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3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="feedback-dialog-title"> <div data-view-component="true" class="Overlay-body"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="9thyI1HJqJx1qlRrvo2g9hwqlgNSR+egu541w22DMqnbSd3Xx5ST2LNCU9AjVu/C3wToRLVotnRxcm+KgzF+LA==" /> <p>We read every piece of feedback, and take your input very seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> 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dir=\"auto\"\u003eTraining Open Instruction-Following Language Models\u003c/h1\u003e\u003ca id=\"user-content-training-open-instruction-following-language-models\" class=\"anchor\" aria-label=\"Permalink: Training Open Instruction-Following Language Models\" href=\"#training-open-instruction-following-language-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repo serves as an open effort on instruction-tuning popular pretrained language models on publicly available datasets. We release this repo and will keep updating it with:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eCode for finetuning language models with latest techniques and instruction datasets in a unified format.\u003c/li\u003e\n\u003cli\u003eCode for running standard evaluation on a range of benchmarks, targeting for differnt capabilities of these language models.\u003c/li\u003e\n\u003cli\u003eCheckpoints or other useful artifacts that we build in our exploration.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003ePlease see our first paper \u003ca href=\"https://arxiv.org/abs/2306.04751\" rel=\"nofollow\"\u003eHow Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources\u003c/a\u003e for more thoughts behind this project and our initial findings. Please see our second paper \u003ca href=\"https://arxiv.org/abs/2311.10702\" rel=\"nofollow\"\u003eCamels in a Changing Climate: Enhancing LM Adaptation with Tulu 2\u003c/a\u003e for results using Llama-2 models and direct preference optimization. We are still working on more models. For more recent results involving PPO and DPO please see our third paper \u003ca href=\"https://arxiv.org/abs/2406.09279\" rel=\"nofollow\"\u003eUnpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback\u003c/a\u003e.\u003c/p\u003e\n\u003cp align=\"center\" width=\"100%\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/allenai/open-instruct/blob/main/assets/images/tulu_logo.png\"\u003e\u003cimg src=\"/allenai/open-instruct/raw/main/assets/images/tulu_logo.png\" alt=\"Tülu (a hybrid camel) represents a suite of LLaMa models that we built by fully-finetuning them on a strong mix of datasets.\" style=\"width: 20%; max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNews\u003c/h2\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-label=\"Permalink: News\" href=\"#news\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2024-07-01] We released \u003ca href=\"https://arxiv.org/abs/2406.09279\" rel=\"nofollow\"\u003eUnpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback\u003c/a\u003e and have majorly updated our codebase to support new models and package versions.\u003c/li\u003e\n\u003cli\u003e[2023-11-27] We released \u003ca href=\"https://arxiv.org/abs/2311.10702\" rel=\"nofollow\"\u003eCamels in a Changing Climate: Enhancing LM Adaptation with Tulu 2\u003c/a\u003e. Check out our models \u003ca href=\"https://huggingface.co/collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. We have added a DPO finetuning script for replicating our results.\u003c/li\u003e\n\u003cli\u003e[2023-09-26] We switched to use the official \u003ca href=\"https://github.com/tatsu-lab/alpaca_eval\"\u003ealpaca-eval\u003c/a\u003e library to run AlpacaFarm evaluation but use regenerated longer reference outputs. This will change our numbers reported in the paper. We will update the paper soon.\u003c/li\u003e\n\u003cli\u003e[2023-09-25] Supported using \u003ca href=\"https://github.com/vllm-project/vllm/\"\u003evLLM\u003c/a\u003e for our evaluations, which speeds up the evaluation by 10x.\u003c/li\u003e\n\u003cli\u003e[2023-09-17] Supported \u003ca href=\"https://arxiv.org/abs/2106.09685\" rel=\"nofollow\"\u003eLoRA\u003c/a\u003e and \u003ca href=\"https://arxiv.org/abs/2305.14314\" rel=\"nofollow\"\u003eQLoRA\u003c/a\u003e finetuning. See \u003ca href=\"#parameter-efficient-finetuning\"\u003ehere\u003c/a\u003e for more details.\u003c/li\u003e\n\u003cli\u003e[2023-08-18] Added support for \u003ca href=\"https://github.com/microsoft/TOXIGEN\"\u003eToxiGen\u003c/a\u003e/\u003ca href=\"https://github.com/sylinrl/TruthfulQA\"\u003eTruthfulQA\u003c/a\u003e evaluation. Check our \u003ccode\u003escripts/eval/\u003c/code\u003e for examples of running them.\u003c/li\u003e\n\u003cli\u003e[2023-08-08] Supported several new instruction dataset, including \u003ca href=\"https://huggingface.co/datasets/GAIR/lima\" rel=\"nofollow\"\u003eLIMA\u003c/a\u003e / \u003ca href=\"https://github.com/nlpxucan/WizardLM\"\u003eWizardLM\u003c/a\u003e / \u003ca href=\"https://huggingface.co/datasets/Open-Orca/OpenOrca\" rel=\"nofollow\"\u003eOpen-Orca\u003c/a\u003e. See the \u003ca href=\"/allenai/open-instruct/blob/main/scripts/data/prepare_train_data.sh\"\u003epreparation script\u003c/a\u003e for details. Performance hasn't been evaluated yet.\u003c/li\u003e\n\u003cli\u003e[2023-08-06] Supported LLaMa 2 finetuning and FlashAttention-2 by bumping the version of transformers and many other dependencies.\u003c/li\u003e\n\u003cli\u003e[2023-06-29] Added \u003ca href=\"#licensing\"\u003elicensing info\u003c/a\u003e for our released models.\u003c/li\u003e\n\u003cli\u003e[2023-06-09] Released Tülu (a suite of LLaMa models fully-finetuned on a strong mix of datasets) and many other checkpoints on HuggingFace \u003ca href=\"#released-checkpoints\"\u003e[Links]\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2023-06-09] Initial release of the codebase containing the training and evaluation code for our \u003ca href=\"https://arxiv.org/abs/2306.04751\" rel=\"nofollow\"\u003earxiv paper\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOur setup mostly follows our \u003ca href=\"/allenai/open-instruct/blob/main/Dockerfile\"\u003eDockerfile\u003c/a\u003e, which uses Python 3.10. \u003cem\u003eNote that Open Instruct is a research codebase and does not guarantee backward compatibility.