Datasets:

---

ftopal

/

huggingface-models

like 0

[ "transformers", "transformer", "text-to-video", "en", "dataset:Kalaphant/Videos", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #transformer #text-to-video #en #dataset-Kalaphant/Videos #endpoints_compatible #region-us \n" ]

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

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

[]

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

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nModel Card for Bloom-1b7\n========================\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Recommendations\n5. Training Data\n6. Evaluation\n7. Environmental Impact\n8. Technical Specifications\n9. Citation\n10. Glossary and Calculations\n11. More Information\n12. Model Card Authors\n13. Model Card Contact\n\n\nModel Details\n-------------", "### Model Description\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n* Developed by: BigScience (website)\n\n\n\t+ All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n* Model Type: Transformer-based Language Model\n* Version: 1.0.0\n* Languages: Multiple; see training data\n* License: RAIL License v1.0 (link)\n* Release Date Estimate: Monday, 11.July.2022\n* Funded by:\n\n\n\t+ The French government.\n\t+ Hugging Face (website).\n\t+ Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs", "### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\n\nThe following table shows the distribution of programming languages.\n\n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n(More evaluation scores forthcoming at the end of model training.)\n\n\n* BLOOM Book: Read generations from BLOOM based on prompts provided by the community\n\n\nEnvironmental Impact\n--------------------\n\n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\nTechnical Specifications\n------------------------\n\n\n*This section provides information for people who work on model development.*\n\n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 1,722,408,960 parameters:\n\n\n\t+ 513,802,240 embedding parameters\n\t+ 24 layers, 16 attention heads\n\t+ Hidden layers are 2048-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 64 V100 16/32GB GPUs (16 nodes):\n\n\n\t+ 4 GPUs per node\n\t+ 40 CPUs per task\n\t+ 1 task per node\n\t+ CPU: AMD\n\t+ CPU memory: 160GB per node\n\t+ GPU memory: 64GB or 128GB (depending on node availability during training) per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "### Training\n\n\n* Checkpoint size:\n\n\n\t+ Fp16 weights: 2.6GB (# params \\* 2)\n\t+ Full checkpoint with optimizer states: --\n* Training throughput: --\n* Number of epochs: 1\n* Dates:\n\n\n\t+ Start: 11th March, 2022 11:42am PST\n\t+ End: 20 May, 2022\n* Server training location: Île-de-France, France", "### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\nMore Information\n----------------", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n\nModel Card Contact\n------------------\n\n\nSend Questions to: bigscience-contact@URL" ]

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:gpt2", "license:mit", "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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #dataset-eli5_category #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[]

[]

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

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

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

[]

[]

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

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

[ "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "ko", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "ko" ]

[ "# Crow13/nvnv-alice-Q8_0-GGUF\nThis model was converted to GGUF format from 'instructkr/nvnv-alice' 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 #llama-cpp #gguf-my-repo #text-generation #ko #license-cc-by-nc-4.0 #region-us \n", "# Crow13/nvnv-alice-Q8_0-GGUF\nThis model was converted to GGUF format from 'instructkr/nvnv-alice' 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." ]

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

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

[]

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

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

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

[]

[]

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

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

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:aaditya/OpenBioLLM-Llama3-8B", "base_model:o2satz/WS_med_QA_Dolphin", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* aaditya/OpenBioLLM-Llama3-8B\n* o2satz/WS_med_QA_Dolphin", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #base_model-aaditya/OpenBioLLM-Llama3-8B #base_model-o2satz/WS_med_QA_Dolphin #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* aaditya/OpenBioLLM-Llama3-8B\n* o2satz/WS_med_QA_Dolphin", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-base", "license:apache-2.0", "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: 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* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 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* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_userresponse_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.001_4iters_bs128_nodpo_only4w_userresponse_iter_3\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_userresponse_iter_2 on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_userresponse_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_4iters_bs128_nodpo_only4w_userresponse_iter_3\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_userresponse_iter_2 on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

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

[]

[]

[ "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Joalbom14/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n", "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Joalbom14/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2403.19522", "base_model:mistralai/Mistral-7B-v0.1", "base_model:cognitivecomputations/dolphin-2.8-mistral-7b-v02", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2403.19522" ]

[]

