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docs/df.csv

@@ -24,6 +24,7 @@ https://huggingface.co/jondurbin/airoboros-l2-70b-gpt4-1.4.1,,Llama2,Airoboros (
 https://github.com/THUDM/ChatGLM-6B/blob/main/README_en.md,"From the readme, ""ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese QA and dialogue. The model is trained for about 1T tokens of Chinese and English corpus, supplemented by supervised fine-tuning, feedback bootstrap, and reinforcement learning wit human feedback. With only about 6.2 billion parameters, the model is able to generate answers that are in line with human preference.""",GLM (own),Unspecified,Apache 2.0,THUDM,https://github.com/THUDM,Knowledge Engineering Group (KEG) & Data Mining at Tsinghua University,partial,https://github.com/THUDM/ChatGLM-6B/blob/main/README_en.md#deployment,Some code made available on Github,partial,http://doi.org/10.18653/v1/2022.acl-long.26,"Training data not centrally made available, but described in 2022 ACL paper, appears to be mostly public datasets",open,https://huggingface.co/THUDM/chatglm-6b/tree/main,Model made available through HuggingFace,closed,,"docs mention ""supervised fine-tuning, feedback bootstrap, and reinforcement learning wit human feedback"", but none of the datasets used are clearly specified.",closed,,No weights or checkpoints corresponding to the delta of the LLM vs RLHF provided,open,https://github.com/THUDM/ChatGLM-6B/blob/main/LICENSE,Apache 2.0,partial,https://github.com/THUDM/ChatGLM-6B/blob/main/ptuning/README_en.md,"Some documentation available, but a lot of code is not commented or explained.",partial,,Full details of architecture not specified in a single place,closed,,,partial,https://aclanthology.org/2022.acl-long.26/,"ACL 2022 paper describes the training of the GLM base model, but the RLHF portion is more recent (there is also a related ICLR paper for a newer generation https://openreview.net/forum?id=-Aw0rrrPUF)",closed,https://huggingface.co/THUDM/chatglm-6b,No modelcard; the HuggingFace modelcard spot is used just as the homepage for the model.,closed,,No datasheet,closed,,No package,open,https://github.com/THUDM/ChatGLM-6B/blob/main/README_en.md#api-deployment,API provided through fastapi uvicorn,/projects/ChatGLM-6B.yaml,5.5
 https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1,,unclear,unspecified,Apache 2.0,Mistral AI,https://mistral.ai/,,partial,https://github.com/mistralai/mistral-src,repository provides 'minimal code to run our 7B model',closed,,No information provided on pretraining data,open,https://github.com/mistralai/mistral-src#download-the-model,Base LLM model made available for download,closed,https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1,No information provided expect that instruction tuning is done using an unspecified 'variety of publicly available conversation datasets',partial,https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/tree/main,Instruct version of the model made available but no information on fine-tuning procedure provided,open,https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/README.md,Apache 2.0,closed,https://github.com/mistralai/mistral-src,the little code that is available is uncommented and undocumented,partial,https://github.com/mistralai/mistral-src,Some information on architecture provided in github repo,partial,http://arxiv.org/abs/2310.06825,"Preprint rehashes marketing blurbs also given in blog and provides no details about pretraining datasets, instruction tuning datasets, or fine-tuning process, hence partial.",closed,,No peer reviewed paper available,closed,,"No model card available, HuggingFace modelcard just points to a corporate blog post",closed,,No datasheet available,partial,https://docs.mistral.ai/quickstart/,Docker image shared on github,open,https://docs.mistral.ai/api,API specification provided by vLLM,/projects/mistral-7B.yaml,5.5