\u003c/em\u003e We offer two installation strategies:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eLocal installation\u003c/strong\u003e: This is the recommended way to install Open Instruct. You can install the dependencies by running the following commands:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install --upgrade pip \u0026quot;setuptools\u0026lt;70.0.0\u0026quot; wheel \n# TODO, unpin setuptools when this issue in flash attention is resolved\npip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121\npip install packaging\npip install flash-attn==2.6.3 --no-build-isolation\npip install -r requirements.txt\npython -m nltk.downloader punkt\npip install -e .\"\u003e\u003cpre\u003epip install --upgrade pip \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esetuptools\u0026lt;70.0.0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e wheel \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e TODO, unpin setuptools when this issue in flash attention is resolved\u003c/span\u003e\npip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121\npip install packaging\npip install flash-attn==2.6.3 --no-build-isolation\npip install -r requirements.txt\npython -m nltk.downloader punkt\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eDocker installation\u003c/strong\u003e: You can also use the Dockerfile to build a Docker image. You can build the image with the following command:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker build --build-arg CUDA=12.1.0 --build-arg TARGET=cudnn8-devel --build-arg DIST=ubuntu20.04 . -t open_instruct_dev\n# if you are interally at AI2, you can create an image like this:\nbeaker image delete $(whoami)/open_instruct_dev \nbeaker image create open_instruct_dev -n open_instruct_dev -w ai2/$(whoami)\"\u003e\u003cpre\u003edocker build --build-arg CUDA=12.1.0 --build-arg TARGET=cudnn8-devel --build-arg DIST=ubuntu20.04 \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e -t open_instruct_dev\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e if you are interally at AI2, you can create an image like this:\u003c/span\u003e\nbeaker image delete \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ewhoami\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/open_instruct_dev \nbeaker image create open_instruct_dev -n open_instruct_dev -w ai2/\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ewhoami\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you are internally at AI2, you may launch experiments using our always-up-to-date auto-built image \u003ccode\u003enathanl/open_instruct_auto\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining\u003c/h2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAfter having setup the environment, you are ready to launch some experiments. We provide a few examples below. To learn more about how to reproduce the Tulu 3 models, please refer to the \u003ca href=\"/allenai/open-instruct/blob/main/docs/tulu3.md\"\u003eTulu 3 README\u003c/a\u003e. The instructions and documentations for Tulu 1 and Tulu 2 are in \u003ca href=\"/allenai/open-instruct/blob/main/docs/tulu1_tulu2.md\"\u003eTulu 1 and 2 README\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFinetuning\u003c/h3\u003e\u003ca id=\"user-content-finetuning\" class=\"anchor\" aria-label=\"Permalink: Finetuning\" href=\"#finetuning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can run the following commands for getting started:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# quick debugging run using 1 GPU\nsh scripts/finetune_with_accelerate_config.sh 1 configs/train_configs/sft/mini.yaml\n# train an 8B tulu3 model using 8 GPU\nsh scripts/finetune_with_accelerate_config.sh 8 configs/train_configs/tulu3/tulu3_sft.yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e quick debugging run using 1 GPU\u003c/span\u003e\nsh scripts/finetune_with_accelerate_config.sh 1 configs/train_configs/sft/mini.yaml\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e train an 8B tulu3 model using 8 GPU\u003c/span\u003e\nsh scripts/finetune_with_accelerate_config.sh 8 configs/train_configs/tulu3/tulu3_sft.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePreference Tuning\u003c/h3\u003e\u003ca id=\"user-content-preference-tuning\" class=\"anchor\" aria-label=\"Permalink: Preference Tuning\" href=\"#preference-tuning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# quick debugging run using 1 GPU\nsh scripts/dpo_train_with_accelerate_config.sh 1 configs/train_configs/dpo/mini.yaml\n# train an 8B tulu3 model using 8 GPU\nsh scripts/finetune_with_accelerate_config.sh 8 configs/train_configs/tulu3/tulu3_dpo_8b.yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e quick debugging run using 1 GPU\u003c/span\u003e\nsh scripts/dpo_train_with_accelerate_config.sh 1 configs/train_configs/dpo/mini.yaml\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e train an 8B tulu3 model using 8 GPU\u003c/span\u003e\nsh scripts/finetune_with_accelerate_config.sh 8 configs/train_configs/tulu3/tulu3_dpo_8b.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRLVR\u003c/h3\u003e\u003ca id=\"user-content-rlvr\" class=\"anchor\" aria-label=\"Permalink: RLVR\" href=\"#rlvr\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# quick debugging run using 2 GPU (1 for inference, 1 for training)\n# here we are using `HuggingFaceTB/SmolLM2-360M-Instruct`; it's prob not\n# gonna work, but it's easy to test run and print stuff.\npython open_instruct/ppo_vllm_thread_ray_gtrl.py \\\n --dataset_mixer '{\u0026quot;ai2-adapt-dev/gsm8k_math_ifeval_ground_truth_mixed\u0026quot;: 1.0}' \\\n --dataset_train_splits train \\\n --dataset_eval_mixer '{\u0026quot;ai2-adapt-dev/gsm8k_math_ground_truth\u0026quot;: 1.0}' \\\n --dataset_eval_splits test \\\n --max_token_length 2048 \\\n --max_prompt_token_length 2048 \\\n --response_length 2048 \\\n --model_name_or_path HuggingFaceTB/SmolLM2-360M-Instruct \\\n --reward_model_path HuggingFaceTB/SmolLM2-360M-Instruct \\\n --non_stop_penalty \\\n --stop_token eos \\\n --temperature 1.0 \\\n --ground_truths_key ground_truth \\\n --chat_template tulu \\\n --sft_messages_key messages \\\n --learning_rate 3e-7 \\\n --total_episodes 10000 \\\n --penalty_reward_value -10.0 \\\n --deepspeed_stage 3 \\\n --per_device_train_batch_size 2 \\\n --local_rollout_forward_batch_size 2 \\\n --local_mini_batch_size 32 \\\n --local_rollout_batch_size 32 \\\n --num_epochs 1 \\\n --actor_num_gpus_per_node 1 \\\n --vllm_tensor_parallel_size 1 \\\n --beta 0.05 \\\n --apply_verifiable_reward true \\\n --output_dir output/rlvr_1b \\\n --seed 