Datasets:

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ftopal

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huggingface-models

like 0

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

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[ "TAGS\n#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n" ]

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

[]

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[ "TAGS\n#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n" ]

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

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[]

[ "TAGS\n#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n" ]

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

[]

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[]

[ "TAGS\n#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n" ]

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

[]

[]

[]

[ "TAGS\n#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n" ]

[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:2304.01373", "arxiv:2101.00027", "arxiv:2201.07311", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "2304.01373", "2101.00027", "2201.07311" ]

[]

[ "### Intended Use\n\n\nThe primary intended use of Pythia is research on the behavior, functionality,\nand limitations of large language models. This suite is intended to provide\na controlled setting for performing scientific experiments. We also provide\n154 checkpoints per model: initial 'step0', 10 log-spaced checkpoints\n'step{1,2,4...512}', and 143 evenly-spaced checkpoints from 'step1000' to\n'step143000'. These checkpoints are hosted on Hugging Face as branches. Note\nthat branch '143000' corresponds exactly to the model checkpoint on the 'main'\nbranch of each model.\n\n\nYou may also further fine-tune and adapt Pythia-1B for deployment,\nas long as your use is in accordance with the Apache 2.0 license. Pythia\nmodels work with the Hugging Face Transformers\nLibrary. If you decide to use\npre-trained Pythia-1B as a basis for your fine-tuned model, please\nconduct your own risk and bias assessment.", "### Out-of-scope use\n\n\nThe Pythia Suite is not intended for deployment. It is not a in itself\na product and cannot be used for human-facing interactions. For example,\nthe model may generate harmful or offensive text. Please evaluate the risks\nassociated with your particular use case.\n\n\nPythia models are English-language only, and are not suitable for translation\nor generating text in other languages.\n\n\nPythia-1B has not been fine-tuned for downstream contexts in which\nlanguage models are commonly deployed, such as writing genre prose,\nor commercial chatbots. This means Pythia-1B will not\nrespond to a given prompt the way a product like ChatGPT does. This is because,\nunlike this model, ChatGPT was fine-tuned using methods such as Reinforcement\nLearning from Human Feedback (RLHF) to better “follow” human instructions.", "### Limitations and biases\n\n\nThe core functionality of a large language model is to take a string of text\nand predict the next token. The token used by the model need not produce the\nmost “accurate” text. Never rely on Pythia-1B to produce factually accurate\noutput.\n\n\nThis model was trained on the Pile, a dataset\nknown to contain profanity and texts that are lewd or otherwise offensive.\nSee Section 6 of the Pile paper for a\ndiscussion of documented biases with regards to gender, religion, and race.\nPythia-1B may produce socially unacceptable or undesirable text, *even if*\nthe prompt itself does not include anything explicitly offensive.\n\n\nIf you plan on using text generated through, for example, the Hosted Inference\nAPI, we recommend having a human curate the outputs of this language model\nbefore presenting it to other people. Please inform your audience that the\ntext was generated by Pythia-1B.", "### Quickstart\n\n\nPythia models can be loaded and used via the following code, demonstrated here\nfor the third 'pythia-70m-deduped' checkpoint:\n\n\nRevision/branch 'step143000' corresponds exactly to the model checkpoint on\nthe 'main' branch of each model. \n\nFor more information on how to use all Pythia models, see documentation on\nGitHub.\n\n\nTraining\n--------", "### Training data\n\n\nThe Pile is a 825GiB general-purpose dataset in\nEnglish. It was created by EleutherAI specifically for training large language\nmodels. It contains texts from 22 diverse sources, roughly broken down into\nfive categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),\nprose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and\nmiscellaneous (e.g. GitHub, Enron Emails). See the Pile\npaper for a breakdown of all data sources,\nmethodology, and a discussion of ethical implications. Consult the\ndatasheet for more detailed documentation\nabout the Pile and its component datasets. The Pile can be downloaded from\nthe official website, or from a community\nmirror. \n\nThe Pile was not deduplicated before being used to train Pythia-1B.", "### Training procedure\n\n\nAll models were trained on the exact same data, in the exact same order. Each\nmodel saw 299,892,736,000 tokens during training, and 143 checkpoints for each\nmodel are saved every 2,097,152,000 tokens, spaced evenly throughout training,\nfrom 'step1000' to 'step143000' (which is the same as 'main'). In addition, we\nalso provide frequent early checkpoints: 'step0' and 'step{1,2,4...512}'.\nThis corresponds to training for just under 1 epoch on the Pile for\nnon-deduplicated models, and about 1.5 epochs on the deduplicated Pile.\n\n\nAll *Pythia* models trained for 143000 steps at a batch size\nof 2M (2,097,152 tokens). \n\nSee GitHub for more details on training\nprocedure, including how to reproduce\nit. \n\nPythia uses the same tokenizer as GPT-NeoX-\n20B.\n\n\nEvaluations\n-----------\n\n\nAll 16 *Pythia* models were evaluated using the LM Evaluation\nHarness. You can access\nthe results by model and step at 'results/json/\\*' in the GitHub\nrepository. \n\nExpand the sections below to see plots of evaluation results for all\nPythia and Pythia-deduped models compared with OPT and BLOOM.\n\n\n\nLAMBADA – OpenAI\n\n\n\nPhysical Interaction: Question Answering (PIQA)\n\n\n\nWinoGrande\n\n\n\nAI2 Reasoning Challenge—Easy Set\n\n\n\nSciQ\n\n\nChangelog\n---------\n\n\nThis section compares differences between previously released\nPythia v0 and the current\nmodels. See Appendix B of the Pythia paper for further discussion of these\nchanges and the motivation behind them. We found that retraining Pythia had no\nimpact on benchmark performance.\n\n\n* All model sizes are now trained with uniform batch size of 2M tokens.\nPreviously, the models of size 160M, 410M, and 1.4B parameters were trained\nwith batch sizes of 4M tokens.\n* We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,\n128,256,512} in addition to every 1000 training steps.\n* Flash Attention was used in the new retrained suite.\n* We remedied a minor inconsistency that existed in the original suite: all\nmodels of size 2.8B parameters or smaller had a learning rate (LR) schedule\nwhich decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and\n12B models all used an LR schedule which decayed to a minimum LR of 0. In\nthe redone training runs, we rectified this inconsistency: all models now were\ntrained with LR decaying to a minimum of 0.1× their maximum LR.", "### Naming convention and parameter count\n\n\n*Pythia* models were renamed in January 2023. It is possible that the old\nnaming convention still persists in some documentation by accident. The\ncurrent naming convention (70M, 160M, etc.) is based on total parameter count." ]

