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22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 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0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_advanced_security","context":"product","tag":"link","label":"github_advanced_security_link_product_navbar"}" href="https://github.com/security/advanced-security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" 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0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 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</div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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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"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 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="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" 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dir=\"auto\"\u003emLoRA\u003c/p\u003e\u003c/h1\u003e\u003ca id=\"user-content--mlora\" class=\"anchor\" aria-label=\"Permalink: mLoRA\" href=\"#-mlora\"\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=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 align=\"center\" tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\n \u003cp dir=\"auto\"\u003eAn Efficient \"Factory\" to Build Multiple LoRA Adapters\u003c/p\u003e\n\u003c/h3\u003e\u003ca id=\"user-content---an-efficient-factory-to-build-multiple-lora-adapters\" class=\"anchor\" aria-label=\"Permalink: An Efficient \u0026quot;Factory\u0026quot; to Build Multiple LoRA Adapters\" href=\"#--an-efficient-factory-to-build-multiple-lora-adapters\"\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\"\u003e\u003ca href=\"https://github.com/TUDB-Labs/mLoRA/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a2c98decab3eff184e93422585b4628e83b4b3e8c815765f181aa2773df8a4cd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f545544422d4c6162732f6d4c6f52413f6c6f676f3d476974487562\" alt=\"\" data-canonical-src=\"https://img.shields.io/github/stars/TUDB-Labs/mLoRA?logo=GitHub\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3babc2b57a9c150f14b8e4c7996e15e22c4eec64d23da357140599063fdfd336/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f545544422d4c6162732f6d4c6f5241\" alt=\"\" data-canonical-src=\"https://img.shields.io/github/license/TUDB-Labs/mLoRA\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7fbabb7a4357fa738873542af7ce84d0fa2855d355ffb373c39e003c38bc8955/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f545544422d4c6162732f6d4c6f5241\" alt=\"\" data-canonical-src=\"https://img.shields.io/github/languages/top/TUDB-Labs/mLoRA\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/TUDB-Labs/mLoRA/releases/latest\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/43a3bb5c620f29d9b223431308e33cab4b7bb70117a34414969022ed750a4181/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f545544422d4c6162732f6d4c6f5241\" alt=\"\" data-canonical-src=\"https://img.shields.io/github/v/release/TUDB-Labs/mLoRA\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/TUDB-Labs/mLoRA/actions/workflows/main-finetune-test.yaml\"\u003e\u003cimg src=\"https://github.com/TUDB-Labs/mLoRA/actions/workflows/main-finetune-test.yaml/badge.svg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/TUDB-Labs/mLoRA/blob/main/docs/assets/theme.webp\"\u003e\u003cimg src=\"/TUDB-Labs/mLoRA/raw/main/docs/assets/theme.webp\" width=\"50%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003emLoRA (a.k.a Multi-LoRA Fine-Tune) is an open-source framework designed for efficient fine-tuning of multiple Large Language Models (LLMs) using LoRA and its variants. Key features of mLoRA include:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eConcurrent fine-tuning of multiple LoRA adapters.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eShared base model among multiple LoRA adapters.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eEfficient pipeline parallelism algorithm.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSupport for multiple LoRA variant algorithms and various base models.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSupport for multiple reinforcement learning preference alignment algorithms.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe end-to-end architecture of the mLoRA is shown in the figure:\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/TUDB-Labs/mLoRA/blob/main/docs/assets/architecture.jpg\"\u003e\u003cimg src=\"/TUDB-Labs/mLoRA/raw/main/docs/assets/architecture.jpg\" width=\"70%\u0026quot;\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLatest News\u003c/h2\u003e\u003ca id=\"user-content-latest-news\" class=\"anchor\" aria-label=\"Permalink: Latest News\" href=\"#latest-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[2025/01] mLoRA has been accepted by VLDB'25\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuickstart\u003c/h2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\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\"\u003eFirstly, you should clone this repository and install dependencies (or use our image):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Clone Repository\ngit clone https://github.com/TUDB-Labs/mLoRA\ncd mLoRA\n# Install requirements need the Python \u0026gt;= 3.12\npip install .