3 \\\n --num_evals 3 \\\n --save_freq 100 \\\n --reward_model_multiplier 0.0 \\\n --gradient_checkpointing \\\n --with_tracking\n\n# train an 8B tulu3 model using 8 GPU (1 for inference, 7 for training)\npython open_instruct/ppo_vllm_thread_ray_gtrl.py \\\n --dataset_mixer '{\u0026quot;ai2-adapt-dev/gsm8k_math_ifeval_ground_truth_mixed\u0026quot;: 1.0}' \\\n --dataset_train_splits train \\\n --dataset_eval_mixer '{\u0026quot;ai2-adapt-dev/gsm8k_math_ground_truth\u0026quot;: 1.0}' \\\n --dataset_eval_splits test \\\n --max_token_length 2048 \\\n --max_prompt_token_length 2048 \\\n --response_length 2048 \\\n --model_name_or_path allenai/Llama-3.1-Tulu-3-8B-DPO \\\n --reward_model_path allenai/Llama-3.1-Tulu-3-8B-RM \\\n --non_stop_penalty \\\n --stop_token eos \\\n --temperature 1.0 \\\n --ground_truths_key ground_truth \\\n --chat_template tulu \\\n --sft_messages_key messages \\\n --learning_rate 3e-7 \\\n --total_episodes 10000000 \\\n --penalty_reward_value -10.0 \\\n --deepspeed_stage 3 \\\n --per_device_train_batch_size 2 \\\n --local_rollout_forward_batch_size 2 \\\n --local_mini_batch_size 32 \\\n --local_rollout_batch_size 32 \\\n --actor_num_gpus_per_node 7 \\\n --vllm_tensor_parallel_size 1 \\\n --beta 0.05 \\\n --apply_verifiable_reward true \\\n --output_dir output/rlvr_8b \\\n --seed 3 \\\n --num_evals 3 \\\n --save_freq 100 \\\n --reward_model_multiplier 0.0 \\\n --gradient_checkpointing \\\n --with_tracking\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e quick debugging run using 2 GPU (1 for inference, 1 for training)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e here we are using `HuggingFaceTB/SmolLM2-360M-Instruct`; it's prob not\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e gonna work, but it's easy to test run and print stuff.\u003c/span\u003e\npython open_instruct/ppo_vllm_thread_ray_gtrl.py \\\n --dataset_mixer \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e{\"ai2-adapt-dev/gsm8k_math_ifeval_ground_truth_mixed\": 1.0}\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e \\\n --dataset_train_splits train \\\n --dataset_eval_mixer \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e{\"ai2-adapt-dev/gsm8k_math_ground_truth\": 1.0}\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e \\\n --dataset_eval_splits \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e \\\n --max_token_length 2048 \\\n --max_prompt_token_length 2048 \\\n --response_length 2048 \\\n --model_name_or_path HuggingFaceTB/SmolLM2-360M-Instruct \\\n --reward_model_path HuggingFaceTB/SmolLM2-360M-Instruct \\\n --non_stop_penalty \\\n --stop_token eos \\\n --temperature 1.0 \\\n --ground_truths_key ground_truth \\\n --chat_template tulu \\\n --sft_messages_key messages \\\n --learning_rate 3e-7 \\\n --total_episodes 10000 \\\n --penalty_reward_value -10.0 \\\n --deepspeed_stage 3 \\\n --per_device_train_batch_size 2 \\\n --local_rollout_forward_batch_size 2 \\\n --local_mini_batch_size 32 \\\n --local_rollout_batch_size 32 \\\n --num_epochs 1 \\\n --actor_num_gpus_per_node 1 \\\n --vllm_tensor_parallel_size 1 \\\n --beta 0.05 \\\n --apply_verifiable_reward \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e \\\n --output_dir output/rlvr_1b \\\n --seed 3 \\\n --num_evals 3 \\\n --save_freq 100 \\\n --reward_model_multiplier 0.0 \\\n --gradient_checkpointing \\\n --with_tracking\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e train an 8B tulu3 model using 8 GPU (1 for inference, 7 for training)\u003c/span\u003e\npython open_instruct/ppo_vllm_thread_ray_gtrl.py \\\n --dataset_mixer \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e{\"ai2-adapt-dev/gsm8k_math_ifeval_ground_truth_mixed\": 1.0}\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e \\\n --dataset_train_splits train \\\n --dataset_eval_mixer \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e{\"ai2-adapt-dev/gsm8k_math_ground_truth\": 1.0}\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e \\\n --dataset_eval_splits \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e \\\n --max_token_length 2048 \\\n --max_prompt_token_length 2048 \\\n --response_length 2048 \\\n --model_name_or_path allenai/Llama-3.1-Tulu-3-8B-DPO \\\n --reward_model_path allenai/Llama-3.1-Tulu-3-8B-RM \\\n --non_stop_penalty \\\n --stop_token eos \\\n --temperature 1.0 \\\n --ground_truths_key ground_truth \\\n --chat_template tulu \\\n --sft_messages_key messages \\\n --learning_rate 3e-7 \\\n --total_episodes 10000000 \\\n --penalty_reward_value -10.0 \\\n --deepspeed_stage 3 \\\n --per_device_train_batch_size 2 \\\n --local_rollout_forward_batch_size 2 \\\n --local_mini_batch_size 32 \\\n --local_rollout_batch_size 32 \\\n --actor_num_gpus_per_node 7 \\\n --vllm_tensor_parallel_size 1 \\\n --beta 0.05 \\\n --apply_verifiable_reward \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e \\\n --output_dir output/rlvr_8b \\\n --seed 3 \\\n --num_evals 3 \\\n --save_freq 100 \\\n --reward_model_multiplier 0.0 \\\n --gradient_checkpointing \\\n --with_tracking\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContamination checks\u003c/h2\u003e\u003ca id=\"user-content-contamination-checks\" class=\"anchor\" aria-label=\"Permalink: Contamination checks\" href=\"#contamination-checks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe release our scripts for measuring the overlap between instruction tuning datasets and evaluation datasets in \u003ccode\u003e./decontamination\u003c/code\u003e. See the \u003ca href=\"/allenai/open-instruct/blob/main/decontamination/README.md\"\u003eREADME\u003c/a\u003e for more details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeveloping\u003c/h3\u003e\u003ca id=\"user-content-developing\" class=\"anchor\" aria-label=\"Permalink: Developing\" href=\"#developing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhen submitting a PR to this repo, we check the core code in \u003ccode\u003eopen_instruct/\u003c/code\u003e for style with the following:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"make style\nmake quality\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003emake style\nmake quality\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRepo structure\u003c/h3\u003e\u003ca id=\"user-content-repo-structure\" class=\"anchor\" aria-label=\"Permalink: Repo structure\" href=\"#repo-structure\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"├── assets/ \u0026lt;- Images, licenses, etc.