[ "# ExperimentOne (Mistral-Hermes-Dolphin-7b)\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the Model Stock merge method using mistralai/Mistral-7B-v0.1 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.8-mistral-7b-v02\n* NousResearch/Hermes-2-Pro-Mistral-7B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2403.19522 #base_model-mistralai/Mistral-7B-v0.1 #base_model-cognitivecomputations/dolphin-2.8-mistral-7b-v02 #base_model-NousResearch/Hermes-2-Pro-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ExperimentOne (Mistral-Hermes-Dolphin-7b)\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the Model Stock merge method using mistralai/Mistral-7B-v0.1 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.8-mistral-7b-v02\n* NousResearch/Hermes-2-Pro-Mistral-7B", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Llama-2-7b-chat-hf_fictional_arc_easy_english_v3\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 8\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 18", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #dataset-generator #base_model-meta-llama/Llama-2-7b-chat-hf #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Llama-2-7b-chat-hf_fictional_arc_easy_english_v3\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 8\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 18", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

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

[]

[]

[ "# CS505_COQE_viT5_train_Instruction0_SOPAL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_SOPAL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

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

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

[ "music", "rvc", "cam", "camaron", "marvel", "ochs", "model", "audio-to-audio", "en", "license:openrail", "region:us" ]

[]

[ "en" ]

[ "# Cam - Camaron Marvel Ochs [EN] (2020)", "# 354 Epochs - RVC V2 - rmvpe\nTrained on 11 minutes 36 seconds of isolated acapellas from The Otherside album using UVR (Voc FT + Reverb HQ) \nand Audacity to remove parts with double vocals and vocals from others (+Noise Gate)" ]

[ "TAGS\n#music #rvc #cam #camaron #marvel #ochs #model #audio-to-audio #en #license-openrail #region-us \n", "# Cam - Camaron Marvel Ochs [EN] (2020)", "# 354 Epochs - RVC V2 - rmvpe\nTrained on 11 minutes 36 seconds of isolated acapellas from The Otherside album using UVR (Voc FT + Reverb HQ) \nand Audacity to remove parts with double vocals and vocals from others (+Noise Gate)" ]

[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

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

[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

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

[ "1910.09700" ]

[]

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

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

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

[]

[]

[ "# CS505_COQE_viT5_train_Instruction0_OSPAL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_OSPAL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_userresponse_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.001_4iters_bs256_nodpo_only4w_userresponse_iter_3\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_userresponse_iter_2 on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_userresponse_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_4iters_bs256_nodpo_only4w_userresponse_iter_3\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_userresponse_iter_2 on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

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

[]

[]

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

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

[ "transformers", "tensorboard", "safetensors", "gemma", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:argilla/dpo-mix-7k", "base_model:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gemma #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-argilla/dpo-mix-7k #base_model-HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

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

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 3,002,557,440 parameters:\n\n\n\t+ 642,252,800 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 2560-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nZero-shot evaluations:\n\n\nSee this repository for JSON files: URL\n\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 3,002,557,440 parameters:\n\n\n\t+ 642,252,800 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 2560-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nZero-shot evaluations:\n\n\nSee this repository for JSON files: URL\n\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "transformers", "tensorboard", "safetensors", "gemma", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:GAIR/lima", "base_model:google/gemma-7b", "license:gemma", "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: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gemma #text-generation #alignment-handbook #trl #sft #generated_from_trainer #conversational #dataset-GAIR/lima #base_model-google/gemma-7b #license-gemma #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

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

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

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 3,002,557,440 parameters:\n\n\n\t+ 642,252,800 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 2560-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nZero-shot evaluations:\n\n\nSee this repository for JSON files: URL\n\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 3,002,557,440 parameters:\n\n\n\t+ 642,252,800 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 2560-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nZero-shot evaluations:\n\n\nSee this repository for JSON files: URL\n\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:davidberenstein1957/ultra-feedback-dutch-cleaned-hq-spin-geitje-7b-ultra-sft_iter1", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #generated_from_trainer #conversational #base_model-davidberenstein1957/ultra-feedback-dutch-cleaned-hq-spin-geitje-7b-ultra-sft_iter1 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

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

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[ "# CS505_COQE_viT5_train_Instruction0_ASPOL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_ASPOL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

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[ "# CS505_COQE_viT5_train_Instruction0_PSAOL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_PSAOL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