[ "TAGS\n#transformers #safetensors #gpt_neox #text-generation #arxiv-2304.01373 #arxiv-2101.00027 #arxiv-2201.07311 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Intended Use\n\n\nThe primary intended use of Pythia is research on the behavior, functionality,\nand limitations of large language models. This suite is intended to provide\na controlled setting for performing scientific experiments. We also provide\n154 checkpoints per model: initial 'step0', 10 log-spaced checkpoints\n'step{1,2,4...512}', and 143 evenly-spaced checkpoints from 'step1000' to\n'step143000'. These checkpoints are hosted on Hugging Face as branches. Note\nthat branch '143000' corresponds exactly to the model checkpoint on the 'main'\nbranch of each model.\n\n\nYou may also further fine-tune and adapt Pythia-1B for deployment,\nas long as your use is in accordance with the Apache 2.0 license. Pythia\nmodels work with the Hugging Face Transformers\nLibrary. If you decide to use\npre-trained Pythia-1B as a basis for your fine-tuned model, please\nconduct your own risk and bias assessment.", "### Out-of-scope use\n\n\nThe Pythia Suite is not intended for deployment. It is not a in itself\na product and cannot be used for human-facing interactions. For example,\nthe model may generate harmful or offensive text. Please evaluate the risks\nassociated with your particular use case.\n\n\nPythia models are English-language only, and are not suitable for translation\nor generating text in other languages.\n\n\nPythia-1B has not been fine-tuned for downstream contexts in which\nlanguage models are commonly deployed, such as writing genre prose,\nor commercial chatbots. This means Pythia-1B will not\nrespond to a given prompt the way a product like ChatGPT does. This is because,\nunlike this model, ChatGPT was fine-tuned using methods such as Reinforcement\nLearning from Human Feedback (RLHF) to better “follow” human instructions.", "### Limitations and biases\n\n\nThe core functionality of a large language model is to take a string of text\nand predict the next token. The token used by the model need not produce the\nmost “accurate” text. Never rely on Pythia-1B to produce factually accurate\noutput.\n\n\nThis model was trained on the Pile, a dataset\nknown to contain profanity and texts that are lewd or otherwise offensive.\nSee Section 6 of the Pile paper for a\ndiscussion of documented biases with regards to gender, religion, and race.\nPythia-1B may produce socially unacceptable or undesirable text, *even if*\nthe prompt itself does not include anything explicitly offensive.\n\n\nIf you plan on using text generated through, for example, the Hosted Inference\nAPI, we recommend having a human curate the outputs of this language model\nbefore presenting it to other people. Please inform your audience that the\ntext was generated by Pythia-1B.", "### Quickstart\n\n\nPythia models can be loaded and used via the following code, demonstrated here\nfor the third 'pythia-70m-deduped' checkpoint:\n\n\nRevision/branch 'step143000' corresponds exactly to the model checkpoint on\nthe 'main' branch of each model. \n\nFor more information on how to use all Pythia models, see documentation on\nGitHub.\n\n\nTraining\n--------", "### Training data\n\n\nThe Pile is a 825GiB general-purpose dataset in\nEnglish. It was created by EleutherAI specifically for training large language\nmodels. It contains texts from 22 diverse sources, roughly broken down into\nfive categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),\nprose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and\nmiscellaneous (e.g. GitHub, Enron Emails). See the Pile\npaper for a breakdown of all data sources,\nmethodology, and a discussion of ethical implications. Consult the\ndatasheet for more detailed documentation\nabout the Pile and its component datasets. The Pile can be downloaded from\nthe official website, or from a community\nmirror. \n\nThe Pile was not deduplicated before being used to train Pythia-1B.", "### Training procedure\n\n\nAll models were trained on the exact same data, in the exact same order. Each\nmodel saw 299,892,736,000 tokens during training, and 143 checkpoints for each\nmodel are saved every 2,097,152,000 tokens, spaced evenly throughout training,\nfrom 'step1000' to 'step143000' (which is the same as 'main'). In addition, we\nalso provide frequent early checkpoints: 'step0' and 'step{1,2,4...512}'.\nThis corresponds to training for just under 1 epoch on the Pile for\nnon-deduplicated models, and about 1.5 epochs on the deduplicated Pile.\n\n\nAll *Pythia* models trained for 143000 steps at a batch size\nof 2M (2,097,152 tokens). \n\nSee GitHub for more details on training\nprocedure, including how to reproduce\nit. \n\nPythia uses the same tokenizer as GPT-NeoX-\n20B.\n\n\nEvaluations\n-----------\n\n\nAll 16 *Pythia* models were evaluated using the LM Evaluation\nHarness. You can access\nthe results by model and step at 'results/json/\\*' in the GitHub\nrepository. \n\nExpand the sections below to see plots of evaluation results for all\nPythia and Pythia-deduped models compared with OPT and BLOOM.\n\n\n\nLAMBADA – OpenAI\n\n\n\nPhysical Interaction: Question Answering (PIQA)\n\n\n\nWinoGrande\n\n\n\nAI2 Reasoning Challenge—Easy Set\n\n\n\nSciQ\n\n\nChangelog\n---------\n\n\nThis section compares differences between previously released\nPythia v0 and the current\nmodels. See Appendix B of the Pythia paper for further discussion of these\nchanges and the motivation behind them. We found that retraining Pythia had no\nimpact on benchmark performance.\n\n\n* All model sizes are now trained with uniform batch size of 2M tokens.\nPreviously, the models of size 160M, 410M, and 1.4B parameters were trained\nwith batch sizes of 4M tokens.\n* We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,\n128,256,512} in addition to every 1000 training steps.\n* Flash Attention was used in the new retrained suite.\n* We remedied a minor inconsistency that existed in the original suite: all\nmodels of size 2.8B parameters or smaller had a learning rate (LR) schedule\nwhich decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and\n12B models all used an LR schedule which decayed to a minimum LR of 0. In\nthe redone training runs, we rectified this inconsistency: all models now were\ntrained with LR decaying to a minimum of 0.1× their maximum LR.", "### Naming convention and parameter count\n\n\n*Pythia* models were renamed in January 2023. It is possible that the old\nnaming convention still persists in some documentation by accident. The\ncurrent naming convention (70M, 160M, etc.) is based on total parameter count." ]

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

[]

[]

[]

[ "TAGS\n#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n" ]

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

[]

[]

[]

[ "TAGS\n#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n" ]

[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "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 0.10.1.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-meta-llama/Meta-Llama-3-8B-Instruct #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.1.dev0" ]

[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-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: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[ "1910.09700" ]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:2304.01373", "arxiv:2101.00027", "arxiv:2201.07311", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

[ "2304.01373", "2101.00027", "2201.07311" ]

[]

