"parameters\": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters\n}\n]'\n\n\n\n\n**Example Rendered Tool Use Completion [CLICK TO EXPAND]**\njson\n[\n{\n\"tool\\_name\": \"internet\\_search\",\n\"parameters\": {\n\"query\": \"biggest penguin in the world\"\n}\n}\n]\n'", "### Grounded Generation and RAG Capabilities:\n\n\nCommand R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.\n\n\nCommand R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.\n\n\nBy default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as 'accurate' grounded generation.\n\n\nThe model is trained with a number of other answering modes, which can be selected by prompt changes. A 'fast' citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.\n\n\nComprehensive documentation for working with Command R+'s grounded generation prompt template can be found here.\n\n\nThe code snippet below shows a minimal working example on how to render a prompt.\n\n\n\n **Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]** \n'\n\n\n\n\n**Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]**\n'\n\n\n\n\n**Example Rendered Grounded Generation Completion [CLICK TO EXPAND]**\n'", "### Code Capabilities:\n\n\nCommand R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.", "### Model Card Contact\n\n\nFor errors or additional questions about details in this model card, contact info@URL.", "### Terms of Use:\n\n\nWe hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 104 billion parameter model to researchers all over the world. This model is governed by a CC-BY-NC License with an acceptable use addendum, and also requires adhering to C4AI's Acceptable Use Policy.", "### Try Chat:\n\n\nYou can try Command R+ chat in the playground here. You can also use it in our dedicated Hugging Face Space here." ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

[ "1910.09700" ]

[]

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

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

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_17_0", "base_model:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 0.1\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.17.1\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_17_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 0.1\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.17.1\n* Tokenizers 0.19.1" ]

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_11_0", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\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: 10\n* training\\_steps: 50\n* mixed\\_precision\\_training: Native AMP", "### 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 #whisper #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\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: 10\n* training\\_steps: 50\n* mixed\\_precision\\_training: Native AMP", "### 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", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2212.04089" ]

[]

[ "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using NousResearch/Meta-Llama-3-8B as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* NousResearch/Meta-Llama-3-8B-Instruct", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2212.04089 #base_model-NousResearch/Meta-Llama-3-8B #base_model-NousResearch/Meta-Llama-3-8B-Instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using NousResearch/Meta-Llama-3-8B as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* NousResearch/Meta-Llama-3-8B-Instruct", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

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[ "# 0.001_4iters_bs256_nodpo_only4w_userresponse_iter_2\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1 on the updated and the original datasets.", "## 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: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\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 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_4iters_bs256_nodpo_only4w_userresponse_iter_2\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1 on the updated and the original datasets.", "## 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: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\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 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: yeilho\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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 #pytorch #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #conversational #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: yeilho\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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", "llama", "text-generation", "not-for-all-audiences", "nsfw", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:vicgalle/Roleplay-Llama-3-8B", "base_model:Undi95/Llama-3-Unholy-8B-e4", "base_model:Undi95/Llama-3-LewdPlay-8B", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

[ "2311.03099", "2306.01708" ]

[]

[ "# LewdPlay-8B\n\nThis is a merge of pre-trained language models created using mergekit.\n\nThe new EVOLVE merge method was used (on MMLU specifically), see below for more information!\n\nUnholy was used for uncensoring, Roleplay Llama 3 for the DPO train he got on top, and LewdPlay for the... lewd side.", "## Prompt template: Llama3", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using ./mergekit/input_models/Roleplay-Llama-3-8B_213413727 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923\n* ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Support\n\nIf you want to support me, you can here." ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #merge #conversational #arxiv-2311.03099 #arxiv-2306.01708 #base_model-vicgalle/Roleplay-Llama-3-8B #base_model-Undi95/Llama-3-Unholy-8B-e4 #base_model-Undi95/Llama-3-LewdPlay-8B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "# LewdPlay-8B\n\nThis is a merge of pre-trained language models created using mergekit.\n\nThe new EVOLVE merge method was used (on MMLU specifically), see below for more information!\n\nUnholy was used for uncensoring, Roleplay Llama 3 for the DPO train he got on top, and LewdPlay for the... lewd side.", "## Prompt template: Llama3", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using ./mergekit/input_models/Roleplay-Llama-3-8B_213413727 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923\n* ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Support\n\nIf you want to support me, you can here." ]

[ "transformers", "gguf", "reasoning", "preference_learning", "nca", "en", "dataset:openbmb/UltraInteract_sft", "dataset:openbmb/UltraInteract_pair", "dataset:openbmb/UltraFeedback", "base_model:openbmb/Eurux-8x22b-nca", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #reasoning #preference_learning #nca #en #dataset-openbmb/UltraInteract_sft #dataset-openbmb/UltraInteract_pair #dataset-openbmb/UltraFeedback #base_model-openbmb/Eurux-8x22b-nca #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ "transformers", "tensorboard", "safetensors", "phi", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "custom_code", "dataset:GAIR/lima", "base_model:microsoft/phi-2", "license:mit", "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: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_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\\_ratio: 0.1\n* num\\_epochs: 10.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #phi #text-generation #alignment-handbook #trl #sft #generated_from_trainer #conversational #custom_code #dataset-GAIR/lima #base_model-microsoft/phi-2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_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\\_ratio: 0.1\n* num\\_epochs: 10.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

[]

[]