\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Clone Repository\u003c/span\u003e\ngit clone https://github.com/TUDB-Labs/mLoRA\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e mLoRA\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install requirements need the Python \u0026gt;= 3.12\u003c/span\u003e\npip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ccode\u003emlora_train.py\u003c/code\u003e code is a starting point for batch fine-tuning LoRA adapters.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python mlora_train.py \\\n --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \\\n --config demo/lora/lora_case_1.yaml\"\u003e\u003cpre\u003epython mlora_train.py \\\n --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \\\n --config demo/lora/lora_case_1.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can check the adapters' configuration in \u003ca href=\"/TUDB-Labs/mLoRA/blob/main/demo\"\u003edemo\u003c/a\u003e folder, there are some configuration regarding the use of different LoRA variants and reinforcement learning preference alignment algorithms.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor further detailed usage information, please use \u003ccode\u003e--help\u003c/code\u003e option:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python mlora_train.py --help\"\u003e\u003cpre\u003epython mlora_train.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeployment using pipeline parallelism\u003c/h2\u003e\u003ca id=\"user-content-deployment-using-pipeline-parallelism\" class=\"anchor\" aria-label=\"Permalink: Deployment using pipeline parallelism\" href=\"#deployment-using-pipeline-parallelism\"\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\"\u003eSimilar to Quickstart, the command to start in a two-node environment is as follows:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNOTE1: Use environment variables \u003ccode\u003eMASTER_ADDR/MASTER_PORT\u003c/code\u003e to set the master node.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNOTE2: Set balance, indicating the number of decoder layers allocated to each rank.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# in the first node\nexport MASTER_ADDR=master.svc.cluster.local\nexport MASTER_PORT=12355\npython mlora_pp_train.py \\\n --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \\\n --config demo/lora/lora_case_1.yaml \\\n --pipeline \\\n --device \u0026quot;cuda:0\u0026quot; \\\n --rank 0 \\\n --balance 12 13 \\\n --no-recompute \\\n --precision fp32\n\n# in the second node\nexport MASTER_ADDR=master.svc.cluster.local\nexport MASTER_PORT=12355\npython mlora_pp_train.py \\\n --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \\\n --config demo/lora/lora_case_1.yaml \\\n --pipeline \\\n --device \u0026quot;cuda:1\u0026quot; \\\n --rank 1 \\\n --balance 12 13 \\\n --no-recompute \\\n --precision fp32\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e in the first node\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MASTER_ADDR=master.svc.cluster.local\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MASTER_PORT=12355\npython mlora_pp_train.py \\\n --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \\\n --config demo/lora/lora_case_1.yaml \\\n --pipeline \\\n --device \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ecuda:0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --rank 0 \\\n --balance 12 13 \\\n --no-recompute \\\n --precision fp32\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e in the second node\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MASTER_ADDR=master.svc.cluster.local\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MASTER_PORT=12355\npython mlora_pp_train.py \\\n --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \\\n --config demo/lora/lora_case_1.yaml \\\n --pipeline \\\n --device \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ecuda:1\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --rank 1 \\\n --balance 12 13 \\\n --no-recompute \\\n --precision fp32\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuickstart with Docker\u003c/h2\u003e\u003ca id=\"user-content-quickstart-with-docker\" class=\"anchor\" aria-label=\"Permalink: Quickstart with Docker\" href=\"#quickstart-with-docker\"\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\"\u003emLoRA offers an official Docker image for quick start and development, The image is available on Dockerhub Packages registry.