 https://github.com/nlpxucan/WizardLM,Empowering Large Pre-Trained Language Models to Follow Complex Instructions,LLaMA-7B,Evol-Instruct (synthetic),CC-BY-NC-4.0,Microsoft & Peking University,https://github.com/nlpxucan,,partial,https://github.com/nlpxucan/WizardLM/tree/main/WizardLM,Fast-evolving repository contains WizardLM code,partial,,"Based on LLaMA, which is claimed to be public but nowhere exactly documented.",closed,,"Based on LLaMA weights, which are not openly available though a leaked versions is in wide circulation.",open,https://github.com/nlpxucan/WizardLM/tree/main/WizardLM#training-data,The Evol-Instruct dataset contains 70k instruction-following sequences generated from Evol-Instruct,partial,https://huggingface.co/WizardLM/WizardLM-7B-V1.0/tree/main,Model weights offered as a delta to LLaMA,partial,https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/MODEL_DIFF_LICENSE,"Restricted for academic research purposes only. Code and Model diff release under CC-BY-NC-4.0, software code under Apache 2.0",partial,https://github.com/nlpxucan/WizardLM/tree/main/WizardLM,"Code is only partially documented, not clearly versioned, and appears to be in flux.",open,https://arxiv.org/abs/2304.12244,Architecture described in preprint and partly accessible in code repository,open,https://arxiv.org/abs/2304.12244,Preprint describes method for creating large amounts of LLM-based synthetic RLHF data and fine-tuning WizardLM based on it,closed,,No peer-reviewed paper or data audit found,closed,https://huggingface.co/WizardLM/WizardLM-7B-V1.0,Model card is only a placeholder and generates an error (missing yaml metadata),closed,https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k,Dataset card for Evol-Instruct generates an error,closed,,No package available,closed,,No API available,/projects/wizardlm-7B-V1.yaml,5.5
+https://mistral.ai/news/mistral-nemo/,,Mistral NeMo,unspecified,Apache 2.0 (model weights only),Mistral AI,https://mistral.ai/,,partial,https://github.com/mistralai/mistral-inference,repository provides 'minimal code to run our models',closed,,No information provided on pretraining data,open,https://models.mistralcdn.com/mistral-nemo-2407/mistral-nemo-base-2407.tar,Base LLM model made available for download,closed,https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1,No information provided expect that instruction tuning is done using an unspecified 'variety of publicly available conversation datasets',partial,https://models.mistralcdn.com/mistral-nemo-2407/mistral-nemo-instruct-2407.tar,Instruct version of the model made available but no information on fine-tuning procedure provided,open,https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/README.md,Apache 2.0,closed,https://github.com/mistralai/mistral-inference,the little code that is available is uncommented and undocumented,partial,https://github.com/mistralai/mistral-inference,Some information on architecture provided in github repo and in release blogpost,closed,,No preprint found,closed,,No peer reviewed paper available,closed,,No model card available,closed,,No datasheet available,partial,https://docs.mistral.ai/quickstart/,Docker image shared on github,open,https://docs.mistral.ai/api,API specification provided by vLLM,/projects/mistral-nemo.yaml,5.0
 https://qwenlm.github.io/blog/qwen1.5/,"This is based on the 72B version, the largest of 8 available model sizes.",QwenLM,Unspecified,Qianwen License,Alibaba Cloud,,Qwen (abbr. for Tongyi Qianwen 通义千问) refers to the large language model family built by Alibaba Cloud,partial,https://github.com/QwenLM/Qwen1.5/,Repository provides sparse source code and some examples for SFT,closed,,Pretraining data not specified or documented.,open,https://huggingface.co/Qwen/Qwen1.5-72B/tree/main,Also available in smaller model sizes,closed,https://qwen.readthedocs.io/en/latest/training/SFT/llama_factory.html,Data not specified or documented. Some example code in repo provides directions but no details.,open,https://huggingface.co/Qwen/Qwen1.5-72B-Chat/tree/main,Also available in smaller model sizes,closed,,Qianwen License,partial,,Repository is fairly well-documented.,partial,,No clear description of architecture found.,closed,,No preprint found.,closed,,No peer-reviewed paper found.,closed,,"Model card on HF only serves as a pointer to the model, no actual info provided.",closed,,No datasheet.,partial,,No specific package provided but integrates well with many widely used packages,open,,Available through various APIs,/projects/qwen-1.5-chat.yaml,5.0
