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docs/df.csv

@@ -1,5 +1,5 @@
 project.link,project.notes,project.llmbase,project.rlbase,project.license,org.name,org.link,org.notes,opencode.class,opencode.link,opencode.notes,llmdata.class,llmdata.link,llmdata.notes,llmweights.class,llmweights.link,llmweights.notes,rldata.class,rldata.link,rldata.notes,rlweights.class,rlweights.link,rlweights.notes,license.class,license.link,license.notes,code.class,code.link,code.notes,architecture.class,architecture.link,architecture.notes,preprint.class,preprint.link,preprint.notes,paper.class,paper.link,paper.notes,modelcard.class,modelcard.link,modelcard.notes,datasheet.class,datasheet.link,datasheet.notes,package.class,package.link,package.notes,api.class,api.link,api.notes,source.file,openness
-https://blog.allenai.org/olmo-open-language-model-87ccfc95f580,,OLMo 7B,OpenInstruct,Apache 2.0,AllenAI,https://allenai.org/allennlp,Slogan is AI for the common good,open,https://github.com/allenai/OLMo,"Multiple repos with training, architecture and fine-tuning code available",open,https://huggingface.co/datasets/allenai/dolma,Dolma training data released and documented in exemplary way,open,https://huggingface.co/collections/allenai/olmo-suite-65aeaae8fe5b6b2122b46778,OLMo 7B and many training checkpoints available,open,https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned,Instruction tuning datasets documented and made available in exemplary ways,open,https://huggingface.co/allenai/OLMo-7B-Instruct/tree/main,Full model weights made available,open,https://huggingface.co/allenai/OLMo-7B-Instruct,Apache 2.0,open,https://huggingface.co/allenai/OLMo-7B-Instruct#model-sources,"repositories and code well-described, commented and documented",open,https://huggingface.co/allenai/OLMo-7B-Instruct#model-sources,"Architectured documented in detail in model card, preprint, and technical blog posts",open,https://arxiv.org/abs/2402.00838,"Preprint describes model architecture, training and fine-tuning data, and training and SFT pipelines",closed,,No peer-reviewed paper found,open,https://huggingface.co/allenai/OLMo-7B-Instruct,Model card provides broad overview and links to full details,open,https://huggingface.co/datasets/allenai/dolma,"Data sheets and documentation available for the datasets used, linked here is Dolma",open,https://pypi.org/project/ai2-olmo/,AI2-olmo available on PyPi,partial,https://huggingface.co/allenai/OLMo-7B-hf,Available through HuggingFace though model is too large to run on free inference API,/projects/olmo-7b-instruct.yaml,12.5
+https://blog.allenai.org/olmo-open-language-model-87ccfc95f580,,OLMo 7B,OpenInstruct,Apache 2.0,Ai2,https://allenai.org,Allen Institute for AI (non-profit research institute),open,https://github.com/allenai/OLMo,"Multiple repos with training, architecture and fine-tuning code available",open,https://huggingface.co/datasets/allenai/dolma,Dolma training data released and documented in exemplary way,open,https://huggingface.co/collections/allenai/olmo-suite-65aeaae8fe5b6b2122b46778,OLMo 7B and many training checkpoints available,open,https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned,Instruction tuning datasets documented and made available in exemplary ways,open,https://huggingface.co/allenai/OLMo-7B-Instruct/tree/main,Full model weights made available,open,https://huggingface.co/allenai/OLMo-7B-Instruct,Apache 2.0,open,https://huggingface.co/allenai/OLMo-7B-Instruct#model-sources,"repositories and code well-described, commented and documented",open,https://huggingface.co/allenai/OLMo-7B-Instruct#model-sources,"Architectured documented in detail in model card, preprint, and technical blog posts",open,https://arxiv.org/abs/2402.00838,"Preprint describes model architecture, training and fine-tuning data, and training and SFT pipelines",closed,,No peer-reviewed paper found,open,https://huggingface.co/allenai/OLMo-7B-Instruct,Model card provides broad overview and links to full details,open,https://huggingface.co/datasets/allenai/dolma,"Data sheets and documentation available for the datasets used, linked here is Dolma",open,https://pypi.org/project/ai2-olmo/,AI2-olmo available on PyPi,partial,https://huggingface.co/allenai/OLMo-7B-hf,Available through HuggingFace though model is too large to run on free inference API,/projects/olmo-7b-instruct.yaml,12.5
 https://huggingface.co/bigscience/bloomz,,"BLOOMZ, mT0",xP3,Apache 2.0 and RAIL (responsible AI license),bigscience-workshop,https://github.com/bigscience-workshop,,open,https://github.com/bigscience-workshop/xmtf,Repository provides a guided overview to all components,open,https://github.com/bigscience-workshop/xmtf#data,Data made available & documented in detail in repo and preprint,open,https://github.com/bigscience-workshop/xmtf#models,Model made available on github,open,https://huggingface.co/datasets/bigscience/xP3all,From the documentation 'xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks',partial,https://huggingface.co/bigscience/bloomz-optimizer-states/tree/main,Fine-tuned checkpoint available for download,partial,https://bigscience.huggingface.co/blog/the-bigscience-rail-license,"Code