[ "### Intended Use\n\n\nThe primary intended use of Pythia is research on the behavior, functionality,\nand limitations of large language models. This suite is intended to provide\na controlled setting for performing scientific experiments. We also provide\n154 checkpoints per model: initial 'step0', 10 log-spaced checkpoints\n'step{1,2,4...512}', and 143 evenly-spaced checkpoints from 'step1000' to\n'step143000'. These checkpoints are hosted on Hugging Face as branches. Note\nthat branch '143000' corresponds exactly to the model checkpoint on the 'main'\nbranch of each model.\n\n\nYou may also further fine-tune and adapt Pythia-1B for deployment,\nas long as your use is in accordance with the Apache 2.0 license. Pythia\nmodels work with the Hugging Face Transformers\nLibrary. If you decide to use\npre-trained Pythia-1B as a basis for your fine-tuned model, please\nconduct your own risk and bias assessment.", "### Out-of-scope use\n\n\nThe Pythia Suite is not intended for deployment. It is not a in itself\na product and cannot be used for human-facing interactions. For example,\nthe model may generate harmful or offensive text. Please evaluate the risks\nassociated with your particular use case.\n\n\nPythia models are English-language only, and are not suitable for translation\nor generating text in other languages.\n\n\nPythia-1B has not been fine-tuned for downstream contexts in which\nlanguage models are commonly deployed, such as writing genre prose,\nor commercial chatbots. This means Pythia-1B will not\nrespond to a given prompt the way a product like ChatGPT does. This is because,\nunlike this model, ChatGPT was fine-tuned using methods such as Reinforcement\nLearning from Human Feedback (RLHF) to better “follow” human instructions.", "### Limitations and biases\n\n\nThe core functionality of a large language model is to take a string of text\nand predict the next token. The token used by the model need not produce the\nmost “accurate” text. Never rely on Pythia-1B to produce factually accurate\noutput.\n\n\nThis model was trained on the Pile, a dataset\nknown to contain profanity and texts that are lewd or otherwise offensive.\nSee Section 6 of the Pile paper for a\ndiscussion of documented biases with regards to gender, religion, and race.\nPythia-1B may produce socially unacceptable or undesirable text, *even if*\nthe prompt itself does not include anything explicitly offensive.\n\n\nIf you plan on using text generated through, for example, the Hosted Inference\nAPI, we recommend having a human curate the outputs of this language model\nbefore presenting it to other people. Please inform your audience that the\ntext was generated by Pythia-1B.", "### Quickstart\n\n\nPythia models can be loaded and used via the following code, demonstrated here\nfor the third 'pythia-70m-deduped' checkpoint:\n\n\nRevision/branch 'step143000' corresponds exactly to the model checkpoint on\nthe 'main' branch of each model. \n\nFor more information on how to use all Pythia models, see documentation on\nGitHub.\n\n\nTraining\n--------", "### Training data\n\n\nThe Pile is a 825GiB general-purpose dataset in\nEnglish. It was created by EleutherAI specifically for training large language\nmodels. It contains texts from 22 diverse sources, roughly broken down into\nfive categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),\nprose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and\nmiscellaneous (e.g. GitHub, Enron Emails). See the Pile\npaper for a breakdown of all data sources,\nmethodology, and a discussion of ethical implications. Consult the\ndatasheet for more detailed documentation\nabout the Pile and its component datasets. The Pile can be downloaded from\nthe official website, or from a community\nmirror. \n\nThe Pile was not deduplicated before being used to train Pythia-1B.", "### Training procedure\n\n\nAll models were trained on the exact same data, in the exact same order. Each\nmodel saw 299,892,736,000 tokens during training, and 143 checkpoints for each\nmodel are saved every 2,097,152,000 tokens, spaced evenly throughout training,\nfrom 'step1000' to 'step143000' (which is the same as 'main'). In addition, we\nalso provide frequent early checkpoints: 'step0' and 'step{1,2,4...512}'.\nThis corresponds to training for just under 1 epoch on the Pile for\nnon-deduplicated models, and about 1.5 epochs on the deduplicated Pile.\n\n\nAll *Pythia* models trained for 143000 steps at a batch size\nof 2M (2,097,152 tokens). \n\nSee GitHub for more details on training\nprocedure, including how to reproduce\nit. \n\nPythia uses the same tokenizer as GPT-NeoX-\n20B.\n\n\nEvaluations\n-----------\n\n\nAll 16 *Pythia* models were evaluated using the LM Evaluation\nHarness. You can access\nthe results by model and step at 'results/json/\\*' in the GitHub\nrepository. \n\nExpand the sections below to see plots of evaluation results for all\nPythia and Pythia-deduped models compared with OPT and BLOOM.\n\n\n\nLAMBADA – OpenAI\n\n\n\nPhysical Interaction: Question Answering (PIQA)\n\n\n\nWinoGrande\n\n\n\nAI2 Reasoning Challenge—Easy Set\n\n\n\nSciQ\n\n\nChangelog\n---------\n\n\nThis section compares differences between previously released\nPythia v0 and the current\nmodels. See Appendix B of the Pythia paper for further discussion of these\nchanges and the motivation behind them. We found that retraining Pythia had no\nimpact on benchmark performance.\n\n\n* All model sizes are now trained with uniform batch size of 2M tokens.\nPreviously, the models of size 160M, 410M, and 1.4B parameters were trained\nwith batch sizes of 4M tokens.\n* We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,\n128,256,512} in addition to every 1000 training steps.\n* Flash Attention was used in the new retrained suite.\n* We remedied a minor inconsistency that existed in the original suite: all\nmodels of size 2.8B parameters or smaller had a learning rate (LR) schedule\nwhich decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and\n12B models all used an LR schedule which decayed to a minimum LR of 0. In\nthe redone training runs, we rectified this inconsistency: all models now were\ntrained with LR decaying to a minimum of 0.1× their maximum LR.", "### Naming convention and parameter count\n\n\n*Pythia* models were renamed in January 2023. It is possible that the old\nnaming convention still persists in some documentation by accident. The\ncurrent naming convention (70M, 160M, etc.) is based on total parameter count." ]