[ "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Laz4rz/hf-huggy-1-bonus\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n", "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Laz4rz/hf-huggy-1-bonus\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "transformers", "safetensors", "gguf", "llama", "text-generation", "nlp", "code", "conversational", "en", "dataset:Josephgflowers/just_cinder", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[ "## Model Summary\n\nThe Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties.\nThe model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support.\n\nThe model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures.\nWhen assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters.\n\nResources and Technical Documentation:\n\n+ Phi-3 Microsoft Blog\n+ Phi-3 Technical Report\n+ Phi-3 on Azure AI Studio\n+ Phi-3 GGUF: 4K\n+ Phi-3 ONNX: 4K", "## Intended Uses\n\nPrimary use cases\n\nThe model is intended for commercial and research use in English. The model provides uses for applications which require:\n\n1) Memory/compute constrained environments\n2) Latency bound scenarios\n3) Strong reasoning (especially code, math and logic)\n\nOur model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. \n\nUse case considerations\n\nOur models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.\n\nNothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.", "## How to Use\n\nPhi-3 Mini-4K-Instruct has been integrated in the development version (4.40.0) of 'transformers'. Until the official version is released through 'pip', ensure that you are doing one of the following:\n\n* When loading the model, ensure that 'trust_remote_code=True' is passed as an argument of the 'from_pretrained()' function.\n\n* Update your local 'transformers' to the development version: 'pip uninstall -y transformers && pip install git+URL The previous command is an alternative to cloning and installing from the source.\n\nThe current 'transformers' version can be verified with: 'pip list | grep transformers'.\n\nPhi-3 Mini-4K-Instruct is also available in HuggingChat.", "### Chat Format\n\nGiven the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows. \nYou can provide the prompt as a question with a generic template as follow:\n\nFor example:\n\n\nwhere the model generates the text after '<|assistant|>' . In case of few-shots prompt, the prompt can be formatted as the following:", "### Sample inference code\n\nThis code snippets show how to get quickly started with running the model on a GPU:", "## Responsible AI Considerations\n\nLike other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:\n\n+ Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. \n+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. \n+ Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. \n+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. \n+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as \"typing, math, random, collections, datetime, itertools\". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. \n\nDevelopers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:\n\n+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.\n+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. \n+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). \n+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. \n+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.", "## Training", "### Model\n\n* Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.\n* Inputs: Text. It is best suited for prompts using chat format.\n* Context length: 4K tokens\n* GPUs: 512 H100-80G\n* Training time: 7 days\n* Training data: 3.3T tokens\n* Outputs: Generated text in response to the input\n* Dates: Our models were trained between February and April 2024\n* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.", "### Datasets\n\nOur training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of \n1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; \n2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); \n3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.", "### Fine-tuning\n\nA basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided here.", "## Software\n\n* PyTorch\n* DeepSpeed\n* Transformers\n* Flash-Attention", "## Hardware\nNote that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:\n* NVIDIA A100\n* NVIDIA A6000\n* NVIDIA H100\n\nIf you want to run the model on:\n* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation=\"eager\"\n* CPU: use the GGUF quantized models 4K\n+ Optimized inference on GPU, CPU, and Mobile: use the ONNX models 4K", "## Cross Platform Support\n\nONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model here.\n\nOptimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. \nAlong with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile.\n\nHere are some of the optimized configurations we have added: \n\n1. ONNX models for int4 DML: Quantized to int4 via AWQ\n2. ONNX model for fp16 CUDA\n3. ONNX model for int4 CUDA: Quantized to int4 via RTN\n4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN", "## License\n\nThe model is licensed under the MIT license.", "## Trademarks\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies." ]

[ "TAGS\n#transformers #safetensors #gguf #llama #text-generation #nlp #code #conversational #en #dataset-Josephgflowers/just_cinder #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Model Summary\n\nThe Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties.\nThe model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support.\n\nThe model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures.\nWhen assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters.\n\nResources and Technical Documentation:\n\n+ Phi-3 Microsoft Blog\n+ Phi-3 Technical Report\n+ Phi-3 on Azure AI Studio\n+ Phi-3 GGUF: 4K\n+ Phi-3 ONNX: 4K", "## Intended Uses\n\nPrimary use cases\n\nThe model is intended for commercial and research use in English. The model provides uses for applications which require:\n\n1) Memory/compute constrained environments\n2) Latency bound scenarios\n3) Strong reasoning (especially code, math and logic)\n\nOur model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. \n\nUse case considerations\n\nOur models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.\n\nNothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.", "## How to Use\n\nPhi-3 Mini-4K-Instruct has been integrated in the development version (4.40.0) of 'transformers'. Until the official version is released through 'pip', ensure that you are doing one of the following:\n\n* When loading the model, ensure that 'trust_remote_code=True' is passed as an argument of the 'from_pretrained()' function.\n\n* Update your local 'transformers' to the development version: 'pip uninstall -y transformers && pip install git+URL The previous command is an alternative to cloning and installing from the source.\n\nThe current 'transformers' version can be verified with: 'pip list | grep transformers'.\n\nPhi-3 Mini-4K-Instruct is also available in HuggingChat.", "### Chat Format\n\nGiven the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows. \nYou can provide the prompt as a question with a generic template as follow:\n\nFor example:\n\n\nwhere the model generates the text after '<|assistant|>' . In case of few-shots prompt, the prompt can be formatted as the following:", "### Sample inference code\n\nThis code snippets show how to get quickly started with running the model on a GPU:", "## Responsible AI Considerations\n\nLike other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:\n\n+ Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. \n+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. \n+ Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. \n+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. \n+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as \"typing, math, random, collections, datetime, itertools\". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. \n\nDevelopers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:\n\n+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.\n+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. \n+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). \n+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. \n+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.", "## Training", "### Model\n\n* Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.\n* Inputs: Text. It is best suited for prompts using chat format.\n* Context length: 4K tokens\n* GPUs: 512 H100-80G\n* Training time: 7 days\n* Training data: 3.3T tokens\n* Outputs: Generated text in response to the input\n* Dates: Our models were trained between February and April 2024\n* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.", "### Datasets\n\nOur training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of \n1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; \n2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); \n3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.", "### Fine-tuning\n\nA basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided here.", "## Software\n\n* PyTorch\n* DeepSpeed\n* Transformers\n* Flash-Attention", "## Hardware\nNote that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:\n* NVIDIA A100\n* NVIDIA A6000\n* NVIDIA H100\n\nIf you want to run the model on:\n* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation=\"eager\"\n* CPU: use the GGUF quantized models 4K\n+ Optimized inference on GPU, CPU, and Mobile: use the ONNX models 4K", "## Cross Platform Support\n\nONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model here.\n\nOptimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. \nAlong with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile.\n\nHere are some of the optimized configurations we have added: \n\n1. ONNX models for int4 DML: Quantized to int4 via AWQ\n2. ONNX model for fp16 CUDA\n3. ONNX model for int4 CUDA: Quantized to int4 via RTN\n4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN", "## License\n\nThe model is licensed under the MIT license.", "## Trademarks\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies." ]

[ "transformers", "gguf", "llama-3-70b", "sft", "medical", "en", "base_model:johnsnowlabs/JSL-MedLlama-3-70B-v1.0", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #llama-3-70b #sft #medical #en #base_model-johnsnowlabs/JSL-MedLlama-3-70B-v1.0 #license-cc-by-nc-nd-4.0 #endpoints_compatible #region-us \n" ]

[ "gguf", "region:us" ]

[]

[]

[]

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

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "peft", "tensorboard", "safetensors", "llava", "trl", "sft", "generated_from_trainer", "base_model:llava-hf/llava-1.5-7b-hf", "4-bit", "region:us" ]

[]

[]

[ "# llava-1.5-7b-hf-ft-mix-vsft\n\nThis model is a fine-tuned version of llava-hf/llava-1.5-7b-hf on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.4e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 8\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: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.1\n- Pytorch 2.2.0\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#peft #tensorboard #safetensors #llava #trl #sft #generated_from_trainer #base_model-llava-hf/llava-1.5-7b-hf #4-bit #region-us \n", "# llava-1.5-7b-hf-ft-mix-vsft\n\nThis model is a fine-tuned version of llava-hf/llava-1.5-7b-hf on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.4e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 8\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: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.1\n- Pytorch 2.2.0\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

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

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: jspr\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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-Instruct-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: jspr\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: jspr\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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 #sft #conversational #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: jspr\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "safetensors", "arxiv:2304.12244", "arxiv:2306.08568", "arxiv:2308.09583", "license:apache-2.0", "region:us" ]

[ "2304.12244", "2306.08568", "2308.09583" ]

[]