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

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 3,002,557,440 parameters:\n\n\n\t+ 642,252,800 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 2560-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nZero-shot evaluations:\n\n\nSee this repository for JSON files: URL\n\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "TAGS\n#gguf #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 3,002,557,440 parameters:\n\n\n\t+ 642,252,800 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 2560-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nZero-shot evaluations:\n\n\nSee this repository for JSON files: URL\n\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.0\n* Validation Loss: 2.2\n* Perplexity: 8.9\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test5.4-8B-10", "Kaoeiri/Keiana-L3-Test6-8B-16", "conversational", "base_model:Kaoeiri/Keiana-L3-Test5.4-8B-10", "base_model:Kaoeiri/Keiana-L3-Test6-8B-16", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Keiana-L3-Test6.1-8B-17\n\nKeiana-L3-Test6.1-8B-17 is a merge of the following models using LazyMergekit:", "# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.\n* Kaoeiri/Keiana-L3-Test5.4-8B-10\n* Kaoeiri/Keiana-L3-Test6-8B-16", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #Kaoeiri/Keiana-L3-Test5.4-8B-10 #Kaoeiri/Keiana-L3-Test6-8B-16 #conversational #base_model-Kaoeiri/Keiana-L3-Test5.4-8B-10 #base_model-Kaoeiri/Keiana-L3-Test6-8B-16 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Keiana-L3-Test6.1-8B-17\n\nKeiana-L3-Test6.1-8B-17 is a merge of the following models using LazyMergekit:", "# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.\n* Kaoeiri/Keiana-L3-Test5.4-8B-10\n* Kaoeiri/Keiana-L3-Test6-8B-16", "## Configuration", "## Usage" ]

[ "en", "region:us" ]

[]

[ "en" ]

[ "### Murabito A\n\n\n\n\n---\n\n\n* 1.5 models\n* Most of these models were only shared between friends\n* Models merged through MBW with quality in mind\n\n\n\n\n---\n\n\n\n\n\n\n\n\n\n\n---\n\n\nDue to the 'age' of these models I've already lost all their past generations, I only managed to salvage a few preview from old gens I posted on a private discord channel." ]

[ "TAGS\n#en #region-us \n", "### Murabito A\n\n\n\n\n---\n\n\n* 1.5 models\n* Most of these models were only shared between friends\n* Models merged through MBW with quality in mind\n\n\n\n\n---\n\n\n\n\n\n\n\n\n\n\n---\n\n\nDue to the 'age' of these models I've already lost all their past generations, I only managed to salvage a few preview from old gens I posted on a private discord channel." ]

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

[]

[]

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

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

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

[]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "TAGS\n#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-large-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

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

[]

[]

[ "# experiemnts\n\nThis model is a fine-tuned version of NousResearch/Meta-Llama-3-8B on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.6667\n- eval_runtime: 85.9166\n- eval_samples_per_second: 11.639\n- eval_steps_per_second: 1.455\n- epoch: 0.8005\n- step: 5184", "## 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: 1\n- eval_batch_size: 8\n- seed: 1024\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.1\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-NousResearch/Meta-Llama-3-8B #license-other #region-us \n", "# experiemnts\n\nThis model is a fine-tuned version of NousResearch/Meta-Llama-3-8B on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.6667\n- eval_runtime: 85.9166\n- eval_samples_per_second: 11.639\n- eval_steps_per_second: 1.455\n- epoch: 0.8005\n- step: 5184", "## 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: 1\n- eval_batch_size: 8\n- seed: 1024\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.1\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "transformers", "gguf", "not-for-all-audiences", "en", "dataset:v2ray/r-chatgpt-general-dump", "base_model:v2ray/SchizoGPT-8x22B", "license:mit", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #not-for-all-audiences #en #dataset-v2ray/r-chatgpt-general-dump #base_model-v2ray/SchizoGPT-8x22B #license-mit #endpoints_compatible #region-us \n" ]

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

[]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.001_4iters_bs256_nodpo_only4w_iter_2\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_iter_1 on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_iter_1 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_4iters_bs256_nodpo_only4w_iter_2\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_iter_1 on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]

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

[]

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[ "# CS505_COQE_viT5_train_Instruction0_SAOPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_SAOPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

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

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 7,069,016,064 parameters:\n\n\n\t+ 1,027,604,480 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 4096-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.3\n* Validation Loss: 2.9\n* Perplexity: 16\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 7,069,016,064 parameters:\n\n\n\t+ 1,027,604,480 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 4096-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.3\n* Validation Loss: 2.9\n* Perplexity: 16\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "diffusers", "yahoo-open-source-software-incubator", "arxiv:2404.10157", "license:apache-2.0", "region:us" ]

[ "2404.10157" ]

[]