[ "TAGS\n#transformers #safetensors #gpt_neox #text-generation #arxiv-2304.01373 #arxiv-2101.00027 #arxiv-2201.07311 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "### Intended Use\n\n\nThe primary intended use of Pythia is research on the behavior, functionality,\nand limitations of large language models. This suite is intended to provide\na controlled setting for performing scientific experiments. We also provide\n154 checkpoints per model: initial 'step0', 10 log-spaced checkpoints\n'step{1,2,4...512}', and 143 evenly-spaced checkpoints from 'step1000' to\n'step143000'. These checkpoints are hosted on Hugging Face as branches. Note\nthat branch '143000' corresponds exactly to the model checkpoint on the 'main'\nbranch of each model.\n\n\nYou may also further fine-tune and adapt Pythia-1B for deployment,\nas long as your use is in accordance with the Apache 2.0 license. Pythia\nmodels work with the Hugging Face Transformers\nLibrary. If you decide to use\npre-trained Pythia-1B as a basis for your fine-tuned model, please\nconduct your own risk and bias assessment.", "### Out-of-scope use\n\n\nThe Pythia Suite is not intended for deployment. It is not a in itself\na product and cannot be used for human-facing interactions. For example,\nthe model may generate harmful or offensive text. Please evaluate the risks\nassociated with your particular use case.\n\n\nPythia models are English-language only, and are not suitable for translation\nor generating text in other languages.\n\n\nPythia-1B has not been fine-tuned for downstream contexts in which\nlanguage models are commonly deployed, such as writing genre prose,\nor commercial chatbots. This means Pythia-1B will not\nrespond to a given prompt the way a product like ChatGPT does. This is because,\nunlike this model, ChatGPT was fine-tuned using methods such as Reinforcement\nLearning from Human Feedback (RLHF) to better “follow” human instructions.", "### Limitations and biases\n\n\nThe core functionality of a large language model is to take a string of text\nand predict the next token. The token used by the model need not produce the\nmost “accurate” text. Never rely on Pythia-1B to produce factually accurate\noutput.\n\n\nThis model was trained on the Pile, a dataset\nknown to contain profanity and texts that are lewd or otherwise offensive.\nSee Section 6 of the Pile paper for a\ndiscussion of documented biases with regards to gender, religion, and race.\nPythia-1B may produce socially unacceptable or undesirable text, *even if*\nthe prompt itself does not include anything explicitly offensive.\n\n\nIf you plan on using text generated through, for example, the Hosted Inference\nAPI, we recommend having a human curate the outputs of this language model\nbefore presenting it to other people. Please inform your audience that the\ntext was generated by Pythia-1B.", "### Quickstart\n\n\nPythia models can be loaded and used via the following code, demonstrated here\nfor the third 'pythia-70m-deduped' checkpoint:\n\n\nRevision/branch 'step143000' corresponds exactly to the model checkpoint on\nthe 'main' branch of each model. \n\nFor more information on how to use all Pythia models, see documentation on\nGitHub.\n\n\nTraining\n--------", "### Training data\n\n\nThe Pile is a 825GiB general-purpose dataset in\nEnglish. It was created by EleutherAI specifically for training large language\nmodels. It contains texts from 22 diverse sources, roughly broken down into\nfive categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),\nprose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and\nmiscellaneous (e.g. GitHub, Enron Emails). See the Pile\npaper for a breakdown of all data sources,\nmethodology, and a discussion of ethical implications. Consult the\ndatasheet for more detailed documentation\nabout the Pile and its component datasets. The Pile can be downloaded from\nthe official website, or from a community\nmirror. \n\nThe Pile was not deduplicated before being used to train Pythia-1B.", "### Training procedure\n\n\nAll models were trained on the exact same data, in the exact same order. Each\nmodel saw 299,892,736,000 tokens during training, and 143 checkpoints for each\nmodel are saved every 2,097,152,000 tokens, spaced evenly throughout training,\nfrom 'step1000' to 'step143000' (which is the same as 'main'). In addition, we\nalso provide frequent early checkpoints: 'step0' and 'step{1,2,4...512}'.\nThis corresponds to training for just under 1 epoch on the Pile for\nnon-deduplicated models, and about 1.5 epochs on the deduplicated Pile.\n\n\nAll *Pythia* models trained for 143000 steps at a batch size\nof 2M (2,097,152 tokens). \n\nSee GitHub for more details on training\nprocedure, including how to reproduce\nit. \n\nPythia uses the same tokenizer as GPT-NeoX-\n20B.\n\n\nEvaluations\n-----------\n\n\nAll 16 *Pythia* models were evaluated using the LM Evaluation\nHarness. You can access\nthe results by model and step at 'results/json/\\*' in the GitHub\nrepository. \n\nExpand the sections below to see plots of evaluation results for all\nPythia and Pythia-deduped models compared with OPT and BLOOM.\n\n\n\nLAMBADA – OpenAI\n\n\n\nPhysical Interaction: Question Answering (PIQA)\n\n\n\nWinoGrande\n\n\n\nAI2 Reasoning Challenge—Easy Set\n\n\n\nSciQ\n\n\nChangelog\n---------\n\n\nThis section compares differences between previously released\nPythia v0 and the current\nmodels. See Appendix B of the Pythia paper for further discussion of these\nchanges and the motivation behind them. We found that retraining Pythia had no\nimpact on benchmark performance.\n\n\n* All model sizes are now trained with uniform batch size of 2M tokens.\nPreviously, the models of size 160M, 410M, and 1.4B parameters were trained\nwith batch sizes of 4M tokens.\n* We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,\n128,256,512} in addition to every 1000 training steps.\n* Flash Attention was used in the new retrained suite.\n* We remedied a minor inconsistency that existed in the original suite: all\nmodels of size 2.8B parameters or smaller had a learning rate (LR) schedule\nwhich decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and\n12B models all used an LR schedule which decayed to a minimum LR of 0. In\nthe redone training runs, we rectified this inconsistency: all models now were\ntrained with LR decaying to a minimum of 0.1× their maximum LR.", "### Naming convention and parameter count\n\n\n*Pythia* models were renamed in January 2023. It is possible that the old\nnaming convention still persists in some documentation by accident. The\ncurrent naming convention (70M, 160M, etc.) is based on total parameter count." ]

[ "transformers", "safetensors", "gemma", "text-generation", "merge", "mergekit", "lazymergekit", "alfredplpl/suzume-poc", "alpindale/gemma-2b-it", "base_model:alfredplpl/suzume-poc", "base_model:alpindale/gemma-2b-it", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# suzume-linear1\n\nsuzume-linear1 is a merge of the following models using LazyMergekit:\n* alfredplpl/suzume-poc\n* alpindale/gemma-2b-it", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #gemma #text-generation #merge #mergekit #lazymergekit #alfredplpl/suzume-poc #alpindale/gemma-2b-it #base_model-alfredplpl/suzume-poc #base_model-alpindale/gemma-2b-it #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# suzume-linear1\n\nsuzume-linear1 is a merge of the following models using LazyMergekit:\n* alfredplpl/suzume-poc\n* alpindale/gemma-2b-it", "## Configuration", "## Usage" ]

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:vinai/bertweet-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# results_bertweet\n\nThis model is a fine-tuned version of vinai/bertweet-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6385\n- Accuracy: 0.7839\n- F1: 0.4898", "## 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: 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: 100\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-vinai/bertweet-base #autotrain_compatible #endpoints_compatible #region-us \n", "# results_bertweet\n\nThis model is a fine-tuned version of vinai/bertweet-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6385\n- Accuracy: 0.7839\n- F1: 0.4898", "## 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: 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: 100\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "transformers", "gguf", "en", "base_model:featherlite-ai/Featherlite-Vicuna-13B-chat", "license:llama2", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #en #base_model-featherlite-ai/Featherlite-Vicuna-13B-chat #license-llama2 #endpoints_compatible #region-us \n" ]

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

[]

[ "en" ]

[ "### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.", "#### Transformers pipeline", "#### Transformers AutoModelForCausalLM", "### 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#transformers #safetensors #llama #text-generation #facebook #meta #pytorch #llama-3 #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.", "#### Transformers pipeline", "#### Transformers AutoModelForCausalLM", "### 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" ]

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

[ "1910.09700" ]

[]

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

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

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

[ "peft", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "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 0.10.1.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-Trelis/Llama-2-7b-chat-hf-sharded-bf16 #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 0.10.1.dev0" ]

[ "gguf", "facebook", "meta", "llama", "llama-3", "GGUF", "text-generation", "en", "dataset:Yukang/LongAlpaca-16k-length", "license:other", "region:us" ]

[]

[ "en" ]

[ "# GGUFs of Llama-3-8B-16K\nGGUF conversion and quantization of URL\n\nDone with Maxime Labonne's AutoGGUF", "## Orginal model card\nThis is an extended (16K) context version of LLaMA 3 8B (base, not instruct). Trained for five hours on 8x A6000 GPUs, using the 'Yukang/LongAlpaca-16k-length' dataset.\n\n'rope_theta' was set to '1000000.0'. Trained with Axolotl." ]

[ "TAGS\n#gguf #facebook #meta #llama #llama-3 #GGUF #text-generation #en #dataset-Yukang/LongAlpaca-16k-length #license-other #region-us \n", "# GGUFs of Llama-3-8B-16K\nGGUF conversion and quantization of URL\n\nDone with Maxime Labonne's AutoGGUF", "## Orginal model card\nThis is an extended (16K) context version of LLaMA 3 8B (base, not instruct). Trained for five hours on 8x A6000 GPUs, using the 'Yukang/LongAlpaca-16k-length' dataset.\n\n'rope_theta' was set to '1000000.0'. Trained with Axolotl." ]

[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-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 #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-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", "albert", "text-classification", "generated_from_trainer", "base_model:KalaiselvanD/kalai_bert_model_test_4_out", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #albert #text-classification #generated_from_trainer #base_model-KalaiselvanD/kalai_bert_model_test_4_out #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[]

[ "sv" ]

[ "# URL-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "dataset:vicgalle/configurable-system-prompt-multitask", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "# hus960/Unsafe-Llama-3-8B-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'vicgalle/Unsafe-Llama-3-8B' 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 #llama-cpp #gguf-my-repo #dataset-vicgalle/configurable-system-prompt-multitask #license-apache-2.0 #endpoints_compatible #region-us \n", "# hus960/Unsafe-Llama-3-8B-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'vicgalle/Unsafe-Llama-3-8B' 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", "falcon", "text-generation", "custom_code", "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 #falcon #text-generation #custom_code #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", "megatron-bert", "feature-extraction", "sv", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# URL-bpe-32k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-bpe-32k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