licensed under Apache 2.0, model under bespoke 'Responsible AI License' which imposes some limitations",open,https://github.com/bigscience-workshop/xmtf,Code well documented and actively maintained,open,https://github.com/bigscience-workshop/xmtf#create-xp3x,"Architecture described in preprint, code available in github repo, recipe on HuggingFace",open,https://arxiv.org/abs/2211.05100,Preprint (updated June 2023) of 65 pages + 10 page appendix,open,https://aclanthology.org/2023.acl-long.891/,Peer-reviewed paper of 9 pages + 114 page appendix describes the multitask finetuning (instruction tuning) of BLOOM (see preprint) to form BLOOMZ,open,https://huggingface.co/bigscience/bloomz,Model card,open,https://huggingface.co/datasets/bigscience/xP3,Dataset documented in dataset card at HuggingFace,closed,,No packages published,open,https://huggingface.co/spaces/bigscience/petals-api,Petals API via HuggingFace not always available ('not enough hardware capacity'),/projects/bloomz.yaml,12.0
 https://huggingface.co/LLM360/AmberChat,,Amber,ShareGPT + Evol-Instruct (synthetic),Apache 2.0,LLM360,https://www.llm360.ai/index.html,"LLM360, an initiative to fully open-source LLMs",open,https://github.com/LLM360/amber-train/tree/main,amber-train repository includes code for training and finetuning.,open,https://huggingface.co/datasets/LLM360/AmberDatasets,data well-documented and openly available,open,https://huggingface.co/LLM360/Amber,360 model checkpoints released,open,https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k,RL and fine-tuning data shared and documented,open,https://huggingface.co/LLM360/AmberChat,Finetuned model available for download.,open,https://huggingface.co/LLM360/AmberChat,Everything licensed under Apache 2.0,partial,https://github.com/LLM360,Code documented in helpful readme.md files but only partly inline.,partial,https://arxiv.org/abs/2312.06550,"Architecture described in preprint, but not all details documented.",open,https://arxiv.org/abs/2312.06550,"Preprint describes architecture, design choices, training and fine-tuning.",closed,,No peer-reviewed paper yet.,partial,https://huggingface.co/LLM360/AmberChat,Model card doesn't specify use or limitations,partial,https://huggingface.co/datasets/LLM360/AmberDatasets,"Concise description (better than most), but doesn't specify funders, purposes, representativeness, legal status as prescribed by datasheets industry standard",closed,,No released package found,open,https://huggingface.co/LLM360/AmberChat,Free Huggingface inference API.,/projects/amber.yaml,10.0
 https://open-assistant.io/,,Pythia 12B,OpenAssistant Conversations,Apache 2.0,LAION-AI,https://open-assistant.io/,,open,https://github.com/LAION-AI/Open-Assistant,Code includes guide for developers,open,https://github.com/LAION-AI/Open-Assistant/tree/main/data/datasets,Datasets documented in detail and recipes for cleaning up and downloading provided in code notebooks.,open,https://huggingface.co/OpenAssistant,Model weights in several variants downloadable through HuggingFace,open,https://huggingface.co/datasets/OpenAssistant/oasst1,"OpenAssistant Conversations is 'a human-generated, human-annotated assistant-style conversation corpus consisting of 161443 messages distributed across 66497 conversation trees, in 35 different languages, annotated with 461292 quality ratings' (preprint)",closed,,RLHF weights not separately released,open,https://projects.laion.ai/Open-Assistant/docs/faq#what-license-does-open-assistant-use,Apache 2.0,open,https://projects.laion.ai/Open-Assistant/docs/intro,Separate website provides entry point to comprehensive documentation,open,https://github.com/LAION-AI/Open-Assistant/tree/main/model,Instructions to tune the pipeline on training data,partial,https://arxiv.org/abs//2304.07327,"Preprint describes creation of OpenAssistant Conversations corpus for instruction tuning, but not the base LLM, hence partial.",closed,,No peer-reviewed paper or published data audit found,closed,,,closed,,,open,,,open,https://projects.laion.ai/Open-Assistant/api,,/projects/Open-Assistant.yaml,9.5

docs/figure.html

@@ -69,7 +69,7 @@ <h1>Open GenAI: LLMs (simplified table)</h1>
 <p id="table-guide"><em>How to use this table.</em> Every cell records a three-level openness judgement (<span class="openness open"><strong>✔︎</strong> open</span>, <span class="openness partial"><strong>~</strong> partial</span> or <span class="openness closed"><strong>✘</strong> closed</span>) with a direct link to the available evidence; on hover, the cell will display the notes we have on file for that judgement. The name of each project is a direct link to source data. The table is sorted by cumulative openness, where <strong>✔︎</strong> is 1, <strong>~</strong> is 0.5 and <strong>✘</strong> is 0 points. Note that RL may refer to RLHF or other forms of fine-tuning aimed at fostering instruction-following behaviour.</p>
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-<p id="build-time">Figure last built on 2024-07-22 at 23:08 UTC</p>
+<p id="build-time">Figure last built on 2024-08-10 at 19:11 UTC</p>
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