\n├── configs/ \n| ├── beaker_configs/ \u0026lt;- AI2 Beaker configs\n| ├── ds_configs/ \u0026lt;- DeepSpeed configs\n| └── train_configs/ \u0026lt;- Training configs\n├── decontamination/ \u0026lt;- Scripts for measuring train-eval overlap\n├── eval/ \u0026lt;- Evaluation suite for fine-tuned models\n├── human_eval/ \u0026lt;- Human evaluation interface (not maintained)\n├── open_instruct/ \u0026lt;- Source code (flat)\n├── quantize/ \u0026lt;- Scripts for quantization\n├── scripts/ \u0026lt;- Core training and evaluation scripts\n└── Dockerfile \u0026lt;- Dockerfile\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e├── assets/ \u0026lt;- Images, licenses, etc.\n├── configs/ \n| ├── beaker_configs/ \u0026lt;- AI2 Beaker configs\n| ├── ds_configs/ \u0026lt;- DeepSpeed configs\n| └── train_configs/ \u0026lt;- Training configs\n├── decontamination/ \u0026lt;- Scripts for measuring train-eval overlap\n├── eval/ \u0026lt;- Evaluation suite for fine-tuned models\n├── human_eval/ \u0026lt;- Human evaluation interface (not maintained)\n├── open_instruct/ \u0026lt;- Source code (flat)\n├── quantize/ \u0026lt;- Scripts for quantization\n├── scripts/ \u0026lt;- Core training and evaluation scripts\n└── Dockerfile \u0026lt;- Dockerfile\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicensing\u003c/h2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-label=\"Permalink: Licensing\" href=\"#licensing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis codebase is licensed under Apache 2.0 as given in \u003ca href=\"/allenai/open-instruct/blob/main/LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe license we use for V1 models released (along with the base model licenses) can be found in \u003ca href=\"/allenai/open-instruct/blob/main/assets/model_licenses/tulu_license.txt\"\u003eassets/model_licenses/tulu_license.txt\u003c/a\u003e - just replace \u003ccode\u003e\u0026lt;MODELNAME\u0026gt;\u003c/code\u003e with the actual model name (i.e., the name on HuggingFace).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eV2 models are licensed under the \u003ca href=\"https://allenai.org/licenses/impact-lr\" rel=\"nofollow\"\u003elow-risk AI2 ImpACT license\u003c/a\u003e. See \u003ca href=\"https://allenai.org/impact-license\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for more details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOpen Instruct is a project that benefitd from many open-source projects and libraries. We would like to particularly thank the folloiwng projects:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/huggingface/transformers\"\u003eHuggingFace Transformers\u003c/a\u003e: We adapted Hugging Face's Trainer for our finetuning scripts.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/huggingface/trl\"\u003eHuggingFace TRL\u003c/a\u003e and \u003ca href=\"https://github.com/eric-mitchell/direct-preference-optimization\"\u003eeric-mitchell/direct-preference-optimization\u003c/a\u003e: our preference tuning code is adapted from TRL and from Eric Mitchell's DPO code.\u003c/li\u003e\n\u003cli\u003eOpenAI's \u003ca href=\"https://github.com/openai/lm-human-preferences\"\u003elm-human-preferences\u003c/a\u003e, \u003ca href=\"https://github.com/openai/summarize-from-feedback\"\u003esummarize-from-feedback\u003c/a\u003e, and \u003ca href=\"https://github.com/vwxyzjn/summarize_from_feedback_details\"\u003evwxyzjn/summarize_from_feedback_details\u003c/a\u003e: Our core PPO code is adapted from OpenAI's original RLHF code and \u003ca href=\"https://openreview.net/forum?id=kHO2ZTa8e3\" rel=\"nofollow\"\u003eHuang et al (2024)'s reproduction work\u003c/a\u003e of OpenAI's summarize from feedback work.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OpenRLHF/OpenRLHF\"\u003eOpenRLHF\u003c/a\u003e: We adapted OpenRLHF's Ray + vLLM distributed code for scaling up PPO RLVR training into the 70B scale.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you used this repository or our models, please cite our work:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@misc{wang2023far,\n title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources}, \n author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},\n year={2023},\n eprint={2306.04751},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@misc\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003ewang2023far\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eHow Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eYizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2023\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eeprint\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2306.04751\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003earchivePrefix\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003earXiv\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eprimaryClass\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003ecs.CL\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@misc{ivison2023camels,\n title={Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2}, \n author={Hamish Ivison and Yizhong Wang and Valentina Pyatkin and Nathan Lambert and Matthew Peters and Pradeep Dasigi and Joel Jang and David Wadden and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},\n year={2023},\n eprint={2311.10702},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@misc\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003eivison2023camels\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eCamels in a Changing Climate: Enhancing LM Adaptation with Tulu 2\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eHamish Ivison and Yizhong Wang and Valentina Pyatkin and Nathan Lambert and Matthew Peters and Pradeep Dasigi and Joel Jang and David Wadden and Noah A. 