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFirst, you should pull the latest image (the image also use for development):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker pull yezhengmaolove/mlora:latest\"\u003e\u003cpre\u003edocker pull yezhengmaolove/mlora:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDeploy and enter a container to run mLoRA:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker run -itd --runtime nvidia --gpus all \\\n -v ~/your_dataset_dir:/dataset \\\n -v ~/your_model_dir:/model \\\n -p \u0026lt;host_port\u0026gt;:22 \\\n --name mlora \\\n yezhengmaolove/mlora:latest\n# when the container started, use the ssh to login\n# the default password is mlora@123\nssh root@localhost -p \u0026lt;host_port\u0026gt;\n# pull the latest code and run the mlora\ncd /mLoRA\ngit pull\npython mlora_train.py \\\n --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \\\n --config demo/lora/lora_case_1.yaml\"\u003e\u003cpre\u003edocker run -itd --runtime nvidia --gpus all \\\n -v \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/your_dataset_dir:/dataset \\\n -v \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/your_model_dir:/model \\\n -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehost_port\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:22 \\\n --name mlora \\\n yezhengmaolove/mlora:latest\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e when the container started, use the ssh to login\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the default password is mlora@123\u003c/span\u003e\nssh root@localhost -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehost_port\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e pull the latest code and run the mlora\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /mLoRA\ngit pull\npython mlora_train.py \\\n --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \\\n --config demo/lora/lora_case_1.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeploy as service with Docker\u003c/h2\u003e\u003ca id=\"user-content-deploy-as-service-with-docker\" class=\"anchor\" aria-label=\"Permalink: Deploy as service with Docker\" href=\"#deploy-as-service-with-docker\"\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 can deploy mLoAR as a service to continuously receive user requests and perform fine-tuning task.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFirst, you should pull the latest image (use same image for deploy):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker pull yezhengmaolove/mlora:latest\"\u003e\u003cpre\u003edocker pull yezhengmaolove/mlora:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDeploy our mLoRA server:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker run -itd --runtime nvidia --gpus all \\\n -v ~/your_dataset_cache_dir:/cache \\\n -v ~/your_model_dir:/model \\\n -p \u0026lt;host_port\u0026gt;:8000 \\\n --name mlora_server \\\n -e \u0026quot;BASE_MODEL=TinyLlama/TinyLlama-1.1B-Chat-v0.4\u0026quot; \\\n -e \u0026quot;STORAGE_DIR=/cache\u0026quot; \\\n yezhengmaolove/mlora:latest /bin/bash /opt/deploy.sh\"\u003e\u003cpre\u003edocker run -itd --runtime nvidia --gpus all \\\n -v \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/your_dataset_cache_dir:/cache \\\n -v \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/your_model_dir:/model \\\n -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehost_port\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:8000 \\\n --name mlora_server \\\n -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBASE_MODEL=TinyLlama/TinyLlama-1.1B-Chat-v0.4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSTORAGE_DIR=/cache\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n yezhengmaolove/mlora:latest /bin/bash /opt/deploy.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOnce the service is deployed, install and use \u003ccode\u003emlora_cli.py\u003c/code\u003e to interact with the server.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# install the client tools\npip install mlora-cli\n# use the mlora cli tool to connect to mlora server\nmlora_cli\n(mLoRA) set port \u0026lt;host_port\u0026gt;\n(mLoRA) set host http://\u0026lt;host_ip\u0026gt;\n# and enjoy it!!\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install the client tools\u003c/span\u003e\npip install mlora-cli\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e use the mlora cli tool to connect to mlora server\u003c/span\u003e\nmlora_cli\n(mLoRA) \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e port \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehost_port\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n(mLoRA) \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e host http://\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehost_ip\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e and enjoy it!!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdetails\u003e\n\u003csummary\u003eStep-by-step\u003c/summary\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep1. Download the mlora image and install the mlora_cli\u003c/h3\u003e\u003ca id=\"user-content-step1-download-the-mlora-image-and-install-the-mlora_cli\" class=\"anchor\" aria-label=\"Permalink: Step1. Download the mlora image and install the mlora_cli\" href=\"#step1-download-the-mlora-image-and-install-the-mlora_cli\"\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=\"docker pull yezhengmaolove/mlora:latest\npip install mlora-cli\"\u003e\u003cpre\u003edocker pull yezhengmaolove/mlora:latest\npip install mlora-cli\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://asciinema.org/a/TfYrIsXgfZeMxPRrzOkWZ7T4b\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96834a6fe71bdf8f5dbe03c534589a708601c7392b87e9a60c030438e911c668/68747470733a2f2f61736369696e656d612e6f72672f612f5466597249735867665a654d785052727a4f6b575a375434622e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/TfYrIsXgfZeMxPRrzOkWZ7T4b.