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

[ "1910.09700" ]

[]

[ "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\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* Repository:\n* Paper [optional]:\n* Demo [optional]:\n\n\nUses\n----", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use\n\n\nBias, Risks, and Limitations\n----------------------------", "### Recommendations\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nTraining Details\n----------------", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n\n* Training regime:", "#### Speeds, Sizes, Times [optional]\n\n\nEvaluation\n----------", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary\n\n\nModel Examination [optional]\n----------------------------\n\n\nEnvironmental Impact\n--------------------\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n\n* Hardware Type:\n* Hours used:\n* Cloud Provider:\n* Compute Region:\n* Carbon Emitted:\n\n\nTechnical Specifications [optional]\n-----------------------------------", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nGlossary [optional]\n-------------------\n\n\nMore Information [optional]\n---------------------------\n\n\nModel Card Authors [optional]\n-----------------------------\n\n\nModel Card Contact\n------------------" ]

[ "TAGS\n#gguf #arxiv-1910.09700 #region-us \n", "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\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* Repository:\n* Paper [optional]:\n* Demo [optional]:\n\n\nUses\n----", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use\n\n\nBias, Risks, and Limitations\n----------------------------", "### Recommendations\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nTraining Details\n----------------", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n\n* Training regime:", "#### Speeds, Sizes, Times [optional]\n\n\nEvaluation\n----------", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary\n\n\nModel Examination [optional]\n----------------------------\n\n\nEnvironmental Impact\n--------------------\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n\n* Hardware Type:\n* Hours used:\n* Cloud Provider:\n* Compute Region:\n* Carbon Emitted:\n\n\nTechnical Specifications [optional]\n-----------------------------------", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nGlossary [optional]\n-------------------\n\n\nMore Information [optional]\n---------------------------\n\n\nModel Card Authors [optional]\n-----------------------------\n\n\nModel Card Contact\n------------------" ]

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

[]

[ "sv" ]

[ "# URL-bpe-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-bpe-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "transformers", "safetensors", "trl", "dpo", "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 #trl #dpo #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", "megatron-bert", "feature-extraction", "sv", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# URL-bpe-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-bpe-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

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

[]

[ "sv" ]

[ "# URL-bpe-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-bpe-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:unalignment/toxic-dpo-v0.2", "license:other", "region:us" ]

[]

[ "en" ]

[ "# hus960/Llama-3-8b.UNLEASHED-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'raincandy-u/Llama-3-8b.UNLEASHED' 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 #facebook #meta #pytorch #llama #llama-3 #llama-cpp #gguf-my-repo #text-generation #en #dataset-unalignment/toxic-dpo-v0.2 #license-other #region-us \n", "# hus960/Llama-3-8b.UNLEASHED-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'raincandy-u/Llama-3-8b.UNLEASHED' 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", "megatron-bert", "feature-extraction", "sv", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# URL-base.unigram-32k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-base.unigram-32k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

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

[]

[ "sv" ]

[ "# URL-base.unigram-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-base.unigram-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

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

[]

[ "sv" ]

[ "# URL-base.unigram-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-base.unigram-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "transformers", "gguf", "stabilityai", "imatrix", "stable-code-instruct-3b", "text-generation", "en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# From original readme", "## Usage\nHere's how you can run the model use the model:\n\n\n'''" ]

[ "TAGS\n#transformers #gguf #stabilityai #imatrix #stable-code-instruct-3b #text-generation #en #license-other #region-us \n", "# From original readme", "## Usage\nHere's how you can run the model use the model:\n\n\n'''" ]

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

[]

[ "sv" ]

[ "# URL-base.unigram-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-base.unigram-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

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

[]

[ "sv" ]

[ "# URL-base.wordpiece-32k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-base.wordpiece-32k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "gguf", "merge", "mergekit", "lazymergekit", "llama-cpp", "gguf-my-repo", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "region:us" ]

[]

[]

[ "# hus960/Llama-3-SLERP-8B-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'mlabonne/Llama-3-SLERP-8B' 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 #merge #mergekit #lazymergekit #llama-cpp #gguf-my-repo #base_model-meta-llama/Meta-Llama-3-8B #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #region-us \n", "# hus960/Llama-3-SLERP-8B-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'mlabonne/Llama-3-SLERP-8B' 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", "megatron-bert", "feature-extraction", "sv", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# URL-base.wordpiece-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-base.wordpiece-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "transformers", "safetensors", "gemma", "text-generation", "pytorch", "instruct", "finetune", "translation", "conversational", "ko", "dataset:traintogpb/aihub-flores-koen-integrated-sparta-30k", "base_model:beomi/gemma-ko-2b", "license:gemma", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "ko" ]

[ "# Gemma 2B Translation v0.122\n\n- Eval Loss: '0.45365'\n- Train Loss: '0.43420'\n- lr: '6e-05'\n- optimizer: adamw\n- lr_scheduler_type: cosine", "## Prompt Template", "## Model Description\n\n- Developed by: 'lemon-mint'\n- Model type: Gemma\n- Language(s) (NLP): English\n- License: gemma-terms-of-use\n- Finetuned from model: beomi/gemma-ko-2b" ]

[ "TAGS\n#transformers #safetensors #gemma #text-generation #pytorch #instruct #finetune #translation #conversational #ko #dataset-traintogpb/aihub-flores-koen-integrated-sparta-30k #base_model-beomi/gemma-ko-2b #license-gemma #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Gemma 2B Translation v0.122\n\n- Eval Loss: '0.45365'\n- Train Loss: '0.43420'\n- lr: '6e-05'\n- optimizer: adamw\n- lr_scheduler_type: cosine", "## Prompt Template", "## Model Description\n\n- Developed by: 'lemon-mint'\n- Model type: Gemma\n- Language(s) (NLP): English\n- License: gemma-terms-of-use\n- Finetuned from model: beomi/gemma-ko-2b" ]

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

[]

[ "sv" ]

[ "# URL-base.wordpiece-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-base.wordpiece-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

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

[]

[ "sv" ]

[ "# URL-base.wordpiece-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-base.wordpiece-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:TheBloke/Llama-2-7B-fp16", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 5\n* total\\_train\\_batch\\_size: 10\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 100", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-TheBloke/Llama-2-7B-fp16 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 5\n* total\\_train\\_batch\\_size: 10\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 100", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

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

[]

[ "sv" ]

[ "# URL-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-64k-no_pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "transformers", "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "ko", "base_model:lcw99/llama-3-8b-it-ko-chang", "license:other", "endpoints_compatible", "region:us" ]

[]

[ "ko" ]

[ "# Meta-Llama-3-8B-Instruct-GGUF\n\n- This is GGUF quantized version of lcw99/llama-3-8b-it-ko-chang created using URL", "### Model Description\n\nKorean instruction tunning of meta-llama/Meta-Llama-3-8B-Instruct", "#### Chat template\n\nsystem: system message... \nB: user message... \nA: assistant message..." ]