[ "### Training\n\n\nThe following runs the training of text-to-image inpainting ControlNet initialized with the weights of \"stable-diffusion-2-inpainting\":\n\n\nThe following runs the training of text-to-image ControlNet initialized with the weights of \"stable-diffusion-2-base\":", "### Inference\n\n\nPlease refer to 'URL'. Tu run the code you need to download our model checkpoints.\n\n\nModels Checkpoints\n------------------\n\n\n\ns\n\n\nIf you found our work useful, please consider citing our paper:\n\n\nMaintainers\n-----------\n\n\n* Erfan Eshratifar: erfan.eshratifar@URL\n* Joao Soares: jvbsoares@URL\n\n\nLicense\n-------\n\n\nThis project is licensed under the terms of the Apache 2.0 open source license. Please refer to LICENSE for the full terms." ]

[ "TAGS\n#diffusers #yahoo-open-source-software-incubator #arxiv-2404.10157 #license-apache-2.0 #region-us \n", "### Training\n\n\nThe following runs the training of text-to-image inpainting ControlNet initialized with the weights of \"stable-diffusion-2-inpainting\":\n\n\nThe following runs the training of text-to-image ControlNet initialized with the weights of \"stable-diffusion-2-base\":", "### Inference\n\n\nPlease refer to 'URL'. Tu run the code you need to download our model checkpoints.\n\n\nModels Checkpoints\n------------------\n\n\n\ns\n\n\nIf you found our work useful, please consider citing our paper:\n\n\nMaintainers\n-----------\n\n\n* Erfan Eshratifar: erfan.eshratifar@URL\n* Joao Soares: jvbsoares@URL\n\n\nLicense\n-------\n\n\nThis project is licensed under the terms of the Apache 2.0 open source license. Please refer to LICENSE for the full terms." ]

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

[]

[]

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

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

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

[]

[]

[ "# CS505_COQE_viT5_train_Instruction0_OASPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_OASPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

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

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 7,069,016,064 parameters:\n\n\n\t+ 1,027,604,480 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 4096-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.3\n* Validation Loss: 2.9\n* Perplexity: 16\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "TAGS\n#transformers #safetensors #bloom #text-generation #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 7,069,016,064 parameters:\n\n\n\t+ 1,027,604,480 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 4096-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.3\n* Validation Loss: 2.9\n* Perplexity: 16\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test5.4-8B-10", "Kaoeiri/Keiana-L3-Test4.7-8B-3", "Kaoeiri/Keiana-L3-Test6-8B-16", "conversational", "base_model:Kaoeiri/Keiana-L3-Test5.4-8B-10", "base_model:Kaoeiri/Keiana-L3-Test4.7-8B-3", "base_model:Kaoeiri/Keiana-L3-Test6-8B-16", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Keiana-L3-Test6.2-8B-18\n\nKeiana-L3-Test6.2-8B-18 is a merge of the following models using LazyMergekit:", "# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.\n* Kaoeiri/Keiana-L3-Test5.4-8B-10\n* Kaoeiri/Keiana-L3-Test4.7-8B-3\n* Kaoeiri/Keiana-L3-Test6-8B-16", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #Kaoeiri/Keiana-L3-Test5.4-8B-10 #Kaoeiri/Keiana-L3-Test4.7-8B-3 #Kaoeiri/Keiana-L3-Test6-8B-16 #conversational #base_model-Kaoeiri/Keiana-L3-Test5.4-8B-10 #base_model-Kaoeiri/Keiana-L3-Test4.7-8B-3 #base_model-Kaoeiri/Keiana-L3-Test6-8B-16 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Keiana-L3-Test6.2-8B-18\n\nKeiana-L3-Test6.2-8B-18 is a merge of the following models using LazyMergekit:", "# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.\n* Kaoeiri/Keiana-L3-Test5.4-8B-10\n* Kaoeiri/Keiana-L3-Test4.7-8B-3\n* Kaoeiri/Keiana-L3-Test6-8B-16", "## Configuration", "## Usage" ]

[ "gguf", "merge", "mergekit", "lazymergekit", "llama-cpp", "gguf-my-repo", "license:apache-2.0", "region:us" ]

[]

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[ "# DavidAU/DeepSeek-Coder-Instruct-8x1.3b-Q8_0-GGUF\nThis model was converted to GGUF format from 'SanjiWatsuki/DeepSeek-Coder-Instruct-8x1.3b' 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 #license-apache-2.0 #region-us \n", "# DavidAU/DeepSeek-Coder-Instruct-8x1.3b-Q8_0-GGUF\nThis model was converted to GGUF format from 'SanjiWatsuki/DeepSeek-Coder-Instruct-8x1.3b' 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", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]