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep2. Start the mlora server with Docker\u003c/h3\u003e\u003ca id=\"user-content-step2-start-the-mlora-server-with-docker\" class=\"anchor\" aria-label=\"Permalink: Step2. Start the mlora server with Docker\" href=\"#step2-start-the-mlora-server-with-docker\"\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=\"# first, we create a cache dir in host for cache some file\nmkdir ~/cache\n# second, we manually download the model weights from Hugging Face.\nmkdir ~/model \u0026amp;\u0026amp; cd ~/model\ngit clone https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0\n# we map port 8000 used by the mlora server to port 1288 on the host machine.\n# the BASE_MODEL environment variable indicates the path of the base model used by mlora.\n# the STORAGE_DIR environment variable indicates the path where datasets and lora adapters are stored.\n# we use the script /opt/deploy.sh in container to start the server.\ndocker run -itd --runtime nvidia --gpus all \\\n -v ~/cache:/cache \\\n -v ~/model:/model \\\n -p 1288:8000 \\\n --name mlora_server \\\n -e \u0026quot;BASE_MODEL=/model/TinyLlama-1.1B-Chat-v1.0\u0026quot; \\\n -e \u0026quot;STORAGE_DIR=/cache\u0026quot; \\\n yezhengmaolove/mlora:latest /bin/bash /opt/deploy.sh\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e first, we create a cache dir in host for cache some file\u003c/span\u003e\nmkdir \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/cache\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e second, we manually download the model weights from Hugging Face.\u003c/span\u003e\nmkdir \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/model \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/model\ngit clone https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e we map port 8000 used by the mlora server to port 1288 on the host machine.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the BASE_MODEL environment variable indicates the path of the base model used by mlora.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the STORAGE_DIR environment variable indicates the path where datasets and lora adapters are stored.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e we use the script /opt/deploy.sh in container to start the server.\u003c/span\u003e\ndocker run -itd --runtime nvidia --gpus all \\\n -v \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/cache:/cache \\\n -v \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/model:/model \\\n -p 1288:8000 \\\n --name mlora_server \\\n -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBASE_MODEL=/model/TinyLlama-1.1B-Chat-v1.0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSTORAGE_DIR=/cache\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n yezhengmaolove/mlora:latest /bin/bash /opt/deploy.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://asciinema.org/a/LrLH0jU176NQNfawHpCITaLGx\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e5ea7ff6046c75f7cc81fb3c357fab918f4ce7247956443e83241a8f86465f31/68747470733a2f2f61736369696e656d612e6f72672f612f4c724c48306a553137364e514e6661774870434954614c47782e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/LrLH0jU176NQNfawHpCITaLGx.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep3. use mlora_cli tool link to mlora server\u003c/h3\u003e\u003ca id=\"user-content-step3-use-mlora_cli-tool-link-to-mlora-server\" class=\"anchor\" aria-label=\"Permalink: Step3. use mlora_cli tool link to mlora server\" href=\"#step3-use-mlora_cli-tool-link-to-mlora-server\"\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 use mlora_cli link to the server \u003ca href=\"http://127.0.0.1:1288\" rel=\"nofollow\"\u003ehttp://127.0.0.1:1288\u003c/a\u003e (must use the http protocal)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(mLoRA) set port 1288\n(mLoRA) set host http://127.0.0.1\"\u003e\u003cpre\u003e(mLoRA) \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e port 1288\n(mLoRA) \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e host http://127.0.0.1\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://asciinema.org/a/GN1NBc2MEN8GrmcIasmIMDjNa\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3539094e002773a5219315302754848b89d4a558456a558a6f4e701fddce606/68747470733a2f2f61736369696e656d612e6f72672f612f474e314e4263324d454e3847726d634961736d494d446a4e612e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/GN1NBc2MEN8GrmcIasmIMDjNa.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep4. upload some data file for train.\u003c/h3\u003e\u003ca id=\"user-content-step4-upload-some-data-file-for-train\" class=\"anchor\" aria-label=\"Permalink: Step4. upload some data file for train.