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Smith and Yejin Choi and Hannaneh Hajishirzi},\n year={2024},\n eprint={2406.09279},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@misc\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003eivison2024unpacking\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eUnpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eHamish Ivison and Yizhong Wang and Jiacheng Liu and Zeqiu Wu and Valentina Pyatkin and Nathan Lambert and Noah A. Smith and Yejin Choi and Hannaneh Hajishirzi\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2024\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eeprint\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2406.09279\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003earchivePrefix\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003earXiv\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eprimaryClass\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003ecs.CL\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n}\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Training Open Instruction-Following Language Models","anchor":"training-open-instruction-following-language-models","htmlText":"Training Open Instruction-Following Language Models"},{"level":2,"text":"News","anchor":"news","htmlText":"News"},{"level":2,"text":"Setup","anchor":"setup","htmlText":"Setup"},{"level":2,"text":"Training","anchor":"training","htmlText":"Training"},{"level":3,"text":"Finetuning","anchor":"finetuning","htmlText":"Finetuning"},{"level":3,"text":"Preference Tuning","anchor":"preference-tuning","htmlText":"Preference Tuning"},{"level":3,"text":"RLVR","anchor":"rlvr","htmlText":"RLVR"},{"level":2,"text":"Contamination checks","anchor":"contamination-checks","htmlText":"Contamination checks"},{"level":3,"text":"Developing","anchor":"developing","htmlText":"Developing"},{"level":3,"text":"Repo structure","anchor":"repo-structure","htmlText":"Repo structure"},{"level":2,"text":"Licensing","anchor":"licensing","htmlText":"Licensing"},{"level":2,"text":"Acknowledgements","anchor":"acknowledgements","htmlText":"Acknowledgements"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fallenai%2Fopen-instruct"}},{"displayName":"LICENSE","repoName":"open-instruct","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fallenai%2Fopen-instruct"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> 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aria-hidden="true" focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Training Open Instruction-Following Language Models</h1><a id="user-content-training-open-instruction-following-language-models" class="anchor" aria-label="Permalink: Training Open Instruction-Following Language Models" href="#training-open-instruction-following-language-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repo serves as an open effort on instruction-tuning popular pretrained language models on publicly available datasets. We release this repo and will keep updating it with:</p> <ol dir="auto"> <li>Code for finetuning language models with latest techniques and instruction datasets in a unified format.</li> <li>Code for running standard evaluation on a range of benchmarks, targeting for differnt capabilities of these language models.</li> <li>Checkpoints or other useful artifacts that we build in our exploration.</li> </ol> <p dir="auto">Please see our first paper <a href="https://arxiv.org/abs/2306.04751" rel="nofollow">How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources</a> for more thoughts behind this project and our initial findings. Please see our second paper <a href="https://arxiv.org/abs/2311.10702" rel="nofollow">Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2</a> for results using Llama-2 models and direct preference optimization. We are still working on more models. For more recent results involving PPO and DPO please see our third paper <a href="https://arxiv.org/abs/2406.09279" rel="nofollow">Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback</a>.</p> <p align="center" width="100%" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/allenai/open-instruct/blob/main/assets/images/tulu_logo.png"><img src="/allenai/open-instruct/raw/main/assets/images/tulu_logo.png" alt="Tülu (a hybrid camel) represents a suite of LLaMa models that we built by fully-finetuning them on a strong mix of datasets." style="width: 20%; max-width: 100%;"></a> </p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">News</h2><a id="user-content-news" class="anchor" aria-label="Permalink: News" href="#news"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>[2024-07-01] We released <a href="https://arxiv.org/abs/2406.09279" rel="nofollow">Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback</a> and have majorly updated our codebase to support new models and package versions.</li> <li>[2023-11-27] We released <a href="https://arxiv.org/abs/2311.10702" rel="nofollow">Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2</a>. Check out our models <a href="https://huggingface.co/collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101" rel="nofollow">here</a>. We have added a DPO finetuning script for replicating our results.</li> <li>[2023-09-26] We switched to use the official <a href="https://github.com/tatsu-lab/alpaca_eval">alpaca-eval</a> library to run AlpacaFarm evaluation but use regenerated longer reference outputs. This will change our numbers reported in the paper. We will update the paper soon.</li> <li>[2023-09-25] Supported using <a href="https://github.com/vllm-project/vllm/">vLLM</a> for our evaluations, which speeds up the evaluation by 10x.</li> <li>[2023-09-17] Supported <a href="https://arxiv.org/abs/2106.09685" rel="nofollow">LoRA</a> and <a href="https://arxiv.org/abs/2305.14314" rel="nofollow">QLoRA</a> finetuning. See <a href="#parameter-efficient-finetuning">here</a> for more details.</li> <li>[2023-08-18] Added support for <a href="https://github.com/microsoft/TOXIGEN">ToxiGen</a>/<a href="https://github.com/sylinrl/TruthfulQA">TruthfulQA</a> evaluation. Check our <code>scripts/eval/</code> for examples of running them.</li> <li>[2023-08-08] Supported several new instruction dataset, including <a href="https://huggingface.co/datasets/GAIR/lima" rel="nofollow">LIMA</a> / <a href="https://github.com/nlpxucan/WizardLM">WizardLM</a> / <a href="https://huggingface.co/datasets/Open-Orca/OpenOrca" rel="nofollow">Open-Orca</a>. See the <a href="/allenai/open-instruct/blob/main/scripts/data/prepare_train_data.sh">preparation script</a> for details. Performance hasn't been evaluated yet.</li> <li>[2023-08-06] Supported LLaMa 2 finetuning and FlashAttention-2 by bumping the version of transformers and many other dependencies.</li> <li>[2023-06-29] Added <a href="#licensing">licensing info</a> for our released models.</li> <li>[2023-06-09] Released Tülu (a suite of LLaMa models fully-finetuned on a strong mix of datasets) and many other checkpoints on HuggingFace <a href="#released-checkpoints">[Links]</a>.</li> <li>[2023-06-09] Initial release of the codebase containing the training and evaluation code for our <a href="https://arxiv.org/abs/2306.04751" rel="nofollow">arxiv paper</a>.