[ "TAGS\n#transformers #gguf #facebook #meta #pytorch #llama #llama-3 #text-generation #ko #base_model-lcw99/llama-3-8b-it-ko-chang #license-other #endpoints_compatible #region-us \n", "# Meta-Llama-3-8B-Instruct-GGUF\n\n- This is GGUF quantized version of lcw99/llama-3-8b-it-ko-chang created using URL", "### Model Description\n\nKorean instruction tunning of meta-llama/Meta-Llama-3-8B-Instruct", "#### Chat template\n\nsystem: system message... \nB: user message... \nA: assistant message..." ]

[ "transformers", "tf", "roberta", "question-answering", "generated_from_keras_callback", "base_model:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 60, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* TensorFlow 2.15.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tf #roberta #question-answering #generated_from_keras_callback #base_model-deepset/roberta-base-squad2 #license-cc-by-4.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 60, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* TensorFlow 2.15.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[]

[ "sv" ]

[ "# URL-bpe-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-bpe-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "gguf", "text-generation", "en", "de", "license:apache-2.0", "region:us" ]

[]

[ "en", "de" ]

[]

[ "TAGS\n#gguf #text-generation #en #de #license-apache-2.0 #region-us \n" ]

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

[]

[ "sv" ]

[ "# URL-large.unigram-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-large.unigram-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:projecte-aina/roberta-base-ca-v2-cased-te", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# modelofine\n\nThis model is a fine-tuned version of projecte-aina/roberta-base-ca-v2-cased-te on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8036\n- Accuracy: 0.0672", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-projecte-aina/roberta-base-ca-v2-cased-te #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# modelofine\n\nThis model is a fine-tuned version of projecte-aina/roberta-base-ca-v2-cased-te on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8036\n- Accuracy: 0.0672", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "text-generation", "custom_code", "license:mit", "autotrain_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #safetensors #text-generation #custom_code #license-mit #autotrain_compatible #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "sv" ]

[ "# URL-large.unigram-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-large.unigram-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "transformers", "safetensors", "llama", "text-generation", "awq", "int8", "llama3", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

[]

[ "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#transformers #safetensors #llama #text-generation #awq #int8 #llama3 #facebook #meta #pytorch #llama-3 #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #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" ]

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

[]

[ "sv" ]

[ "# URL-large.wordpiece-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-large.wordpiece-32k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

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

[]

[]

[ "# detr-resnet-50_finetuned_cppe5\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 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: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 1.13.1+cu117\n- Datasets 2.15.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n", "# detr-resnet-50_finetuned_cppe5\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 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: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 1.13.1+cu117\n- Datasets 2.15.0\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "llama", "text-generation", "autoawq", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[]

[ "en" ]

[ "## Model Details\n\nThis is meta-llama/Meta-Llama-3-8B quantized and serialized with AutoAWQ in 4-bit. \n\nDetails here:\n\nFine-tune Llama 3 on Your Computer\n\n\n- Developed by: The Kaitchup\n- Language(s) (NLP): English\n- License: Apache 2.0 license ; You must also accept the Llama 3 license" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #autoawq #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "## Model Details\n\nThis is meta-llama/Meta-Llama-3-8B quantized and serialized with AutoAWQ in 4-bit. \n\nDetails here:\n\nFine-tune Llama 3 on Your Computer\n\n\n- Developed by: The Kaitchup\n- Language(s) (NLP): English\n- License: Apache 2.0 license ; You must also accept the Llama 3 license" ]

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

[]

[ "sv" ]

[ "# URL-large.wordpiece-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "TAGS\n#transformers #safetensors #megatron-bert #feature-extraction #sv #endpoints_compatible #region-us \n", "# URL-large.wordpiece-64k-pretok.25k-steps\n\nThis BERT model was trained using the NeMo library.\nThe size of the model is a regular bert-large.\nThe model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 25k training steps using a batch size of 8k.\n\nThe model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters:\n- URL-32k-no_pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-bpe-64k-no_pretok.25k-steps\n- URL-bpe-64k-pretok.25k-steps\n- URL-base.unigram-32k-no_pretok.25k-steps\n- URL-base.unigram-32k-pretok.25k-steps\n- URL-base.unigram-64k-no_pretok.25k-steps\n- URL-base.unigram-64k-pretok.25k-steps\n- URL-base.wordpiece-32k-no_pretok.25k-steps\n- URL-base.wordpiece-32k-pretok.25k-steps\n- URL-base.wordpiece-64k-no_pretok.25k-steps\n- URL-base.wordpiece-64k-pretok.25k-steps\n- URL-64k-no_pretok.25k-steps\n- URL-bpe-32k-pretok.25k-steps\n- URL-large.unigram-32k-pretok.25k-steps\n- URL-large.unigram-64k-pretok.25k-steps\n- URL-large.wordpiece-32k-pretok.25k-steps\n- URL-large.wordpiece-64k-pretok.25k-steps", "## Acknowledgements\n\nThe training was performed on the Luxembourg national supercomputer MeluXina.\nThe authors gratefully acknowledge the LuxProvide teams for their expert support." ]

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "fi", "license:other", "region:us" ]

[]

[ "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "fi" ]

[ "# hus960/openbuddy-qwen1.5-14b-v21.1-32k-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'OpenBuddy/openbuddy-qwen1.5-14b-v21.1-32k' 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 #llama-cpp #gguf-my-repo #text-generation #zh #en #fr #de #ja #ko #it #ru #fi #license-other #region-us \n", "# hus960/openbuddy-qwen1.5-14b-v21.1-32k-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'OpenBuddy/openbuddy-qwen1.5-14b-v21.1-32k' 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", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* total\\_train\\_batch\\_size: 16\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: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #conversational #dataset-generator #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* total\\_train\\_batch\\_size: 16\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: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:TheBloke/Llama-2-7B-fp16", "region:us" ]

[]

[]

[ "### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 5\n* total\\_train\\_batch\\_size: 10\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-TheBloke/Llama-2-7B-fp16 #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 5\n* total\\_train\\_batch\\_size: 10\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

[]

[]

[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:BAAI/bge-small-en-v1.5", "model-index", "region:us" ]

[ "2209.11055" ]

[]

[ "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: BAAI/bge-small-en-v1.5\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 5 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (32, 32)\n* num\\_epochs: (10, 10)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.11.8\n* SetFit: 1.0.3\n* Sentence Transformers: 2.7.0\n* Transformers: 4.40.0\n* PyTorch: 2.2.2\n* Datasets: 2.19.0\n* Tokenizers: 0.19.1", "### BibTeX" ]

[ "TAGS\n#setfit #safetensors #bert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-BAAI/bge-small-en-v1.5 #model-index #region-us \n", "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: BAAI/bge-small-en-v1.5\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 5 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (32, 32)\n* num\\_epochs: (10, 10)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.11.8\n* SetFit: 1.0.3\n* Sentence Transformers: 2.7.0\n* Transformers: 4.40.0\n* PyTorch: 2.2.2\n* Datasets: 2.19.0\n* Tokenizers: 0.19.1", "### BibTeX" ]

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "license:other", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.1.dev0\n* Transformers 4.41.0.dev0\n* Pytorch 2.1.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-meta-llama/Meta-Llama-3-70B-Instruct #license-other #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 500", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.1.dev0\n* Transformers 4.41.0.dev0\n* Pytorch 2.1.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.17887", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2403.17887" ]

[]