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

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

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

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[]

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

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

[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

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

[ "TAGS\n#transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Meta-Llama-3-8B-Instruct_fictional_Spanish_v1\n\nThis model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 36", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #dataset-generator #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Meta-Llama-3-8B-Instruct_fictional_Spanish_v1\n\nThis model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 36", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: sjonas50\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: sjonas50\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

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

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

[]

[ "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 7,069,016,064 parameters:\n\n\n\t+ 1,027,604,480 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 4096-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.3\n* Validation Loss: 2.9\n* Perplexity: 16\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "TAGS\n#gguf #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #region-us \n", "### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n-------------", "### Basics\n\n\n*This section provides information for anyone who wants to know about the model.*\n\n\n\nClick to expand \n\nDeveloped by: BigScience (website)\n\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n\n\nModel Type: Transformer-based Language Model\n\n\nVersion: 1.0.0\n\n\nLanguages: Multiple; see training data\n\n\nLicense: RAIL License v1.0 (link)\n\n\nRelease Date Estimate: Monday, 11.July.2022\n\n\nSend Questions to: bigscience-contact@URL\n\n\nCite as: BigScience, *BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model*. International, May 2021-May 2022\n\n\nFunded by:\n\n\n* The French government.\n* Hugging Face (website).\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*", "### Technical Specifications\n\n\n*This section provides information for people who work on model development.*\n\n\n\nClick to expand \n\nPlease see the BLOOM training README for full details on replicating training.\n\n\nModel Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n\n\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 7,069,016,064 parameters:\n\n\n\t+ 1,027,604,480 embedding parameters\n\t+ 30 layers, 32 attention heads\n\t+ Hidden layers are 4096-dimensional\n\t+ Sequence length of 2048 tokens used (see BLOOM tokenizer, tokenizer description)\n\n\nObjective Function: Cross Entropy with mean reduction (see API documentation).\n\n\nCompute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).\n\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n\n\n\t+ Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\t+ 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\t+ CPU: AMD\n\t+ CPU memory: 512GB per node\n\t+ GPU memory: 640GB per node\n\t+ Inter-node connect: Omni-Path Architecture (OPA)\n\t+ NCCL-communications network: a fully dedicated subnet\n\t+ Disc IO network: shared network with other types of nodes\n* Software:\n\n\n\t+ Megatron-DeepSpeed (Github link)\n\t+ DeepSpeed (Github link)\n\t+ PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)\n\t+ apex (Github link)", "#### Training\n\n\nTraining logs: Tensorboard link\n\n\n* Number of epochs: 1 (*current target*)\n* Dates:\n\n\n\t+ Started 11th March, 2022 11:42am PST\n\t+ Ended 5th July, 2022\n* Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n* Server training location: Île-de-France, France", "#### Tokenization\n\n\nThe BLOOM tokenizer (link) is a learned subword tokenizer trained using:\n\n\n* A byte-level Byte Pair Encoding (BPE) algorithm\n* A simple pre-tokenization rule, no normalization\n* A vocabulary size of 250,680\n\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.", "### Environmental Impact\n\n\n\nClick to expand \n\nThe training supercomputer, Jean Zay (website), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n\n\nEstimated carbon emissions: *(Forthcoming upon completion of training.)*\n\n\nEstimated electricity usage: *(Forthcoming upon completion of training.)*\n\n\n\n \n\n\nUses\n----\n\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.\nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n\nClick to expand", "### Intended Use\n\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.", "#### Direct Use\n\n\n* Text generation\n* Exploring characteristics of language generated by a language model\n\n\n\t+ Examples: Cloze tests, counterfactuals, generations with reframings", "#### Downstream Use\n\n\n* Tasks that leverage language models include: Information Extraction, Question Answering, Summarization", "### Misuse and Out-of-scope Use\n\n\n*This section addresses what users ought not do with the model.*\n\n\nSee the BLOOM License, Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.", "#### Out-of-scope Uses\n\n\nUsing the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.", "##### Out-of-scope Uses Include:\n\n\n* Usage in biomedical domains, political and legal domains, or finance domains\n* Usage for evaluating or scoring individuals, such as for employment, education, or credit\n* Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct", "#### Misuse\n\n\nIntentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:\n\n\n* Spam generation\n* Disinformation and influence operations\n* Disparagement and defamation\n* Harassment and abuse\n* Deception\n* Unconsented impersonation and imitation\n* Unconsented surveillance\n* Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions", "### Intended Users", "#### Direct Users\n\n\n* General Public\n* Researchers\n* Students\n* Educators\n* Engineers/developers\n* Non-commercial entities\n* Community advocates, including human and civil rights groups", "#### Indirect Users\n\n\n* Users of derivatives created by Direct Users, such as those using software with an intended use\n* Users of Derivatives of the Model, as described in the License", "#### Others Affected (Parties Prenantes)\n\n\n* People and groups referred to by the LLM\n* People and groups exposed to outputs of, or decisions based on, the LLM\n* People and groups whose original work is included in the LLM\n\n\n\n \n\n\nTraining Data\n-------------\n\n\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\nClick to expand \n\nDetails for each dataset are provided in individual Data Cards.