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Setup</h2><a id="user-content-setup" class="anchor" aria-label="Permalink: Setup" href="#setup"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Our setup mostly follows our <a href="/allenai/open-instruct/blob/main/Dockerfile">Dockerfile</a>, which uses Python 3.10. <em>Note that Open Instruct is a research codebase and does not guarantee backward compatibility.</em> We offer two installation strategies:</p> <ul dir="auto"> <li><strong>Local installation</strong>: This is the recommended way to install Open Instruct. You can install the dependencies by running the following commands:</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install --upgrade pip "setuptools<70.0.0" wheel # TODO, unpin setuptools when this issue in flash attention is resolved pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121 pip install packaging pip install flash-attn==2.6.3 --no-build-isolation pip install -r requirements.txt python -m nltk.downloader punkt pip install -e ."><pre>pip install --upgrade pip <span class="pl-s"><span class="pl-pds">"</span>setuptools<70.0.0<span class="pl-pds">"</span></span> wheel <span class="pl-c"><span class="pl-c">#</span> TODO, unpin setuptools when this issue in flash attention is resolved</span> pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121 pip install packaging pip install flash-attn==2.6.3 --no-build-isolation pip install -r requirements.txt python -m nltk.downloader punkt pip install -e <span class="pl-c1">.</span></pre></div> <ul dir="auto"> <li><strong>Docker installation</strong>: You can also use the Dockerfile to build a Docker image. You can build the image with the following command:</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker build --build-arg CUDA=12.1.0 --build-arg TARGET=cudnn8-devel --build-arg DIST=ubuntu20.04 . -t open_instruct_dev # if you are interally at AI2, you can create an image like this: beaker image delete $(whoami)/open_instruct_dev beaker image create open_instruct_dev -n open_instruct_dev -w ai2/$(whoami)"><pre>docker build --build-arg CUDA=12.1.0 --build-arg TARGET=cudnn8-devel --build-arg DIST=ubuntu20.04 <span class="pl-c1">.</span> -t open_instruct_dev <span class="pl-c"><span class="pl-c">#</span> if you are interally at AI2, you can create an image like this:</span> beaker image delete <span class="pl-s"><span class="pl-pds">$(</span>whoami<span class="pl-pds">)</span></span>/open_instruct_dev beaker image create open_instruct_dev -n open_instruct_dev -w ai2/<span class="pl-s"><span class="pl-pds">$(</span>whoami<span class="pl-pds">)</span></span></pre></div> <p dir="auto">If you are internally at AI2, you may launch experiments using our always-up-to-date auto-built image <code>nathanl/open_instruct_auto</code>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Training</h2><a id="user-content-training" class="anchor" aria-label="Permalink: Training" href="#training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">After having setup the environment, you are ready to launch some experiments. We provide a few examples below. To learn more about how to reproduce the Tulu 3 models, please refer to the <a href="/allenai/open-instruct/blob/main/docs/tulu3.md">Tulu 3 README</a>. The instructions and documentations for Tulu 1 and Tulu 2 are in <a href="/allenai/open-instruct/blob/main/docs/tulu1_tulu2.md">Tulu 1 and 2 README</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Finetuning</h3><a id="user-content-finetuning" class="anchor" aria-label="Permalink: Finetuning" href="#finetuning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can run the following commands for getting started:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# quick debugging run using 1 GPU sh scripts/finetune_with_accelerate_config.sh 1 configs/train_configs/sft/mini.yaml # train an 8B tulu3 model using 8 GPU sh scripts/finetune_with_accelerate_config.sh 8 configs/train_configs/tulu3/tulu3_sft.yaml"><pre><span class="pl-c"><span class="pl-c">#</span> quick debugging run using 1 GPU</span> sh scripts/finetune_with_accelerate_config.sh 1 configs/train_configs/sft/mini.yaml <span class="pl-c"><span class="pl-c">#</span> train an 8B tulu3 model using 8 GPU</span> sh scripts/finetune_with_accelerate_config.sh 8 configs/train_configs/tulu3/tulu3_sft.yaml</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Preference Tuning</h3><a id="user-content-preference-tuning" class="anchor" aria-label="Permalink: Preference Tuning" href="#preference-tuning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# quick debugging run using 1 GPU sh scripts/dpo_train_with_accelerate_config.sh 1 configs/train_configs/dpo/mini.yaml # train an 8B tulu3 model using 8 GPU sh scripts/finetune_with_accelerate_config.sh 8 configs/train_configs/tulu3/tulu3_dpo_8b.yaml"><pre><span class="pl-c"><span class="pl-c">#</span> quick debugging run using 1 GPU</span> sh scripts/dpo_train_with_accelerate_config.sh 1 configs/train_configs/dpo/mini.yaml <span class="pl-c"><span class="pl-c">#</span> train an 8B tulu3 model using 8 GPU</span> sh scripts/finetune_with_accelerate_config.sh 8 configs/train_configs/tulu3/tulu3_dpo_8b.yaml</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">RLVR</h3><a id="user-content-rlvr" class="anchor" aria-label="Permalink: RLVR" href="#rlvr"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# quick debugging run using 2 GPU (1 for inference, 1 for training) # here we are using `HuggingFaceTB/SmolLM2-360M-Instruct`; it's prob not # gonna work, but it's easy to test run and print stuff. python open_instruct/ppo_vllm_thread_ray_gtrl.py \ --dataset_mixer '{"ai2-adapt-dev/gsm8k_math_ifeval_ground_truth_mixed": 1.0}' \ --dataset_train_splits train \ --dataset_eval_mixer '{"ai2-adapt-dev/gsm8k_math_ground_truth": 1.0}' \ --dataset_eval_splits test \ --max_token_length 2048 \ --max_prompt_token_length 2048 \ --response_length 2048 \ --model_name_or_path HuggingFaceTB/SmolLM2-360M-Instruct \ --reward_model_path HuggingFaceTB/SmolLM2-360M-Instruct \ --non_stop_penalty \ --stop_token eos \ --temperature 1.0 \ --ground_truths_key ground_truth \ --chat_template tulu \ --sft_messages_key messages \ --learning_rate 3e-7 \ --total_episodes 10000 \ --penalty_reward_value -10.0 \ --deepspeed_stage 3 \ --per_device_train_batch_size 2 \ --local_rollout_forward_batch_size 2 \ --local_mini_batch_size 