[ "# Llama-3-6B-Instruct-pruned\n*Experimental*\n\nUsing PruneMe to find minimal average distance. Thank you for awesome toolkit @arcee-ai !\n<img src=\"./URL\" alt=\"distance\" width=\"390\"/>\n*It shows pruning the 22-30 layer is the best option, but I'm worried about drasitical change between 22 to 23.*", "### Disclaimer\nI haven't done any post-training (called 'healing' process as the paper suggests), will do it later but no guarantee at all.\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* meta-llama/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-2403.17887 #base_model-meta-llama/Meta-Llama-3-8B-Instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Llama-3-6B-Instruct-pruned\n*Experimental*\n\nUsing PruneMe to find minimal average distance. Thank you for awesome toolkit @arcee-ai !\n<img src=\"./URL\" alt=\"distance\" width=\"390\"/>\n*It shows pruning the 22-30 layer is the best option, but I'm worried about drasitical change between 22 to 23.*", "### Disclaimer\nI haven't done any post-training (called 'healing' process as the paper suggests), will do it later but no guarantee at all.\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* meta-llama/Meta-Llama-3-8B-Instruct", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "endpoints_compatible", "region:us" ]

[]

[]

[ "# RachidAR/llama3-Mirage-Walker-8b-v0.2-slerp-Q6_K-GGUF\nThis model was converted to GGUF format from 'taozi555/llama3-Mirage-Walker-8b-v0.2-slerp' 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 #mergekit #merge #llama-cpp #gguf-my-repo #base_model-meta-llama/Meta-Llama-3-8B-Instruct #endpoints_compatible #region-us \n", "# RachidAR/llama3-Mirage-Walker-8b-v0.2-slerp-Q6_K-GGUF\nThis model was converted to GGUF format from 'taozi555/llama3-Mirage-Walker-8b-v0.2-slerp' 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", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "1910.09700" ]

[]

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

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

[ "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", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "code", "medical ", "farmer", "doctor", "Mega-Series", "Cyber-Series", "Role-Play", "Self-Rag", "ThinkingBot", "milestone", "mega-series", "SpydazWebAI", "llama-cpp", "gguf-my-repo", "en", "dataset:gretelai/synthetic_text_to_sql", "dataset:HuggingFaceTB/cosmopedia", "dataset:teknium/OpenHermes-2.5", "dataset:Open-Orca/SlimOrca", "dataset:Open-Orca/OpenOrca", "dataset:cognitivecomputations/dolphin-coder", "dataset:databricks/databricks-dolly-15k", "dataset:yahma/alpaca-cleaned", "dataset:uonlp/CulturaX", "dataset:mwitiderrick/SwahiliPlatypus", "dataset:swahili", "dataset:Rogendo/English-Swahili-Sentence-Pairs", "dataset:ise-uiuc/Magicoder-Evol-Instruct-110K", "dataset:meta-math/MetaMathQA", "base_model:LeroyDyer/Mixtral_AI_CyberUltron", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# LeroyDyer/Mixtral_AI_CyberUltron-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'LeroyDyer/Mixtral_AI_CyberUltron' 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 #text-generation-inference #unsloth #mistral #trl #code #medical #farmer #doctor #Mega-Series #Cyber-Series #Role-Play #Self-Rag #ThinkingBot #milestone #mega-series #SpydazWebAI #llama-cpp #gguf-my-repo #en #dataset-gretelai/synthetic_text_to_sql #dataset-HuggingFaceTB/cosmopedia #dataset-teknium/OpenHermes-2.5 #dataset-Open-Orca/SlimOrca #dataset-Open-Orca/OpenOrca #dataset-cognitivecomputations/dolphin-coder #dataset-databricks/databricks-dolly-15k #dataset-yahma/alpaca-cleaned #dataset-uonlp/CulturaX #dataset-mwitiderrick/SwahiliPlatypus #dataset-swahili #dataset-Rogendo/English-Swahili-Sentence-Pairs #dataset-ise-uiuc/Magicoder-Evol-Instruct-110K #dataset-meta-math/MetaMathQA #base_model-LeroyDyer/Mixtral_AI_CyberUltron #license-apache-2.0 #endpoints_compatible #region-us \n", "# LeroyDyer/Mixtral_AI_CyberUltron-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'LeroyDyer/Mixtral_AI_CyberUltron' 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", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

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

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

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:filipealmeida/Mistral-7B-Instruct-v0.1-sharded", "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 0.10.1.dev0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-filipealmeida/Mistral-7B-Instruct-v0.1-sharded #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 0.10.1.dev0" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

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

[ "1910.09700" ]

[]

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

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

[ "peft", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "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 0.10.1.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-Trelis/Llama-2-7b-chat-hf-sharded-bf16 #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 0.10.1.dev0" ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu118\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert/distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu118\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us" ]

[]

[]

[ "# RachidAR/Llama-3-8B-Instruct-Physics-5k-Scar-Q6_K-GGUF\nThis model was converted to GGUF format from 'nmdr/Llama-3-8B-Instruct-Physics-5k-Scar' 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 #llama-cpp #gguf-my-repo #endpoints_compatible #region-us \n", "# RachidAR/Llama-3-8B-Instruct-Physics-5k-Scar-Q6_K-GGUF\nThis model was converted to GGUF format from 'nmdr/Llama-3-8B-Instruct-Physics-5k-Scar' 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", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "code", "medical ", "farmer", "doctor", "Mega-Series", "Cyber-Series", "Role-Play", "Self-Rag", "ThinkingBot", "milestone", "mega-series", "SpydazWebAI", "llama-cpp", "gguf-my-repo", "en", "dataset:gretelai/synthetic_text_to_sql", "dataset:HuggingFaceTB/cosmopedia", "dataset:teknium/OpenHermes-2.5", "dataset:Open-Orca/SlimOrca", "dataset:Open-Orca/OpenOrca", "dataset:cognitivecomputations/dolphin-coder", "dataset:databricks/databricks-dolly-15k", "dataset:yahma/alpaca-cleaned", "dataset:uonlp/CulturaX", "dataset:mwitiderrick/SwahiliPlatypus", "dataset:swahili", "dataset:Rogendo/English-Swahili-Sentence-Pairs", "dataset:ise-uiuc/Magicoder-Evol-Instruct-110K", "dataset:meta-math/MetaMathQA", "base_model:LeroyDyer/Mixtral_AI_Ultron", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# LeroyDyer/Mixtral_AI_Ultron-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'LeroyDyer/Mixtral_AI_Ultron' 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 #text-generation-inference #unsloth #mistral #trl #code #medical #farmer #doctor #Mega-Series #Cyber-Series #Role-Play #Self-Rag #ThinkingBot #milestone #mega-series #SpydazWebAI #llama-cpp #gguf-my-repo #en #dataset-gretelai/synthetic_text_to_sql #dataset-HuggingFaceTB/cosmopedia #dataset-teknium/OpenHermes-2.5 #dataset-Open-Orca/SlimOrca #dataset-Open-Orca/OpenOrca #dataset-cognitivecomputations/dolphin-coder #dataset-databricks/databricks-dolly-15k #dataset-yahma/alpaca-cleaned #dataset-uonlp/CulturaX #dataset-mwitiderrick/SwahiliPlatypus #dataset-swahili #dataset-Rogendo/English-Swahili-Sentence-Pairs #dataset-ise-uiuc/Magicoder-Evol-Instruct-110K #dataset-meta-math/MetaMathQA #base_model-LeroyDyer/Mixtral_AI_Ultron #license-apache-2.0 #endpoints_compatible #region-us \n", "# LeroyDyer/Mixtral_AI_Ultron-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'LeroyDyer/Mixtral_AI_Ultron' 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", "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us" ]

[]

[]