\n\n\nTraining data includes:\n\n\n* 45 natural languages\n* 12 programming languages\n* In 1.5TB of pre-processed text, converted into 350B unique tokens (see the tokenizer section for more.)", "#### Languages\n\n\nThe pie chart shows the distribution of languages in training data.\n\n\n!pie chart showing the distribution of languages in training data\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n\n\n\nClick to expand \n\n\n\nThe following table shows the distribution of programming languages.\n\n\n\nClick to expand \n\nExtension: java, Language: Java, Number of files: 5,407,724\nExtension: php, Language: PHP, Number of files: 4,942,186\nExtension: cpp, Language: C++, Number of files: 2,503,930\nExtension: py, Language: Python, Number of files: 2,435,072\nExtension: js, Language: JavaScript, Number of files: 1,905,518\nExtension: cs, Language: C#, Number of files: 1,577,347\nExtension: rb, Language: Ruby, Number of files: 6,78,413\nExtension: cc, Language: C++, Number of files: 443,054\nExtension: hpp, Language: C++, Number of files: 391,048\nExtension: lua, Language: Lua, Number of files: 352,317\nExtension: go, Language: GO, Number of files: 227,763\nExtension: ts, Language: TypeScript, Number of files: 195,254\nExtension: C, Language: C, Number of files: 134,537\nExtension: scala, Language: Scala, Number of files: 92,052\nExtension: hh, Language: C++, Number of files: 67,161\nExtension: H, Language: C++, Number of files: 55,899\nExtension: tsx, Language: TypeScript, Number of files: 33,107\nExtension: rs, Language: Rust, Number of files: 29,693\nExtension: phpt, Language: PHP, Number of files: 9,702\nExtension: c++, Language: C++, Number of files: 1,342\nExtension: h++, Language: C++, Number of files: 791\nExtension: php3, Language: PHP, Number of files: 540\nExtension: phps, Language: PHP, Number of files: 270\nExtension: php5, Language: PHP, Number of files: 166\nExtension: php4, Language: PHP, Number of files: 29\n\n\n\n\n \n\n\nRisks and Limitations\n---------------------\n\n\n*This section identifies foreseeable harms and misunderstandings.*\n\n\n\nClick to expand \n\nModel may:\n\n\n* Overrepresent some viewpoints and underrepresent others\n* Contain stereotypes\n* Contain personal information\n* Generate:\n\n\n\t+ Hateful, abusive, or violent language\n\t+ Discriminatory or prejudicial language\n\t+ Content that may not be appropriate for all settings, including sexual content\n* Make errors, including producing incorrect information as if it were factual\n* Generate irrelevant or repetitive outputs\n\n\n\n \n\n\nEvaluation\n----------\n\n\n*This section describes the evaluation protocols and provides the results.*\n\n\n\nClick to expand", "### Metrics\n\n\n*This section describes the different ways performance is calculated and why.*\n\n\nIncludes:\n\n\n\nAnd multiple different metrics for specific tasks. *(More evaluation metrics forthcoming upon completion of evaluation protocol.)*", "### Factors\n\n\n*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n\n* Language, such as English or Yoruba\n* Domain, such as newswire or stories\n* Demographic characteristics, such as gender or nationality", "### Results\n\n\n*Results are based on the Factors and Metrics.*\n\n\nTrain-time Evaluation:\n\n\nAs of 25.May.2022, 15:00 PST:\n\n\n* Training Loss: 2.3\n* Validation Loss: 2.9\n* Perplexity: 16\n\n\n\n \n\n\nRecommendations\n---------------\n\n\n*This section provides information on warnings and potential mitigations.*\n\n\n\nClick to expand \n\n* Indirect users should be made aware when the content they're working with is created by the LLM.\n* Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.\n* Models pretrained with the LLM should include an updated Model Card.\n* Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n \n\n\nGlossary and Calculations\n-------------------------\n\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\nClick to expand \n\n* Loss: A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss.\n* Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.\n* High-stakes settings: Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed Artificial Intelligence (AI) Act.\n* Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.\n* Human rights: Includes those rights defined in the Universal Declaration of Human Rights.\n* Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as \"personal data\" in the European Union's General Data Protection Regulation; and \"personal information\" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.\n* Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)\n* Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n\n \n\n\nMore Information\n----------------\n\n\n\nClick to expand", "### Dataset Creation\n\n\nBlog post detailing the design choices during the dataset creation: URL", "### Technical Specifications\n\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: URL\n\n\nMore details on the architecture/optimizer: URL\n\n\nBlog post on the hardware/engineering side: URL\n\n\nDetails on the distributed setup used for the training: URL\n\n\nTensorboard updated during the training: URL\n\n\nInsights on how to approach training, negative results: URL\n\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): URL", "### Initial Results\n\n\nInitial prompting experiments using interim checkpoints: URL\n\n\n\n \n\n\nModel Card Authors\n------------------\n\n\n*Ordered roughly chronologically and by amount of time spent.*\n\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff" ]