[ "## See here for the WizardLM-2-7B re-upload.", "## News [2024/04/15]\n\nWe introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, \nwhich have improved performance on complex chat, multilingual, reasoning and agent. \nNew family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.\n\n- WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works \nand consistently outperforms all the existing state-of-the-art opensource models.\n- WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. \n- WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.\n\nFor more details of WizardLM-2 please read our release blog post and upcoming paper.", "## Model Details\n\n* Model name: WizardLM-2 8x22B\n* Developed by: WizardLM@Microsoft AI\n* Model type: Mixture of Experts (MoE)\n* Base model: mistral-community/Mixtral-8x22B-v0.1\n* Parameters: 141B\n* Language(s): Multilingual\n* Blog: Introducing WizardLM-2\n* Repository: URL\n* Paper: WizardLM-2 (Upcoming)\n* License: Apache2.0", "## Model Capacities\n\n\nMT-Bench\n\nWe also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. \nThe WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. \nMeanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.\n\n<p align=\"center\" width=\"100%\">\n<a ><img src=\"URL/URL alt=\"MTBench\" style=\"width: 96%; min-width: 300px; display: block; margin: auto;\"></a>\n</p>\n\n\nHuman Preferences Evaluation\n\nWe carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. \nWe report the win:loss rate without tie:\n\n- WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314.\n- WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat.\n- WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta.\n\n<p align=\"center\" width=\"100%\">\n<a ><img src=\"URL/URL alt=\"Win\" style=\"width: 96%; min-width: 300px; display: block; margin: auto;\"></a>\n</p>", "## Method Overview\nWe built a fully AI powered synthetic training system to train WizardLM-2 models, please refer to our blog for more details of this system.\n\n<p align=\"center\" width=\"100%\">\n<a ><img src=\"URL/URL alt=\"Method\" style=\"width: 96%; min-width: 300px; display: block; margin: auto;\"></a>\n</p>", "## Usage\n\n<b>Note for model system prompts usage:</b>\n\n\n<b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports multi-turn conversation. The prompt should be as following:\n\n\n\n<b> Inference WizardLM-2 Demo Script</b>\n\nWe provide a WizardLM-2 inference demo code on our github." ]

[ "TAGS\n#safetensors #arxiv-2304.12244 #arxiv-2306.08568 #arxiv-2308.09583 #license-apache-2.0 #region-us \n", "## See here for the WizardLM-2-7B re-upload.", "## News [2024/04/15]\n\nWe introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, \nwhich have improved performance on complex chat, multilingual, reasoning and agent. \nNew family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.\n\n- WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works \nand consistently outperforms all the existing state-of-the-art opensource models.\n- WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. \n- WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.\n\nFor more details of WizardLM-2 please read our release blog post and upcoming paper.", "## Model Details\n\n* Model name: WizardLM-2 8x22B\n* Developed by: WizardLM@Microsoft AI\n* Model type: Mixture of Experts (MoE)\n* Base model: mistral-community/Mixtral-8x22B-v0.1\n* Parameters: 141B\n* Language(s): Multilingual\n* Blog: Introducing WizardLM-2\n* Repository: URL\n* Paper: WizardLM-2 (Upcoming)\n* License: Apache2.0", "## Model Capacities\n\n\nMT-Bench\n\nWe also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. \nThe WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. \nMeanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.\n\n<p align=\"center\" width=\"100%\">\n<a ><img src=\"URL/URL alt=\"MTBench\" style=\"width: 96%; min-width: 300px; display: block; margin: auto;\"></a>\n</p>\n\n\nHuman Preferences Evaluation\n\nWe carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. \nWe report the win:loss rate without tie:\n\n- WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314.\n- WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat.\n- WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta.\n\n<p align=\"center\" width=\"100%\">\n<a ><img src=\"URL/URL alt=\"Win\" style=\"width: 96%; min-width: 300px; display: block; margin: auto;\"></a>\n</p>", "## Method Overview\nWe built a fully AI powered synthetic training system to train WizardLM-2 models, please refer to our blog for more details of this system.\n\n<p align=\"center\" width=\"100%\">\n<a ><img src=\"URL/URL alt=\"Method\" style=\"width: 96%; min-width: 300px; display: block; margin: auto;\"></a>\n</p>", "## Usage\n\n<b>Note for model system prompts usage:</b>\n\n\n<b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports multi-turn conversation. The prompt should be as following:\n\n\n\n<b> Inference WizardLM-2 Demo Script</b>\n\nWe provide a WizardLM-2 inference demo code on our github." ]

[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[ "# miqu-evil-dpo", "# Model Details", "## Description\nmiqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a.\n\nIt is trained with evil-tune method applied.\n\n!image/png", "## Prompt template: Mistral Inst", "## Disclaimer\nThe AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use." ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# miqu-evil-dpo", "# Model Details", "## Description\nmiqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a.\n\nIt is trained with evil-tune method applied.\n\n!image/png", "## Prompt template: Mistral Inst", "## Disclaimer\nThe AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use." ]

[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "orpo", "generated_from_trainer", "conversational", "dataset:orpo-explorers/OHP-15k-Stratified-1", "base_model:orpo-explorers/kaist-mistral-orpo-capybara-beta-0.05", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# kaist-mistral-orpo-capybara-ohp-3epoch-beta-0.2\n\nThis model is a fine-tuned version of orpo-explorers/kaist-mistral-orpo-capybara-beta-0.05 on the orpo-explorers/OHP-15k-Stratified-1 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-06\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- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2.post303\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #alignment-handbook #trl #orpo #generated_from_trainer #conversational #dataset-orpo-explorers/OHP-15k-Stratified-1 #base_model-orpo-explorers/kaist-mistral-orpo-capybara-beta-0.05 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# kaist-mistral-orpo-capybara-ohp-3epoch-beta-0.2\n\nThis model is a fine-tuned version of orpo-explorers/kaist-mistral-orpo-capybara-beta-0.05 on the orpo-explorers/OHP-15k-Stratified-1 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-06\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- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2.post303\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: kchopra04\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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 #gguf #llama #text-generation-inference #unsloth #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: kchopra04\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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", "mistral", "text-generation", "mergekit", "merge", "arxiv:2212.04089", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2212.04089" ]

[]

[ "# final_merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using /content/evol_merge_storage/input_models/Mistral-7B-v0.1_8133861 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /content/evol_merge_storage/input_models/Hermes-2-Pro-Mistral-7B_2793206805\n* /content/evol_merge_storage/input_models/Dans-AdventurousWinds-Mk2-7b_1152917843\n* /content/evol_merge_storage/input_models/zephyr-7b-beta_2449712360", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Usage" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2212.04089 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# final_merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using /content/evol_merge_storage/input_models/Mistral-7B-v0.1_8133861 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /content/evol_merge_storage/input_models/Hermes-2-Pro-Mistral-7B_2793206805\n* /content/evol_merge_storage/input_models/Dans-AdventurousWinds-Mk2-7b_1152917843\n* /content/evol_merge_storage/input_models/zephyr-7b-beta_2449712360", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Usage" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-large-patch4-window7-224", "license:apache-2.0", "model-index", "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: 1e-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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "TAGS\n#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-large-patch4-window7-224 #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: 1e-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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:tner/xlm-roberta-base-panx-dataset-ar", "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: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### 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 #xlm-roberta #token-classification #generated_from_trainer #base_model-tner/xlm-roberta-base-panx-dataset-ar #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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### 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", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: TheTsar1209\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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 #conversational #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: TheTsar1209\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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", "base_model:petals-team/falcon-rw-1b", "license:apache-2.0", "region:us" ]