[ "# RachidAR/ablation-model-fineweb-v1-Q6_K-GGUF\nThis model was converted to GGUF format from 'HuggingFaceFW/ablation-model-fineweb-v1' 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 #llama-cpp #gguf-my-repo #endpoints_compatible #region-us \n", "# RachidAR/ablation-model-fineweb-v1-Q6_K-GGUF\nThis model was converted to GGUF format from 'HuggingFaceFW/ablation-model-fineweb-v1' 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." ]

[ "peft", "pytorch", "has_space", "region:us" ]

[]

[]

[ "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n- PEFT 0.4.0\n- PEFT 0.4.0\n\n- PEFT 0.4.0" ]

[ "TAGS\n#peft #pytorch #has_space #region-us \n", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n- PEFT 0.4.0\n- PEFT 0.4.0\n\n- PEFT 0.4.0" ]

[ "gguf", "llama", "NorskGPT", "instruct", "finetune", "no", "base_model:bineric/NorskGPT-Llama3-8b", "license:cc-by-nc-sa-4.0", "has_space", "region:us" ]

[]

[ "no" ]

[ "### About GGUF\n\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.\n\n\nPrompt template: ChatML\n-----------------------\n\n\nExplanation of quantisation methods\n-----------------------------------\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n\n\n\nProvided files\n--------------\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL\n\n\nThanks to TheBloke for preparing an amazing README on how to use GGUF models" ]

[ "TAGS\n#gguf #llama #NorskGPT #instruct #finetune #no #base_model-bineric/NorskGPT-Llama3-8b #license-cc-by-nc-sa-4.0 #has_space #region-us \n", "### About GGUF\n\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.\n\n\nPrompt template: ChatML\n-----------------------\n\n\nExplanation of quantisation methods\n-----------------------------------\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n\n\n\nProvided files\n--------------\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL\n\n\nThanks to TheBloke for preparing an amazing README on how to use GGUF models" ]

[ "fastai", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:HuggingFaceTB/cosmopedia", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# BasedBots/cosmo-1b-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'HuggingFaceTB/cosmo-1b' using URL.\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#fastai #gguf #llama-cpp #gguf-my-repo #text-generation #en #dataset-HuggingFaceTB/cosmopedia #license-apache-2.0 #region-us \n", "# BasedBots/cosmo-1b-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'HuggingFaceTB/cosmo-1b' using URL.\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", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\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-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "physics", "astrophysics", "high-energy physics (HEP)", "history of science", "philosophy of science", "sociology of science", "epistemic change", "arXiv", "en", "dataset:wikipedia", "dataset:bookcorpus", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Model Card for Astro-HEP-BERT\n\nAstro-HEP-BERT is a bidirectional transformer designed primarily to generate contextualized word embeddings for analyzing epistemic change in astrophysics and high-energy physics (see <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"URL >NEPI research project</a>). Built upon Google's 'bert-base-uncased', the model underwent additional training for three epochs using approximately 21.5 million paragraphs extracted from around 600,000 scholarly articles sourced from arXiv, all pertaining to astrophysics and/or high-energy physics (HEP). The sole training objective was masked language modeling.\n\nThe Astro-HEP-BERT project embodies the spirit of a tabletop experiment or grassroots scientific effort. It exclusively utilized open-source inputs during training, and the entire training process was completed on a single MacBook Pro M2/96GB over a span of 6 weeks for 3 epochs. This project stands as a proof of concept, showcasing the viability of employing a bidirectional transformer for research ventures in the history, philosophy, and sociology of science (HPSS) even with limited financial resources.\n\nFor further insights into the model, the corpus, and the underlying research project please refer to the Astro-HEP-BERT paper [link coming soon].", "## Model Details\n\n- Developer: <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"URL Simons</a>\n- Funded by: European Research Council (ERC) under Grant agreement ID: <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"URL >101044932</a>\n- Language (NLP): English\n- License: apache-2.0\n- Parent model: Google's <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"URL" ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #physics #astrophysics #high-energy physics (HEP) #history of science #philosophy of science #sociology of science #epistemic change #arXiv #en #dataset-wikipedia #dataset-bookcorpus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Astro-HEP-BERT\n\nAstro-HEP-BERT is a bidirectional transformer designed primarily to generate contextualized word embeddings for analyzing epistemic change in astrophysics and high-energy physics (see <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"URL >NEPI research project</a>). Built upon Google's 'bert-base-uncased', the model underwent additional training for three epochs using approximately 21.5 million paragraphs extracted from around 600,000 scholarly articles sourced from arXiv, all pertaining to astrophysics and/or high-energy physics (HEP). The sole training objective was masked language modeling.\n\nThe Astro-HEP-BERT project embodies the spirit of a tabletop experiment or grassroots scientific effort. It exclusively utilized open-source inputs during training, and the entire training process was completed on a single MacBook Pro M2/96GB over a span of 6 weeks for 3 epochs. This project stands as a proof of concept, showcasing the viability of employing a bidirectional transformer for research ventures in the history, philosophy, and sociology of science (HPSS) even with limited financial resources.\n\nFor further insights into the model, the corpus, and the underlying research project please refer to the Astro-HEP-BERT paper [link coming soon].", "## Model Details\n\n- Developer: <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"URL Simons</a>\n- Funded by: European Research Council (ERC) under Grant agreement ID: <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"URL >101044932</a>\n- Language (NLP): English\n- License: apache-2.0\n- Parent model: Google's <a target=\"_blank\" rel=\"noopener noreferrer\" href=\"URL" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "license:apache-2.0", "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 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

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

[ "1910.09700" ]

[]

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

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

[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# m0javad/Meta-Llama-3-8B-Q8_0-GGUF\nThis model was converted to GGUF format from 'meta-llama/Meta-Llama-3-8B' \nRefer to the original model card for more details on the model." ]

[ "TAGS\n#gguf #facebook #meta #pytorch #llama #llama-3 #llama-cpp #gguf-my-repo #text-generation #en #license-other #region-us \n", "# m0javad/Meta-Llama-3-8B-Q8_0-GGUF\nThis model was converted to GGUF format from 'meta-llama/Meta-Llama-3-8B' \nRefer to the original model card for more details on the model." ]

[ "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:ai-forever/ruBert-base", "license:apache-2.0", "region:us" ]

[]

[]

[ "# HSE_PRAVO_complexity_classifier3\n\nThis model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 3\n- eval_batch_size: 3\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5", "### Training results", "### Framework versions\n\n- PEFT 0.10.1.dev0\n- Transformers 4.36.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us \n", "# HSE_PRAVO_complexity_classifier3\n\nThis model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 3\n- eval_batch_size: 3\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5", "### Training results", "### Framework versions\n\n- PEFT 0.10.1.dev0\n- Transformers 4.36.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2" ]

[ "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", "gguf", "en", "base_model:NurtureAI/Meta-Llama-3-2x8B-Instruct-MoE-64k-ctx", "license:llama3", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #en #base_model-NurtureAI/Meta-Llama-3-2x8B-Instruct-MoE-64k-ctx #license-llama3 #endpoints_compatible #region-us \n" ]

[ "transformers", "safetensors", "llama", "text-generation", "gptq", "int4", "llama3", "facebook", "meta", "pytorch", "llama-3", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[]

[ "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#transformers #safetensors #llama #text-generation #gptq #int4 #llama3 #facebook #meta #pytorch #llama-3 #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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" ]

[ "transformers", "safetensors", "phi", "text-generation", "conversational", "custom_code", "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 #phi #text-generation #conversational #custom_code #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" ]