\" href=\"#step4-upload-some-data-file-for-train\"\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 use the Stanford Alpaca dataset as a demo, the data just like below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-json notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"[{\u0026quot;instruction\u0026quot;: \u0026quot;\u0026quot;, \u0026quot;input\u0026quot;: \u0026quot;\u0026quot;, \u0026quot;output\u0026quot;: }, {...}]\"\u003e\u003cpre\u003e[{\u003cspan class=\"pl-ent\"\u003e\"instruction\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-ent\"\u003e\"input\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-ent\"\u003e\"output\"\u003c/span\u003e: }, {\u003cspan class=\"pl-ii\"\u003e...\u003c/span\u003e}]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(mLoRA) file upload\n? file type: train data\n? name: alpaca\n? file path: /home/yezhengmao/alpaca-lora/alpaca_data.json\"\u003e\u003cpre\u003e(mLoRA) file upload\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e file type: train data\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e name: alpaca\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e file path: /home/yezhengmao/alpaca-lora/alpaca_data.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://asciinema.org/a/KN41mnlMShZWDs3dIrd64L4nS\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34c0311b12da341514c95c98bf83611d16f1160f74a4984f04f7a86757a28cf5/68747470733a2f2f61736369696e656d612e6f72672f612f4b4e34316d6e6c4d53685a574473336449726436344c346e532e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/KN41mnlMShZWDs3dIrd64L4nS.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep5. upload some template to provide a structured format for generating prompts\u003c/h3\u003e\u003ca id=\"user-content-step5-upload-some-template-to-provide-a-structured-format-for-generating-prompts\" class=\"anchor\" aria-label=\"Permalink: Step5. upload some template to provide a structured format for generating prompts\" href=\"#step5-upload-some-template-to-provide-a-structured-format-for-generating-prompts\"\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\"\u003ethe template in a yaml file, and write by templating language Jinja2, see the demo/prompt.yaml file\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ethe data file you upload can be considered as array data, with the elements in the array being of dictionary type. we consider each element as a data point in the template.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(mLoRA) file upload\n? file type: prompt template\n? name: simple_prompt\n? file path: /home/yezhengmao/mLoRA/demo/prompt.yaml\"\u003e\u003cpre\u003e(mLoRA) file upload\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e file type: prompt template\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e name: simple_prompt\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e file path: /home/yezhengmao/mLoRA/demo/prompt.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://asciinema.org/a/SFY8H0K4DppVvqmQCVuOuThoz\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6ebee91269e0c6a9594f10038de7af2a1232edb0f0ab621ac326db18c905d2d9/68747470733a2f2f61736369696e656d612e6f72672f612f5346593848304b344470705676716d514356754f7554686f7a2e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/SFY8H0K4DppVvqmQCVuOuThoz.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep6. create a dataset\u003c/h3\u003e\u003ca id=\"user-content-step6-create-a-dataset\" class=\"anchor\" aria-label=\"Permalink: Step6. create a dataset\" href=\"#step6-create-a-dataset\"\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 create a dataset, the dataset consists of data, a template, and the corresponding prompter.\nwe can use \u003ccode\u003edataset showcase\u003c/code\u003e command to check the if the prompts are generated correctly.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"(mLoRA) dataset create\n? name: alpaca_dataset\n? train data file: alpaca\n? prompt template file: simple_prompt\n? prompter: instruction\n? data preprocessing: default\n(mLoRA) dataset showcase\n? dataset name: alpaca_dataset\"\u003e\u003cpre\u003e(mLoRA) dataset create\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e name: alpaca_dataset\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e train data file: alpaca\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e prompt template file: simple_prompt\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e prompter: instruction\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e data preprocessing: default\n(mLoRA) dataset showcase\n\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e dataset name: alpaca_dataset\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://asciinema.org/a/mxpwo6gWihjEEsJ0dfcXG98cM\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb8c685349e406543349909e3bdaec2f277f14dc03d7333d77d06bbff9d4dcaa/68747470733a2f2f61736369696e656d612e6f72672f612f6d7870776f36675769686a4545734a3064666358473938634d2e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/mxpwo6gWihjEEsJ0dfcXG98cM.