[]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 600", "### Training results", "### Framework versions\n\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 #base_model-petals-team/falcon-rw-1b #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 600", "### Training results", "### Framework versions\n\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" ]

[ "safetensors", "dataset:InnerI/CAI", "license:llama3", "region:us" ]

[]

[]

[ "# CAI Llama3 Consciousness Model", "## Overview:\n\nThe CAI Llama3 Consciousness Model is a specialized large language model designed to understand and respond to complex questions about consciousness. Trained on the \"Consciousness Benchmark Dataset\" containing 10,000 unique questions and responses, this model represents a significant step toward AI systems that can engage in meaningful discussions about consciousness, philosophy, neuroscience, and related fields.", "## Training Data:\n\nThe model is fine-tuned on a dataset focused on consciousness studies, covering a broad spectrum of topics including philosophy, quantum consciousness, neuroscience, and the explanatory gap. The dataset contains detailed responses to questions exploring various aspects of consciousness, providing a rich foundation for training.", "## Architecture:\n\nThis model is based on the Llama3 8b architecture, a state-of-the-art large language model known for its capability to understand and generate human-like text. The fine-tuning process has been tailored to enhance the model's understanding of consciousness-related topics, allowing it to provide insightful and coherent responses to complex questions.", "## Applications:\n\nThe CAI Llama3 Consciousness Model can be applied to various use cases, such as:", "## Research and Education: \n\nSupporting researchers and educators in the field of consciousness studies by offering insights and detailed responses.", "## Conversational AI:\n\nBuilding conversational agents capable of discussing consciousness, philosophy, and related topics.", "## Knowledge-based Systems:\n\nEnhancing AI systems with knowledge in consciousness studies to provide more comprehensive answers and explanations.", "## Performance and Features:\n\nThe model is designed to offer:\n\nHigh-Quality Responses: Capable of generating coherent and relevant responses to a wide range of consciousness-related questions.\n\nContextual Understanding: Able to understand the context and nuances in complex discussions about consciousness.\n\nFlexibility: Suitable for integration into various AI applications, from conversational agents to research tools.", "## Licensing and Attribution:\nBefore using this dataset, ensure compliance with any licensing agreements or usage restrictions. If you share or redistribute the dataset, provide appropriate attribution to the source.", "## Contact Information:\nFor additional information about the dataset or if you have questions, please contact @innerinetco" ]

[ "TAGS\n#safetensors #dataset-InnerI/CAI #license-llama3 #region-us \n", "# CAI Llama3 Consciousness Model", "## Overview:\n\nThe CAI Llama3 Consciousness Model is a specialized large language model designed to understand and respond to complex questions about consciousness. Trained on the \"Consciousness Benchmark Dataset\" containing 10,000 unique questions and responses, this model represents a significant step toward AI systems that can engage in meaningful discussions about consciousness, philosophy, neuroscience, and related fields.", "## Training Data:\n\nThe model is fine-tuned on a dataset focused on consciousness studies, covering a broad spectrum of topics including philosophy, quantum consciousness, neuroscience, and the explanatory gap. The dataset contains detailed responses to questions exploring various aspects of consciousness, providing a rich foundation for training.", "## Architecture:\n\nThis model is based on the Llama3 8b architecture, a state-of-the-art large language model known for its capability to understand and generate human-like text. The fine-tuning process has been tailored to enhance the model's understanding of consciousness-related topics, allowing it to provide insightful and coherent responses to complex questions.", "## Applications:\n\nThe CAI Llama3 Consciousness Model can be applied to various use cases, such as:", "## Research and Education: \n\nSupporting researchers and educators in the field of consciousness studies by offering insights and detailed responses.", "## Conversational AI:\n\nBuilding conversational agents capable of discussing consciousness, philosophy, and related topics.", "## Knowledge-based Systems:\n\nEnhancing AI systems with knowledge in consciousness studies to provide more comprehensive answers and explanations.", "## Performance and Features:\n\nThe model is designed to offer:\n\nHigh-Quality Responses: Capable of generating coherent and relevant responses to a wide range of consciousness-related questions.\n\nContextual Understanding: Able to understand the context and nuances in complex discussions about consciousness.\n\nFlexibility: Suitable for integration into various AI applications, from conversational agents to research tools.", "## Licensing and Attribution:\nBefore using this dataset, ensure compliance with any licensing agreements or usage restrictions. If you share or redistribute the dataset, provide appropriate attribution to the source.", "## Contact Information:\nFor additional information about the dataset or if you have questions, please contact @innerinetco" ]

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

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[ "# distilled-bert-finetuned-squadv2\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.40.1\n- Pytorch 2.1.0\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n", "# distilled-bert-finetuned-squadv2\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.40.1\n- Pytorch 2.1.0\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

[ "1910.09700" ]

[]

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

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

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

[]

[]

[ "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

[ "TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "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-mistralai/Mistral-7B-Instruct-v0.2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.0" ]

[ "gguf", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "base_model:beomi/Llama-3-Open-Ko-8B", "license:other", "region:us" ]

[]

[]

[ "# sosoai/hansoldeco-beomi-llama3-open-ko-8b-64k-test-Q6_K-GGUF\nThis model was converted to GGUF format from 'sosoai/hansoldeco-beomi-llama3-open-ko-8b-64k-test' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "TAGS\n#gguf #generated_from_trainer #llama-cpp #gguf-my-repo #base_model-beomi/Llama-3-Open-Ko-8B #license-other #region-us \n", "# sosoai/hansoldeco-beomi-llama3-open-ko-8b-64k-test-Q6_K-GGUF\nThis model was converted to GGUF format from 'sosoai/hansoldeco-beomi-llama3-open-ko-8b-64k-test' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "transformers", "safetensors", "bloom", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[]

[]

[ "# BigScience - testing model\n\nThis model aims to test the conversion between Megatron-LM and transformers. It is a small -like model that has been used to debug the script. Use it only for integration tests" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# BigScience - testing model\n\nThis model aims to test the conversion between Megatron-LM and transformers. It is a small -like model that has been used to debug the script. Use it only for integration tests" ]

[ "transformers", "safetensors", "bloom", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

[]

[]

[ "# BigScience - testing model\n\nThis model aims to test the conversion between Megatron-LM and transformers. It is a small -like model that has been used to debug the script. Use it only for integration tests" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "# BigScience - testing model\n\nThis model aims to test the conversion between Megatron-LM and transformers. It is a small -like model that has been used to debug the script. Use it only for integration tests" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "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-mistralai/Mistral-7B-Instruct-v0.2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.0" ]