32 \ --local_rollout_batch_size 32 \ --num_epochs 1 \ --actor_num_gpus_per_node 1 \ --vllm_tensor_parallel_size 1 \ --beta 0.05 \ --apply_verifiable_reward true \ --output_dir output/rlvr_1b \ --seed 3 \ --num_evals 3 \ --save_freq 100 \ --reward_model_multiplier 0.0 \ --gradient_checkpointing \ --with_tracking # train an 8B tulu3 model using 8 GPU (1 for inference, 7 for training) python open_instruct/ppo_vllm_thread_ray_gtrl.py \ --dataset_mixer '{"ai2-adapt-dev/gsm8k_math_ifeval_ground_truth_mixed": 1.0}' \ --dataset_train_splits train \ --dataset_eval_mixer '{"ai2-adapt-dev/gsm8k_math_ground_truth": 1.0}' \ --dataset_eval_splits test \ --max_token_length 2048 \ --max_prompt_token_length 2048 \ --response_length 2048 \ --model_name_or_path allenai/Llama-3.1-Tulu-3-8B-DPO \ --reward_model_path allenai/Llama-3.1-Tulu-3-8B-RM \ --non_stop_penalty \ --stop_token eos \ --temperature 1.0 \ --ground_truths_key ground_truth \ --chat_template tulu \ --sft_messages_key messages \ --learning_rate 3e-7 \ --total_episodes 10000000 \ --penalty_reward_value -10.0 \ --deepspeed_stage 3 \ --per_device_train_batch_size 2 \ --local_rollout_forward_batch_size 2 \ --local_mini_batch_size 32 \ --local_rollout_batch_size 32 \ --actor_num_gpus_per_node 7 \ --vllm_tensor_parallel_size 1 \ --beta 0.05 \ --apply_verifiable_reward true \ --output_dir output/rlvr_8b \ --seed 3 \ --num_evals 3 \ --save_freq 100 \ --reward_model_multiplier 0.0 \ --gradient_checkpointing \ --with_tracking"><pre><span class="pl-c"><span class="pl-c">#</span> quick debugging run using 2 GPU (1 for inference, 1 for training)</span> <span class="pl-c"><span class="pl-c">#</span> here we are using `HuggingFaceTB/SmolLM2-360M-Instruct`; it's prob not</span> <span class="pl-c"><span class="pl-c">#</span> gonna work, but it's easy to test run and print stuff.</span> python open_instruct/ppo_vllm_thread_ray_gtrl.py \ --dataset_mixer <span class="pl-s"><span class="pl-pds">'</span>{"ai2-adapt-dev/gsm8k_math_ifeval_ground_truth_mixed": 1.0}<span class="pl-pds">'</span></span> \ --dataset_train_splits train \ --dataset_eval_mixer <span class="pl-s"><span class="pl-pds">'</span>{"ai2-adapt-dev/gsm8k_math_ground_truth": 1.0}<span class="pl-pds">'</span></span> \ --dataset_eval_splits <span class="pl-c1">test</span> \ --max_token_length 2048 \ --max_prompt_token_length 2048 \ --response_length 2048 \ --model_name_or_path HuggingFaceTB/SmolLM2-360M-Instruct \ --reward_model_path HuggingFaceTB/SmolLM2-360M-Instruct \ --non_stop_penalty \ --stop_token eos \ --temperature 1.0 \ --ground_truths_key ground_truth \ --chat_template tulu \ --sft_messages_key messages \ --learning_rate 3e-7 \ --total_episodes 10000 \ --penalty_reward_value -10.0 \ --deepspeed_stage 3 \ --per_device_train_batch_size 2 \ --local_rollout_forward_batch_size 2 \ --local_mini_batch_size 32 \ --local_rollout_batch_size 32 \ --num_epochs 1 \ --actor_num_gpus_per_node 1 \ --vllm_tensor_parallel_size 1 \ --beta 0.05 \ --apply_verifiable_reward <span class="pl-c1">true</span> \ --output_dir output/rlvr_1b \ --seed 3 \ --num_evals 3 \ --save_freq 100 \ --reward_model_multiplier 0.0 \ --gradient_checkpointing \ --with_tracking <span class="pl-c"><span class="pl-c">#</span> train an 8B tulu3 model using 8 GPU (1 for inference, 7 for training)</span> python open_instruct/ppo_vllm_thread_ray_gtrl.py \ --dataset_mixer <span class="pl-s"><span class="pl-pds">'</span>{"ai2-adapt-dev/gsm8k_math_ifeval_ground_truth_mixed": 1.0}<span class="pl-pds">'</span></span> \ --dataset_train_splits train \ --dataset_eval_mixer <span class="pl-s"><span class="pl-pds">'</span>{"ai2-adapt-dev/gsm8k_math_ground_truth": 1.0}<span class="pl-pds">'</span></span> \ --dataset_eval_splits <span class="pl-c1">test</span> \ --max_token_length 2048 \ --max_prompt_token_length 2048 \ --response_length 2048 \ --model_name_or_path allenai/Llama-3.1-Tulu-3-8B-DPO \ --reward_model_path allenai/Llama-3.1-Tulu-3-8B-RM \ --non_stop_penalty \ --stop_token eos \ --temperature 1.0 \ --ground_truths_key ground_truth \ --chat_template tulu \ --sft_messages_key messages \ --learning_rate 3e-7 \ --total_episodes 10000000 \ --penalty_reward_value -10.0 \ --deepspeed_stage 3 \ --per_device_train_batch_size 2 \ --local_rollout_forward_batch_size 2 \ --local_mini_batch_size 32 \ --local_rollout_batch_size 32 \ --actor_num_gpus_per_node 7 \ --vllm_tensor_parallel_size 1 \ --beta 0.05 \ --apply_verifiable_reward <span class="pl-c1">true</span> \ --output_dir output/rlvr_8b \ --seed 3 \ --num_evals 3 \ --save_freq 100 \ --reward_model_multiplier 0.0 \ --gradient_checkpointing \ --with_tracking</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contamination checks</h2><a id="user-content-contamination-checks" class="anchor" aria-label="Permalink: Contamination checks" href="#contamination-checks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We release our scripts for measuring the overlap between instruction tuning datasets and evaluation datasets in <code>./decontamination</code>. See the <a href="/allenai/open-instruct/blob/main/decontamination/README.md">README</a> for more details.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Developing</h3><a id="user-content-developing" class="anchor" aria-label="Permalink: Developing" href="#developing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">When submitting a PR to this repo, we check the core code in <code>open_instruct/</code> for style with the following:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="make style make quality"><pre class="notranslate"><code>make style make quality </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Repo structure</h3><a id="user-content-repo-structure" class="anchor" aria-label="Permalink: Repo structure" href="#repo-structure"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="├── assets/ <- Images, licenses, etc. ├── configs/ | ├── beaker_configs/ <- AI2 Beaker configs | ├── ds_configs/ <- DeepSpeed configs | └── train_configs/ <- Training configs ├── decontamination/ <- Scripts for measuring train-eval overlap ├── eval/ <- Evaluation suite for fine-tuned models ├── human_eval/ <- Human evaluation interface (not maintained) ├── open_instruct/ <- Source code (flat) ├── quantize/ <- Scripts for quantization ├── scripts/ <- Core training and evaluation scripts └── Dockerfile <- Dockerfile"><pre class="notranslate"><code>├── assets/ <- Images, licenses, etc. ├── configs/ | ├── beaker_configs/ <- AI2 Beaker configs | ├── ds_configs/ <- DeepSpeed configs | └── train_configs/ <- Training configs ├── decontamination/ <- Scripts for measuring train-eval overlap ├── eval/ <- Evaluation suite for fine-tuned models ├── human_eval/ <- Human evaluation interface (not maintained) ├── open_instruct/ <- Source code (flat) ├── quantize/ <- Scripts for quantization ├── scripts/ <- Core training and evaluation scripts └── Dockerfile <- Dockerfile </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Licensing</h2><a id="user-content-licensing" class="anchor" aria-label="Permalink: Licensing" href="#licensing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This codebase is licensed under Apache 2.0 as given in <a href="/allenai/open-instruct/blob/main/LICENSE">LICENSE</a>.