 https://huggingface.co/CarperAI/stable-vicuna-13b-delta,StableVicuna-13B is a Vicuna-13B v0 model fine-tuned using reinforcement learning from human feedback (RLHF) via Proximal Policy Optimization (PPO) on various conversational and instructional datasets,LLaMA,"OASST1 (human), GPT4All (human), Alpaca (synthetic)",,CarperAI,https://carper.ai,,partial,https://huggingface.co/CarperAI/stable-vicuna-13b-delta/tree/main,Some elements of the code made available through HuggingFace,closed,https://huggingface.co/CarperAI/stable-vicuna-13b-delta,Based on LLaMA whose pretraining data has nowhere been disclosed or documented.,partial,https://huggingface.co/CarperAI/stable-vicuna-13b-delta#apply-delta-weights,"Model not functional out of the box as weights require a delta computation. From the docs 'StableVicuna-13B cannot be used from the CarperAI/stable-vicuna-13b-delta weights alone. To obtain the correct model, one must add back the difference between LLaMA 13B and CarperAI/stable-vicuna-13b-delta weights.'",partial,https://huggingface.co/CarperAI/stable-vicuna-13b-delta,"From the documentation 'The reward model used during RLHF was also trained on OpenAssistant Conversations Dataset (OASST1) along with two other datasets Anthropic HH-RLHF, a dataset of preferences about AI assistant helpfulness and harmlessness; and Stanford Human Preferences Dataset a dataset of 385K collective human preferences over responses to questions/instructions in 18 different subject areas, from cooking to legal advice.'",partial,https://huggingface.co/CarperAI/stable-vicuna-13b-delta/discussions/7,The HuggingFace community page has an open question for release of the RL model,partial,https://huggingface.co/CarperAI/stable-vicuna-13b-delta,"CC-BY-NC-SA-4.0. License for LLaMA is more murky, hence partial. As they say 'License for the base LLaMA model's weights is Meta's non-commercial bespoke license.'",partial,https://huggingface.co/CarperAI/stable-vicuna-13b-delta/tree/main,"Code is minimally documented and deployment requires non-trivial configuration, e.g. 'StableVicuna-13B cannot be used from the CarperAI/stable-vicuna-13b-delta weights alone. To obtain the correct model, one must add back the difference between LLaMA 13B and CarperAI/stable-vicuna-13b-delta weights.'",partial,,"Architecture is described in scattered places, but there is no clear and exhaustive overview.",partial,https://arxiv.org/abs/2302.13971,"Preprint covers only the LLaMA base model, hence partial.",closed,,No paper found.,partial,https://huggingface.co/lmsys/vicuna-13b-delta-v0,Model card provides some information but is not fully worked out as recommended in model card literature.,closed,,No datasheet found,closed,,No package found,partial,https://github.com/lm-sys/FastChat/tree/main#api,Addressable via FastChat / HuggingFace API,/projects/stablevicuna.yaml,5.0
 https://huggingface.co/tiiuae/falcon-40b-instruct,,Falcon 40B,Baize (synthetic),Apache 2.0 license,Technology Innovation Institute,https://falconllm.tii.ae,,closed,https://huggingface.co/tiiuae/falcon-40b-instruct,"No source code shared, even though the term ""open source"" is used.",partial,https://huggingface.co/datasets/tiiuae/falcon-refinedweb,"From the documentation 'The key ingredient for the high quality of the Falcon models is their training data, predominantly based (>80%) on RefinedWeb — a novel massive web dataset based on CommonCrawl' (https://huggingface.co/blog/falcon). However, only a small sample is made available.",open,https://huggingface.co/tiiuae/falcon-40b-instruct/tree/main,Model weights available through HuggingFace library,partial,https://github.com/project-baize/baize-chatbot,RL data inherited from Baize but provenance not well-documented. From the documentation 'Falcon-40B-Instruct was finetuned on a 150M tokens from Baize mixed with 5% of RefinedWeb data.',closed,https://github.com/project-baize/baize-chatbot#v1,No RL weights or checkpoints made available,open,,First release came with a legally murky license that was swiftly criticised and now generates a 404. Current documentation 'Falcon-40B-Instruct is made available under the Apache 2.0 license.',closed,,"No source code found, therefore no documentation found.",partial,,"Architecture sketched on HuggingFace as ""Falcon-40B-Instruct is a 40B parameters causal decoder-only model built by TII based on Falcon-40B and finetuned on a mixture of Baize.""",partial,https://arxiv.org/abs/2306.01116,"Preprint covers the creation and curation of RefinedWeb dataset, but not other aspects of the model, hence partial.",closed,,No peer-reviewed paper known.,partial,https://huggingface.co/tiiuae/falcon-40b-instruct,"Model card on HuggingFace is mostly used to advertise the model, not to document its training and evaluation details.",closed,,There is no datasheet available.,closed,,There is no package.,closed,https://huggingface.co/tiiuae/falcon-40b-instruct,"There is no API, and HuggingFace inference API is disabled for this model.",/projects/Falcon-40B-instruct.yaml,4.5