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "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 #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", "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" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "bloom", "text-generation", "arxiv:1909.08053", "arxiv:2110.02861", "arxiv:2108.12409", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "1909.08053", "2110.02861", "2108.12409" ]

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-560m\n=========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary and Calculations\n11. More Information\n12. Model Card Authors\n13. Model Card Contact\n\n\nModel Details\n-------------", "### Model Description\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n* Developed by: BigScience (website)\n\n\n\t+ All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n* Model Type: Transformer-based Language Model\n* Version: 1.0.0\n* Languages: Multiple; see training data\n* License: RAIL License v1.0 (link)\n* Release Date Estimate: Monday, 11.July.2022\n* Funded by:\n\n\n\t+ The French government.\n\t+ Hugging Face (website).\n\t+ Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\nBias, Risks and Limitations\n---------------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs", "### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nThe following table shows the distribution of programming languages.\n\n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\nEnvironmental Impact\n--------------------\n\n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\nTechnical Specifications\n------------------------\n\n\n*This section provides information for people who work on model development.*\n\n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 559,214,592 parameters:\n\n\n\t+ 256,901,120 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1024-dimensional\n\t+ Sequence length of 2048 tokens (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Training throughput: About 150 TFLOPs per GPU\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\nMore Information\n----------------", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n\nModel Card Contact\n------------------\n\n\nSend Questions to: bigscience-contact@URL" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-560m\n=========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary and Calculations\n11. More Information\n12. Model Card Authors\n13. Model Card Contact\n\n\nModel Details\n-------------", "### Model Description\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n* Developed by: BigScience (website)\n\n\n\t+ All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n* Model Type: Transformer-based Language Model\n* Version: 1.0.0\n* Languages: Multiple; see training data\n* License: RAIL License v1.0 (link)\n* Release Date Estimate: Monday, 11.July.2022\n* Funded by:\n\n\n\t+ The French government.\n\t+ Hugging Face (website).\n\t+ Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\nBias, Risks and Limitations\n---------------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs", "### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nThe following table shows the distribution of programming languages.\n\n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\nEnvironmental Impact\n--------------------\n\n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\nTechnical Specifications\n------------------------\n\n\n*This section provides information for people who work on model development.*\n\n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 559,214,592 parameters:\n\n\n\t+ 256,901,120 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1024-dimensional\n\t+ Sequence length of 2048 tokens (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Training throughput: About 150 TFLOPs per GPU\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\nMore Information\n----------------", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n\nModel Card Contact\n------------------\n\n\nSend Questions to: bigscience-contact@URL" ]

[ "transformers", "gguf", "llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "base_model:aaditya/OpenBioLLM-Llama3-8B", "license:llama3", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #llama-3 #llama #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #base_model-aaditya/OpenBioLLM-Llama3-8B #license-llama3 #endpoints_compatible #region-us \n" ]

[ "transformers", "safetensors", "bloom", "text-generation", "arxiv:1909.08053", "arxiv:2110.02861", "arxiv:2108.12409", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

[ "1909.08053", "2110.02861", "2108.12409" ]

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-560m\n=========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary and Calculations\n11. More Information\n12. Model Card Authors\n13. Model Card Contact\n\n\nModel Details\n-------------", "### Model Description\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n* Developed by: BigScience (website)\n\n\n\t+ All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n* Model Type: Transformer-based Language Model\n* Version: 1.0.0\n* Languages: Multiple; see training data\n* License: RAIL License v1.0 (link)\n* Release Date Estimate: Monday, 11.July.2022\n* Funded by:\n\n\n\t+ The French government.\n\t+ Hugging Face (website).\n\t+ Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\nBias, Risks and Limitations\n---------------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs", "### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nThe following table shows the distribution of programming languages.\n\n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\nEnvironmental Impact\n--------------------\n\n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\nTechnical Specifications\n------------------------\n\n\n*This section provides information for people who work on model development.*\n\n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 559,214,592 parameters:\n\n\n\t+ 256,901,120 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1024-dimensional\n\t+ Sequence length of 2048 tokens (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Training throughput: About 150 TFLOPs per GPU\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\nMore Information\n----------------", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n\nModel Card Contact\n------------------\n\n\nSend Questions to: bigscience-contact@URL" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-560m\n=========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary and Calculations\n11. More Information\n12. Model Card Authors\n13. Model Card Contact\n\n\nModel Details\n-------------", "### Model Description\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n* Developed by: BigScience (website)\n\n\n\t+ All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n* Model Type: Transformer-based Language Model\n* Version: 1.0.0\n* Languages: Multiple; see training data\n* License: RAIL License v1.0 (link)\n* Release Date Estimate: Monday, 11.July.2022\n* Funded by:\n\n\n\t+ The French government.\n\t+ Hugging Face (website).\n\t+ Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\nBias, Risks and Limitations\n---------------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs", "### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nThe following table shows the distribution of programming languages.\n\n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\nEnvironmental Impact\n--------------------\n\n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\nTechnical Specifications\n------------------------\n\n\n*This section provides information for people who work on model development.*\n\n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 559,214,592 parameters:\n\n\n\t+ 256,901,120 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1024-dimensional\n\t+ Sequence length of 2048 tokens (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Training throughput: About 150 TFLOPs per GPU\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\nMore Information\n----------------", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n\nModel Card Contact\n------------------\n\n\nSend Questions to: bigscience-contact@URL" ]

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:vaatsav06/Llama3_medqa_final", "base_model:o2satz/L3_med16", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

[]

[]

[ "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* vaatsav06/Llama3_medqa_final\n* o2satz/L3_med16", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #base_model-vaatsav06/Llama3_medqa_final #base_model-o2satz/L3_med16 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* vaatsav06/Llama3_medqa_final\n* o2satz/L3_med16", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "gguf", "arxiv:1909.08053", "arxiv:2110.02861", "arxiv:2108.12409", "region:us" ]

[ "1909.08053", "2110.02861", "2108.12409" ]

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-560m\n=========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary and Calculations\n11. More Information\n12. Model Card Authors\n13. Model Card Contact\n\n\nModel Details\n-------------", "### Model Description\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n* Developed by: BigScience (website)\n\n\n\t+ All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n* Model Type: Transformer-based Language Model\n* Version: 1.0.0\n* Languages: Multiple; see training data\n* License: RAIL License v1.0 (link)\n* Release Date Estimate: Monday, 11.July.2022\n* Funded by:\n\n\n\t+ The French government.\n\t+ Hugging Face (website).\n\t+ Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\nBias, Risks and Limitations\n---------------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs", "### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nThe following table shows the distribution of programming languages.\n\n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\nEnvironmental Impact\n--------------------\n\n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\nTechnical Specifications\n------------------------\n\n\n*This section provides information for people who work on model development.*\n\n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 559,214,592 parameters:\n\n\n\t+ 256,901,120 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1024-dimensional\n\t+ Sequence length of 2048 tokens (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Training throughput: About 150 TFLOPs per GPU\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\nMore Information\n----------------", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n\nModel Card Contact\n------------------\n\n\nSend Questions to: bigscience-contact@URL" ]