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "alpaca", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### Merge Method\n\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\n\nThe following models were included in the merge:\n\n\n* G:\\FModels\\IceCoffeeTest10\n* G:\\FModels\\IceCoffeeTest5", "### Configuration\n\n\nThe following YAML configuration was used to produce this model:\n\n\nHow to download From the command line\n-------------------------------------\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nTo download the 'main' branch to a folder called 'IceCoffeeRP-7b':\n\n\n\nMore advanced huggingface-cli download usage\nIf you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\n\nThe cache location can be changed with the 'HF\\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Merge Method\n\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\n\nThe following models were included in the merge:\n\n\n* G:\\FModels\\IceCoffeeTest10\n* G:\\FModels\\IceCoffeeTest5", "### Configuration\n\n\nThe following YAML configuration was used to produce this model:\n\n\nHow to download From the command line\n-------------------------------------\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nTo download the 'main' branch to a folder called 'IceCoffeeRP-7b':\n\n\n\nMore advanced huggingface-cli download usage\nIf you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\n\nThe cache location can be changed with the 'HF\\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-2b-it", "license:apache-2.0", "region:us" ]

[ "1910.09700" ]

[]

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

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/gemma-2b-it #license-apache-2.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.0" ]

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2403.19522", "base_model:cognitivecomputations/samantha-1.2-mistral-7b", "base_model:mistralai/Mistral-7B-v0.1", "base_model:uukuguy/speechless-code-mistral-7b-v1.0", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2403.19522" ]

[]

[ "# ExperimentV2 AKA NerdySamanthaV2 (Mistral v0.1 & Samantha v1.2 & Speechless Code Mistral v1.0 7B)\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the Model Stock merge method using mistralai/Mistral-7B-v0.1 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/samantha-1.2-mistral-7b\n* uukuguy/speechless-code-mistral-7b-v1.0", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2403.19522 #base_model-cognitivecomputations/samantha-1.2-mistral-7b #base_model-mistralai/Mistral-7B-v0.1 #base_model-uukuguy/speechless-code-mistral-7b-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ExperimentV2 AKA NerdySamanthaV2 (Mistral v0.1 & Samantha v1.2 & Speechless Code Mistral v1.0 7B)\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the Model Stock merge method using mistralai/Mistral-7B-v0.1 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/samantha-1.2-mistral-7b\n* uukuguy/speechless-code-mistral-7b-v1.0", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

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

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[ "# CS505_COQE_viT5_train_Instruction0_POSAL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_POSAL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[]

[]

[ "# CS505_COQE_viT5_train_Instruction0_AOSPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_AOSPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

[]

[]

[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

[ "peft", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-v0.1-GPTQ", "license:apache-2.0", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.004\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\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\\_steps: 2\n* num\\_epochs: 11\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.1\n* Pytorch 2.0.1+cu118\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-TheBloke/Mistral-7B-v0.1-GPTQ #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.004\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\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\\_steps: 2\n* num\\_epochs: 11\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.1\n* Pytorch 2.0.1+cu118\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[ "1910.09700" ]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[]

[]

[ "# CS505_COQE_viT5_train_Instruction0_OAPSL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_OAPSL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

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

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

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

[]

[]

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

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

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

[]