</p> <p dir="auto">The license we use for V1 models released (along with the base model licenses) can be found in <a href="/allenai/open-instruct/blob/main/assets/model_licenses/tulu_license.txt">assets/model_licenses/tulu_license.txt</a> - just replace <code><MODELNAME></code> with the actual model name (i.e., the name on HuggingFace).</p> <p dir="auto">V2 models are licensed under the <a href="https://allenai.org/licenses/impact-lr" rel="nofollow">low-risk AI2 ImpACT license</a>. See <a href="https://allenai.org/impact-license" rel="nofollow">here</a> for more details.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgements</h2><a id="user-content-acknowledgements" class="anchor" aria-label="Permalink: Acknowledgements" href="#acknowledgements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Open Instruct is a project that benefitd from many open-source projects and libraries. We would like to particularly thank the folloiwng projects:</p> <ul dir="auto"> <li><a href="https://github.com/huggingface/transformers">HuggingFace Transformers</a>: We adapted Hugging Face's Trainer for our finetuning scripts.</li> <li><a href="https://github.com/huggingface/trl">HuggingFace TRL</a> and <a href="https://github.com/eric-mitchell/direct-preference-optimization">eric-mitchell/direct-preference-optimization</a>: our preference tuning code is adapted from TRL and from Eric Mitchell's DPO code.</li> <li>OpenAI's <a href="https://github.com/openai/lm-human-preferences">lm-human-preferences</a>, <a href="https://github.com/openai/summarize-from-feedback">summarize-from-feedback</a>, and <a href="https://github.com/vwxyzjn/summarize_from_feedback_details">vwxyzjn/summarize_from_feedback_details</a>: Our core PPO code is adapted from OpenAI's original RLHF code and <a href="https://openreview.net/forum?id=kHO2ZTa8e3" rel="nofollow">Huang et al (2024)'s reproduction work</a> of OpenAI's summarize from feedback work.</li> <li><a href="https://github.com/OpenRLHF/OpenRLHF">OpenRLHF</a>: We adapted OpenRLHF's Ray + vLLM distributed code for scaling up PPO RLVR training into the 70B scale.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you used this repository or our models, please cite our work:</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@misc{wang2023far, title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources}, author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi}, year={2023}, eprint={2306.04751}, archivePrefix={arXiv}, primaryClass={cs.CL} }"><pre><span class="pl-k">@misc</span>{<span class="pl-en">wang2023far</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2023<span class="pl-pds">}</span></span>, <span class="pl-s">eprint</span>=<span class="pl-s"><span class="pl-pds">{</span>2306.04751<span class="pl-pds">}</span></span>, <span class="pl-s">archivePrefix</span>=<span class="pl-s"><span class="pl-pds">{</span>arXiv<span class="pl-pds">}</span></span>, <span class="pl-s">primaryClass</span>=<span class="pl-s"><span class="pl-pds">{</span>cs.CL<span class="pl-pds">}</span></span> }</pre></div> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@misc{ivison2023camels, title={Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2}, author={Hamish Ivison and Yizhong Wang and Valentina Pyatkin and Nathan Lambert and Matthew Peters and Pradeep Dasigi and Joel Jang and David Wadden and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi}, year={2023}, eprint={2311.10702}, archivePrefix={arXiv}, primaryClass={cs.CL} }"><pre><span class="pl-k">@misc</span>{<span class="pl-en">ivison2023camels</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Hamish Ivison and Yizhong Wang and Valentina Pyatkin and Nathan Lambert and Matthew Peters and Pradeep Dasigi and Joel Jang and David Wadden and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2023<span class="pl-pds">}</span></span>, <span class="pl-s">eprint</span>=<span class="pl-s"><span class="pl-pds">{</span>2311.10702<span class="pl-pds">}</span></span>, <span class="pl-s">archivePrefix</span>=<span class="pl-s"><span class="pl-pds">{</span>arXiv<span class="pl-pds">}</span></span>, <span class="pl-s">primaryClass</span>=<span class="pl-s"><span class="pl-pds">{</span>cs.CL<span class="pl-pds">}</span></span> }</pre></div> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@misc{ivison2024unpacking, title={Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback}, author={Hamish Ivison and Yizhong Wang and Jiacheng Liu and Zeqiu Wu and Valentina Pyatkin and Nathan Lambert and Noah A. Smith and Yejin Choi and Hannaneh Hajishirzi}, year={2024}, eprint={2406.09279}, archivePrefix={arXiv}, primaryClass={cs.CL}, }"><pre><span class="pl-k">@misc</span>{<span class="pl-en">ivison2024unpacking</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Hamish Ivison and Yizhong Wang and Jiacheng Liu and Zeqiu Wu and Valentina Pyatkin and Nathan Lambert and Noah A. Smith and Yejin Choi and Hannaneh Hajishirzi<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2024<span class="pl-pds">}</span></span>, <span class="pl-s">eprint</span>=<span class="pl-s"><span class="pl-pds">{</span>2406.09279<span class="pl-pds">}</span></span>, <span class="pl-s">archivePrefix</span>=<span class="pl-s"><span class="pl-pds">{</span>arXiv<span class="pl-pds">}</span></span>, <span class="pl-s">primaryClass</span>=<span class="pl-s"><span class="pl-pds">{</span>cs.CL<span class="pl-pds">}</span></span>, }</pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="rtGHebhBCwkBHSqCv0piKvUCp3rgkMqEZae19Xj2bnfhtV757587KnHKlg/l8aF47ao3Y+FYYQ1ZSmhC5pjFUA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div 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