[ "TAGS\n#gguf #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-560m\n=========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary and Calculations\n11. More Information\n12. Model Card Authors\n13. Model Card Contact\n\n\nModel Details\n-------------", "### Model Description\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n* Developed by: BigScience (website)\n\n\n\t+ All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n* Model Type: Transformer-based Language Model\n* Version: 1.0.0\n* Languages: Multiple; see training data\n* License: RAIL License v1.0 (link)\n* Release Date Estimate: Monday, 11.July.2022\n* Funded by:\n\n\n\t+ The French government.\n\t+ Hugging Face (website).\n\t+ Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\nBias, Risks and Limitations\n---------------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs", "### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nThe following table shows the distribution of programming languages.\n\n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\nEnvironmental Impact\n--------------------\n\n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\nTechnical Specifications\n------------------------\n\n\n*This section provides information for people who work on model development.*\n\n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 559,214,592 parameters:\n\n\n\t+ 256,901,120 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1024-dimensional\n\t+ Sequence length of 2048 tokens (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Training throughput: About 150 TFLOPs per GPU\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\nMore Information\n----------------", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n\nModel Card Contact\n------------------\n\n\nSend Questions to: bigscience-contact@URL" ]

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "dataset:autotrain-produto-google-bert-base-uncased-9099/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# Model Trained Using AutoTrain\n\n- Problem type: Text Classification", "## Validation Metrics\nloss: 1.0682125091552734\n\nf1_macro: 0.17334740509080254\n\nf1_micro: 0.677115987460815\n\nf1_weighted: 0.6445509958774286\n\nprecision_macro: 0.20939709069152226\n\nprecision_micro: 0.677115987460815\n\nprecision_weighted: 0.6403148729034246\n\nrecall_macro: 0.17545960483239412\n\nrecall_micro: 0.677115987460815\n\nrecall_weighted: 0.677115987460815\n\naccuracy: 0.677115987460815" ]

[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #autotrain #dataset-autotrain-produto-google-bert-base-uncased-9099/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\n- Problem type: Text Classification", "## Validation Metrics\nloss: 1.0682125091552734\n\nf1_macro: 0.17334740509080254\n\nf1_micro: 0.677115987460815\n\nf1_weighted: 0.6445509958774286\n\nprecision_macro: 0.20939709069152226\n\nprecision_micro: 0.677115987460815\n\nprecision_weighted: 0.6403148729034246\n\nrecall_macro: 0.17545960483239412\n\nrecall_micro: 0.677115987460815\n\nrecall_weighted: 0.677115987460815\n\naccuracy: 0.677115987460815" ]

[ "transformers", "wav2vec2-ebranchformer", "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 #wav2vec2-ebranchformer #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", "llama", "text-generation", "tenyx-fine-tuning", "dpo", "tenyxchat", "llama3", "conversational", "en", "dataset:HuggingFaceH4/ultrafeedback_binarized", "arxiv:2305.18290", "arxiv:2306.05685", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "has_space" ]

[ "2305.18290", "2306.05685" ]

[ "en" ]