[ "en" ]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

[ "transformers", "pytorch", "llama", "text-generation", "miqu", "70b model", "cat", "miqu cat", "conversational", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[ "## Welcome to Miqu Cat: A 70B Miqu Lora Fine-Tune\n\nIntroducing Miqu Cat, an advanced model fine-tuned by Dr. Kal'tsit then quanted for the the ExllamaV2 project, bringing the model down to an impressive 4.8 bits per weight (bpw). This fine-tuning allows those with limited computational resources to explore its capabilities without compromise.", "### Competitive Edge - *meow!*\n\nMiqu Cat stands out in the arena of Miqu fine-tunes, consistently performing admirably in tests and comparisons. It’s crafted to be less restrictive and more robust than its predecessors and variants, making it a versatile tool in AI-driven applications. \n48GB VRAM to load the model for 8192 Context Length *[\"2x3090\", \"1xA6000\", \"1xA100 80GB\", \"etc.\"]*", "### How to Use Miqu Cat: The Nitty-Gritty\n\nMiqu Cat operates on the CHATML prompt format, designed for straightforward and effective interaction. Whether you're integrating it into existing systems or using it for new projects, its flexible prompt structure facilitates ease of use.", "### Training Specs\n\n- Dataset: 1.5 GB\n- Compute: Dual setup of 8xA100 nodes\n- Duration: Approximately 1000 hours of intensive training", "### Meet the Author\n\nDr. Kal'tsit has been at the forefront of this fine-tuning process, ensuring that Miqu Cat gives the user a unique feel." ]

[ "TAGS\n#transformers #pytorch #llama #text-generation #miqu #70b model #cat #miqu cat #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Welcome to Miqu Cat: A 70B Miqu Lora Fine-Tune\n\nIntroducing Miqu Cat, an advanced model fine-tuned by Dr. Kal'tsit then quanted for the the ExllamaV2 project, bringing the model down to an impressive 4.8 bits per weight (bpw). This fine-tuning allows those with limited computational resources to explore its capabilities without compromise.", "### Competitive Edge - *meow!*\n\nMiqu Cat stands out in the arena of Miqu fine-tunes, consistently performing admirably in tests and comparisons. It’s crafted to be less restrictive and more robust than its predecessors and variants, making it a versatile tool in AI-driven applications. \n48GB VRAM to load the model for 8192 Context Length *[\"2x3090\", \"1xA6000\", \"1xA100 80GB\", \"etc.\"]*", "### How to Use Miqu Cat: The Nitty-Gritty\n\nMiqu Cat operates on the CHATML prompt format, designed for straightforward and effective interaction. Whether you're integrating it into existing systems or using it for new projects, its flexible prompt structure facilitates ease of use.", "### Training Specs\n\n- Dataset: 1.5 GB\n- Compute: Dual setup of 8xA100 nodes\n- Duration: Approximately 1000 hours of intensive training", "### Meet the Author\n\nDr. Kal'tsit has been at the forefront of this fine-tuning process, ensuring that Miqu Cat gives the user a unique feel." ]

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

[]

[]

[ "# CS505_COQE_viT5_train_Instruction0_POASL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_POASL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[]

[]

[ "# CS505_COQE_viT5_train_Instruction0_AOPSL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_AOPSL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

[ "transformers", "safetensors", "mistral", "text-generation", "nsfw", "not-for-all-audiences", "dataset:ResplendentAI/NSFW_RP_Format_NoQuote", "dataset:flammenai/Prude-Phi3-DPO", "base_model:flammenai/flammen21-mistral-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# flammen21X-mistral-7B\n\nA Mistral 7B LLM built from merging pretrained models and finetuning on flammenai/Prude-Phi3-DPO. \nFlammen specializes in exceptional character roleplay, creative writing, and general intelligence.", "### Method\n\nFinetuned using an L4 on Google Colab.\n\nFine-tune a Mistral-7b model with Direct Preference Optimization", "### Configuration\n\nLoRA, model, and training settings:" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #nsfw #not-for-all-audiences #dataset-ResplendentAI/NSFW_RP_Format_NoQuote #dataset-flammenai/Prude-Phi3-DPO #base_model-flammenai/flammen21-mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# flammen21X-mistral-7B\n\nA Mistral 7B LLM built from merging pretrained models and finetuning on flammenai/Prude-Phi3-DPO. \nFlammen specializes in exceptional character roleplay, creative writing, and general intelligence.", "### Method\n\nFinetuned using an L4 on Google Colab.\n\nFine-tune a Mistral-7b model with Direct Preference Optimization", "### Configuration\n\nLoRA, model, and training settings:" ]