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep7. create a adapter\u003c/h3\u003e\u003ca id=\"user-content-step7-create-a-adapter\" class=\"anchor\" aria-label=\"Permalink: Step7. create a adapter\" href=\"#step7-create-a-adapter\"\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\"\u003enow we can use \u003ccode\u003eadapter create\u003c/code\u003e command to create a adapter for train.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://asciinema.org/a/Wf4PHfoGC0PCcciGHOXCkX0xj\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/64a818667ef93076edf24c52360ab3f186c397a51e866ee1a26e98be4d799419/68747470733a2f2f61736369696e656d612e6f72672f612f5766345048666f474330504363636947484f58436b5830786a2e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/Wf4PHfoGC0PCcciGHOXCkX0xj.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep8. !!!! submit task to train !!!!\u003c/h3\u003e\u003ca id=\"user-content-step8--submit-task-to-train-\" class=\"anchor\" aria-label=\"Permalink: Step8. !!!! submit task to train !!!!\" href=\"#step8--submit-task-to-train-\"\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\"\u003eFinally, we can submit the task to train our adapter using the defined dataset.\nNOTE: you can continuously submit or terminal training tasks.\nuse the \u003ccode\u003eadapter ls\u003c/code\u003e or \u003ccode\u003etask ls\u003c/code\u003e to check the tasks' status\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://asciinema.org/a/vr8f1XtA0CBULGIP81w2bakkE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/297f6c1a9017e0ca72c955bfae77e042331d1a6d3c1c1afc72336e88b3d92797/68747470733a2f2f61736369696e656d612e6f72672f612f7672386631587441304342554c4749503831773262616b6b452e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/vr8f1XtA0CBULGIP81w2bakkE.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhy you should use mLoRA\u003c/h2\u003e\u003ca id=\"user-content-why-you-should-use-mlora\" class=\"anchor\" aria-label=\"Permalink: Why you should use mLoRA\" href=\"#why-you-should-use-mlora\"\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\"\u003eUsing mLoRA can save significant computational and memory resources when training multiple adapters simultaneously.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHigh performance on consumer hardware\u003c/h3\u003e\u003ca id=\"user-content-high-performance-on-consumer-hardware\" class=\"anchor\" aria-label=\"Permalink: High performance on consumer hardware\" href=\"#high-performance-on-consumer-hardware\"\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 fine-tuned multiple LoRA adapters using four A6000 graphics cards with fp32 precision and without using checkpointing and any quantization techniques:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eModel\u003c/th\u003e\n\u003cth\u003emLoRA (tokens/s)\u003c/th\u003e\n\u003cth\u003ePEFT-LoRA with FSDP (tokens/s)\u003c/th\u003e\n\u003cth\u003ePEFT-LoRA with TP (tokens/s)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/meta-llama/llama-2-7b\" rel=\"nofollow\"\u003ellama-2-7b (32fp)\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2364\u003c/td\u003e\n\u003ctd\u003e1750\u003c/td\u003e\n\u003ctd\u003e1500\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/meta-llama/llama-2-13b\" rel=\"nofollow\"\u003ellama-2-13b (32fp)\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e1280\u003c/td\u003e\n\u003ctd\u003eOOM\u003c/td\u003e\n\u003ctd\u003e875\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSupported model\u003c/h3\u003e\u003ca id=\"user-content-supported-model\" class=\"anchor\" aria-label=\"Permalink: Supported model\" href=\"#supported-model\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003eModel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e✓\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://llama.meta.com/\" rel=\"nofollow\"\u003eLLaMA\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSupported LoRA variants\u003c/h3\u003e\u003ca id=\"user-content-supported-lora-variants\" class=\"anchor\" aria-label=\"Permalink: Supported LoRA variants\" href=\"#supported-lora-variants\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003eVariant\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e✓\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2305.14314\" rel=\"nofollow\"\u003eQLoRA\u003c/a\u003e,NIPS,2023\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e✓\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2402.12354\" rel=\"nofollow\"\u003eLoRA+\u003c/a\u003e,ICML,2024\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e✓\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2310.11454\" rel=\"nofollow\"\u003eVeRA\u003c/a\u003e,ICLR,2024\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e✓\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2402.09353\" rel=\"nofollow\"\u003eDoRA\u003c/a\u003e,ICML,2024\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSupported preference alignment algorithms\u003c/h3\u003e\u003ca id=\"user-content-supported-preference-alignment-algorithms\" class=\"anchor\" aria-label=\"Permalink: Supported preference alignment