[ "### Hugging face Example\n\n\nPerformance\n===========\n\n\nAt the time of release (April 2024), Llama3-TenyxChat-70B is the highest-ranked open source model on the MT-Bench evaluation available for download.\n\n\nMT-Bench\n--------\n\n\nMT-Bench is a benchmark made up of 80 high-quality multi-turn questions. These questions fall into eight categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, and Humanities. The chat models are rated using 'GPT-4-preview-0125' on a scale of 1 to 10, with higher values corresponding to better responses.\n\n\nModel-name: GPT-4-1106, GPT4-preview-0125 MT Bench: 8.79, Chat Arena Elo: 1251\nModel-name: Claude 3 Opus (20240229), GPT4-preview-0125 MT Bench: 8.57, Chat Arena Elo: 1247\nModel-name: Llama3-TenyxChat-70B, GPT4-preview-0125 MT Bench: 8.15, Chat Arena Elo: NA\nModel-name: *Llama3-70B-Instruct*, GPT4-preview-0125 MT Bench: 7.96, Chat Arena Elo: 1207\nModel-name: Claude 3 Sonnet (20240229), GPT4-preview-0125 MT Bench: 7.82, Chat Arena Elo: 1190\nModel-name: GPT-4-0314, GPT4-preview-0125 MT Bench: 7.96, Chat Arena Elo: 1185\nModel-name: Mixtral, GPT4-preview-0125 MT Bench: 7.38, Chat Arena Elo: 1114\nModel-name: gpt-3.5-turbo-0613, GPT4-preview-0125 MT Bench: 7.37, Chat Arena Elo: 1113\nModel-name: Yi-34B, GPT4-preview-0125 MT Bench: 6.46, Chat Arena Elo: 1099\nModel-name: gpt-3.5-turbo-0125, GPT4-preview-0125 MT Bench: 7.52, Chat Arena Elo: 1096\nModel-name: Llama 2 70B, GPT4-preview-0125 MT Bench: 6.01, Chat Arena Elo: 1082\nModel-name: NV-Llama2-70B-SteerLM-Chat, GPT4-preview-0125 MT Bench: 6.57, Chat Arena Elo: 1076\n\n\n!URL\n\n\nArena Hard\n----------\n\n\nArena-Hard is an evaluation tool for instruction-tuned LLMs containing 500 challenging user queries. They prompt GPT-4-1106-preview as judge to compare the models' responses against a baseline model (default: GPT-4-0314).\n\n\nModel-name: gpt-4-0125-preview, Score: 78.0\nModel-name: claude-3-opus-20240229, Score: 60.4\nModel-name: gpt-4-0314, Score: 50.0\nModel-name: tenyx/Llama3-TenyxChat-70B, Score: 49.0\nModel-name: *meta-llama/Meta-Llama-3-70B-In*, Score: 47.3\nModel-name: claude-3-sonnet-20240229, Score: 46.8\nModel-name: claude-3-haiku-20240307, Score: 41.5\nModel-name: gpt-4-0613, Score: 37.9\nModel-name: mistral-large-2402, Score: 37.7\nModel-name: Qwen1.5-72B-Chat, Score: 36.1\nModel-name: command-r-plus, Score: 33.1\n\n\nOpen LLM Leaderboard Evaluation\n-------------------------------\n\n\nWe now present our results on the Eleuther AI Language Model Evaluation Harness used for benchmarking Open LLM Leaderboard on Hugging Face.\nThe task involves evaluation on '6' key benchmarks across reasoning and knowledge with different *few-shot* settings. Read more details about the benchmark at the leaderboard page.\n\n\n\n\\*The results reported are from local evaluation of our model. 'tenyx/Llama3-TenyxChat-70B' is submitted and will be reflected in the leaderboard once evaluation succeeds.\n\n\nLimitations\n===========\n\n\nLlama3-TenyxChat-70B, like other language models, has its own set of limitations. We haven’t fine-tuned the model explicitly to align with human safety preferences. Therefore, it is capable of producing undesirable outputs, particularly when adversarially prompted. From our observation, the model still tends to struggle with tasks that involve reasoning and math questions. In some instances, it might generate verbose or extraneous content.\n\n\nLicense\n=======\n\n\nLlama3-TenyxChat-70B is distributed under the Meta Llama 3 Community License.\n\n\nIf you use Llama3-TenyxChat-70B for your research, cite us as" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #tenyx-fine-tuning #dpo #tenyxchat #llama3 #conversational #en #dataset-HuggingFaceH4/ultrafeedback_binarized #arxiv-2305.18290 #arxiv-2306.05685 #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n", "### Hugging face Example\n\n\nPerformance\n===========\n\n\nAt the time of release (April 2024), Llama3-TenyxChat-70B is the highest-ranked open source model on the MT-Bench evaluation available for download.\n\n\nMT-Bench\n--------\n\n\nMT-Bench is a benchmark made up of 80 high-quality multi-turn questions. These questions fall into eight categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, and Humanities. The chat models are rated using 'GPT-4-preview-0125' on a scale of 1 to 10, with higher values corresponding to better responses.\n\n\nModel-name: GPT-4-1106, GPT4-preview-0125 MT Bench: 8.79, Chat Arena Elo: 1251\nModel-name: Claude 3 Opus (20240229), GPT4-preview-0125 MT Bench: 8.57, Chat Arena Elo: 1247\nModel-name: Llama3-TenyxChat-70B, GPT4-preview-0125 MT Bench: 8.15, Chat Arena Elo: NA\nModel-name: *Llama3-70B-Instruct*, GPT4-preview-0125 MT Bench: 7.96, Chat Arena Elo: 1207\nModel-name: Claude 3 Sonnet (20240229), GPT4-preview-0125 MT Bench: 7.82, Chat Arena Elo: 1190\nModel-name: GPT-4-0314, GPT4-preview-0125 MT Bench: 7.96, Chat Arena Elo: 1185\nModel-name: Mixtral, GPT4-preview-0125 MT Bench: 7.38, Chat Arena Elo: 1114\nModel-name: gpt-3.5-turbo-0613, GPT4-preview-0125 MT Bench: 7.37, Chat Arena Elo: 1113\nModel-name: Yi-34B, GPT4-preview-0125 MT Bench: 6.46, Chat Arena Elo: 1099\nModel-name: gpt-3.5-turbo-0125, GPT4-preview-0125 MT Bench: 7.52, Chat Arena Elo: 1096\nModel-name: Llama 2 70B, GPT4-preview-0125 MT Bench: 6.01, Chat Arena Elo: 1082\nModel-name: NV-Llama2-70B-SteerLM-Chat, GPT4-preview-0125 MT Bench: 6.57, Chat Arena Elo: 1076\n\n\n!URL\n\n\nArena Hard\n----------\n\n\nArena-Hard is an evaluation tool for instruction-tuned LLMs containing 500 challenging user queries. They prompt GPT-4-1106-preview as judge to compare the models' responses against a baseline model (default: GPT-4-0314).\n\n\nModel-name: gpt-4-0125-preview, Score: 78.0\nModel-name: claude-3-opus-20240229, Score: 60.4\nModel-name: gpt-4-0314, Score: 50.0\nModel-name: tenyx/Llama3-TenyxChat-70B, Score: 49.0\nModel-name: *meta-llama/Meta-Llama-3-70B-In*, Score: 47.3\nModel-name: claude-3-sonnet-20240229, Score: 46.8\nModel-name: claude-3-haiku-20240307, Score: 41.5\nModel-name: gpt-4-0613, Score: 37.9\nModel-name: mistral-large-2402, Score: 37.7\nModel-name: Qwen1.5-72B-Chat, Score: 36.1\nModel-name: command-r-plus, Score: 33.1\n\n\nOpen LLM Leaderboard Evaluation\n-------------------------------\n\n\nWe now present our results on the Eleuther AI Language Model Evaluation Harness used for benchmarking Open LLM Leaderboard on Hugging Face.\nThe task involves evaluation on '6' key benchmarks across reasoning and knowledge with different *few-shot* settings. Read more details about the benchmark at the leaderboard page.\n\n\n\n\\*The results reported are from local evaluation of our model. 'tenyx/Llama3-TenyxChat-70B' is submitted and will be reflected in the leaderboard once evaluation succeeds.\n\n\nLimitations\n===========\n\n\nLlama3-TenyxChat-70B, like other language models, has its own set of limitations. We haven’t fine-tuned the model explicitly to align with human safety preferences. Therefore, it is capable of producing undesirable outputs, particularly when adversarially prompted. From our observation, the model still tends to struggle with tasks that involve reasoning and math questions. In some instances, it might generate verbose or extraneous content.\n\n\nLicense\n=======\n\n\nLlama3-TenyxChat-70B is distributed under the Meta Llama 3 Community License.\n\n\nIf you use Llama3-TenyxChat-70B for your research, cite us as" ]

[ "transformers", "safetensors", "llama", "text-generation", "mlx", "conversational", "en", "ja", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en", "ja" ]

[ "# mlx-community/Swallow-70b-instruct-v0.1-4bit\nThis model was converted to MLX format from ['tokyotech-llm/Swallow-70b-instruct-v0.1']() using mlx-lm version 0.6.0.\nRefer to the original model card for more details on the model.", "## Use with mlx" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mlx #conversational #en #ja #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# mlx-community/Swallow-70b-instruct-v0.1-4bit\nThis model was converted to MLX format from ['tokyotech-llm/Swallow-70b-instruct-v0.1']() using mlx-lm version 0.6.0.\nRefer to the original model card for more details on the model.", "## Use with mlx" ]

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[]

[ "# CS505_COQE_viT5_train_Instruction0_OSAPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\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 #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_OSAPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "4-bit", "region:us" ]

[]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.40.1\n* Pytorch 2.1.2\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#peft #tensorboard #safetensors #mistral #alignment-handbook #trl #dpo #generated_from_trainer #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-mistralai/Mistral-7B-v0.1 #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: 5e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.40.1\n* Pytorch 2.1.2\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "gemma", "text-generation", "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 #gemma #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# CS505_COQE_viT5_train_Instruction0_PSOAL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\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 #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_PSOAL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "Sao10K/L3-Solana-8B-v1", "Kaoeiri/Keiana-L3-Test4.7-8B-3", "conversational", "base_model:Sao10K/L3-Solana-8B-v1", "base_model:Kaoeiri/Keiana-L3-Test4.7-8B-3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Keiana-L3-Test6-8B-16\n\nKeiana-L3-Test6-8B-16 is a merge of the following models using LazyMergekit:", "# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.\n* Sao10K/L3-Solana-8B-v1\n* Kaoeiri/Keiana-L3-Test4.7-8B-3", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #Sao10K/L3-Solana-8B-v1 #Kaoeiri/Keiana-L3-Test4.7-8B-3 #conversational #base_model-Sao10K/L3-Solana-8B-v1 #base_model-Kaoeiri/Keiana-L3-Test4.7-8B-3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Keiana-L3-Test6-8B-16\n\nKeiana-L3-Test6-8B-16 is a merge of the following models using LazyMergekit:", "# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.\n* Sao10K/L3-Solana-8B-v1\n* Kaoeiri/Keiana-L3-Test4.7-8B-3", "## Configuration", "## Usage" ]

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:cognitivecomputations/dolphin-2.9-llama3-8b", "base_model:o2satz/L3_med16QA", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.9-llama3-8b\n* o2satz/L3_med16QA", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #base_model-cognitivecomputations/dolphin-2.9-llama3-8b #base_model-o2satz/L3_med16QA #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.9-llama3-8b\n* o2satz/L3_med16QA", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

[]

[]

[ "# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: nvasko/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n", "# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: nvasko/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

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[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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\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 #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: mahiatlinux\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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 #pytorch #llama #text-generation #text-generation-inference #unsloth #trl #sft #conversational #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: mahiatlinux\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "gguf", "llama3", "ollama", "dolphin", "base_model:cognitivecomputations/dolphin-2.9-llama3-70b", "license:llama3", "region:us" ]

[]

[]

[]

[ "TAGS\n#gguf #llama3 #ollama #dolphin #base_model-cognitivecomputations/dolphin-2.9-llama3-70b #license-llama3 #region-us \n" ]

[ "transformers", "safetensors", "bloom", "text-generation", "arxiv:1909.08053", "arxiv:2110.02861", "arxiv:2108.12409", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "1909.08053", "2110.02861", "2108.12409" ]

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 1,065,314,304 parameters:\n\n\n\t+ 385,351,680 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1536-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.7\n* Validation Loss: 3.1\n* Perplexity: 21.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 1,065,314,304 parameters:\n\n\n\t+ 385,351,680 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1536-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.7\n* Validation Loss: 3.1\n* Perplexity: 21.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "transformers", "safetensors", "bloom", "text-generation", "arxiv:1909.08053", "arxiv:2110.02861", "arxiv:2108.12409", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

[ "1909.08053", "2110.02861", "2108.12409" ]

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 1,065,314,304 parameters:\n\n\n\t+ 385,351,680 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1536-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.7\n* Validation Loss: 3.1\n* Perplexity: 21.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 1,065,314,304 parameters:\n\n\n\t+ 385,351,680 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1536-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.7\n* Validation Loss: 3.1\n* Perplexity: 21.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "gguf", "arxiv:1909.08053", "arxiv:2110.02861", "arxiv:2108.12409", "region:us" ]

[ "1909.08053", "2110.02861", "2108.12409" ]

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 1,065,314,304 parameters:\n\n\n\t+ 385,351,680 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1536-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.7\n* Validation Loss: 3.1\n* Perplexity: 21.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "TAGS\n#gguf #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 1,065,314,304 parameters:\n\n\n\t+ 385,351,680 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 1536-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.7\n* Validation Loss: 3.1\n* Perplexity: 21.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "transformers", "safetensors", "bloom", "text-generation", "arxiv:1909.08053", "arxiv:2110.02861", "arxiv:2108.12409", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "1909.08053", "2110.02861", "2108.12409" ]

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-1b7\n========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary and Calculations\n11. More Information\n12. Model Card Authors\n13. Model Card Contact\n\n\nModel Details\n-------------", "### Model Description\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n* Developed by: BigScience (website)\n\n\n\t+ All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n* Model Type: Transformer-based Language Model\n* Version: 1.0.0\n* Languages: Multiple; see training data\n* License: RAIL License v1.0 (link)\n* Release Date Estimate: Monday, 11.July.2022\n* Funded by:\n\n\n\t+ The French government.\n\t+ Hugging Face (website).\n\t+ Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs", "### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\n\nThe following table shows the distribution of programming languages.\n\n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\n* BLOOM Book: Read generations from BLOOM based on prompts provided by the community\n\n\nEnvironmental Impact\n--------------------\n\n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\nTechnical Specifications\n------------------------\n\n\n*This section provides information for people who work on model development.*\n\n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 1,722,408,960 parameters:\n\n\n\t+ 513,802,240 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 2048-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 64 V100 16/32GB GPUs (16 nodes):\n\n\n\t+ 4 GPUs per node\n\t+ 40 CPUs per task\n\t+ 1 task per node\n\t+ CPU: AMD\n\t+ CPU memory: 160GB per node\n\t+ GPU memory: 64GB or 128GB (depending on node availability during training) per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "### Training\n\n\n* Checkpoint size:\n\n\n\t+ Fp16 weights: 2.6GB (# params \\* 2)\n\t+ Full checkpoint with optimizer states: --\n* Training throughput: --\n* Number of epochs: 1\n* Dates:\n\n\n\t+ Start: 11th March, 2022 11:42am PST\n\t+ End: 20 May, 2022\n* Server training location: Île-de-France, France", "### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\nMore Information\n----------------", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n\nModel Card Contact\n------------------\n\n\nSend Questions to: bigscience-contact@URL" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-1b7\n========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary and Calculations\n11. More Information\n12. Model Card Authors\n13. Model Card Contact\n\n\nModel Details\n-------------", "### Model Description\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n* Developed by: BigScience (website)\n\n\n\t+ All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n* Model Type: Transformer-based Language Model\n* Version: 1.0.0\n* Languages: Multiple; see training data\n* License: RAIL License v1.0 (link)\n* Release Date Estimate: Monday, 11.July.2022\n* Funded by:\n\n\n\t+ The French government.\n\t+ Hugging Face (website).\n\t+ Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs", "### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\n\nThe following table shows the distribution of programming languages.\n\n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\n* BLOOM Book: Read generations from BLOOM based on prompts provided by the community\n\n\nEnvironmental Impact\n--------------------\n\n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\nTechnical Specifications\n------------------------\n\n\n*This section provides information for people who work on model development.*\n\n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 1,722,408,960 parameters:\n\n\n\t+ 513,802,240 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 2048-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 64 V100 16/32GB GPUs (16 nodes):\n\n\n\t+ 4 GPUs per node\n\t+ 40 CPUs per task\n\t+ 1 task per node\n\t+ CPU: AMD\n\t+ CPU memory: 160GB per node\n\t+ GPU memory: 64GB or 128GB (depending on node availability during training) per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "### Training\n\n\n* Checkpoint size:\n\n\n\t+ Fp16 weights: 2.6GB (# params \\* 2)\n\t+ Full checkpoint with optimizer states: --\n* Training throughput: --\n* Number of epochs: 1\n* Dates:\n\n\n\t+ Start: 11th March, 2022 11:42am PST\n\t+ End: 20 May, 2022\n* Server training location: Île-de-France, France", "### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\nMore Information\n----------------", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n\nModel Card Contact\n------------------\n\n\nSend Questions to: bigscience-contact@URL" ]

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

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[ "# CS505_COQE_viT5_train_Instruction0_ASOPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\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 #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_ASOPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]