algorithms\" href=\"#supported-preference-alignment-algorithms\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003eVariant\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e✓\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2305.18290\" rel=\"nofollow\"\u003eDPO\u003c/a\u003e,NeurIPS,2024\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e✓\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2401.08417\" rel=\"nofollow\"\u003eCPO\u003c/a\u003e,ICML,2024\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e✓\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2402.11138\" rel=\"nofollow\"\u003eCIT\u003c/a\u003e,arXiv,2024\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDocument\u003c/h2\u003e\u003ca id=\"user-content-document\" class=\"anchor\" aria-label=\"Permalink: Document\" href=\"#document\"\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\u003eHelp Document[TODO]\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/TUDB-Labs/mLoRA/blob/main/docs/design.md\"\u003eDesign Document\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/TUDB-Labs/mLoRA/blob/main/docs/develop_new_adapter.md\"\u003eHow to develop a new adapter\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/TUDB-Labs/mLoRA/blob/main/benchmarks/README.md\"\u003eHow to reproduce the 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\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\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 welcome contributions to improve this repository! Please review the contribution guidelines before submitting pull requests or issues.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFork the repository.\nCreate a new branch for your feature or fix.\nSubmit a pull request with a detailed explanation of your changes.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can use the pre-commit to check your code.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Install requirements\npip install .[ci_test]\nln -s ../../.github/workflows/pre-commit .git/hooks/pre-commit\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install requirements\u003c/span\u003e\npip install .[ci_test]\nln -s ../../.github/workflows/pre-commit .git/hooks/pre-commit\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOr just call the script to check your code\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\".github/workflows/pre-commit\"\u003e\u003cpre\u003e.github/workflows/pre-commit\u003c/pre\u003e\u003c/div\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\"\u003ePlease cite the repo if you use the code in this repo.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@misc{ye2024mlorafinetuningloraadapters,\n title={mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs}, \n author={Zhengmao Ye and Dengchun Li and Zetao Hu and Tingfeng Lan and Jian Sha and Sicong Zhang and Lei Duan and Jie Zuo and Hui Lu and Yuanchun Zhou and Mingjie Tang},\n year={2024},\n eprint={2312.02515},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2312.02515}, \n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@misc\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003eye2024mlorafinetuningloraadapters\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003emLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs\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\u003eZhengmao Ye and Dengchun Li and Zetao Hu and Tingfeng Lan and Jian Sha and Sicong Zhang and Lei Duan and Jie Zuo and Hui Lu and Yuanchun Zhou and Mingjie Tang\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\u003e2312.02515\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.LG\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eurl\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003ehttps://arxiv.org/abs/2312.02515\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e, \n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCopyright\u003c/h2\u003e\u003ca id=\"user-content-copyright\" class=\"anchor\" aria-label=\"Permalink: Copyright\" href=\"#copyright\"\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\"\u003eCopyright © 2024 All Rights Reserved.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis project is licensed under the \u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache 2.0 License\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"Licensed under the Apache License, Version 2.0 (the \u0026quot;License\u0026quot;);\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \u0026quot;AS IS\u0026quot; BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"mLoRA","anchor":"-mlora","htmlText":"mLoRA"},{"level":3,"text":"An Efficient \"Factory\" to Build Multiple LoRA Adapters","anchor":"--an-efficient-factory-to-build-multiple-lora-adapters","htmlText":"An Efficient \"Factory\" to Build Multiple LoRA Adapters"},{"level":2,"text":"Latest News","anchor":"latest-news","htmlText":"Latest News"},{"level":2,"text":"Quickstart","anchor":"quickstart","htmlText":"Quickstart"},{"level":2,"text":"Deployment using pipeline parallelism","anchor":"deployment-using-pipeline-parallelism","htmlText":"Deployment using pipeline parallelism"},{"level":2,"text":"Quickstart with Docker","anchor":"quickstart-with-docker","htmlText":"Quickstart with Docker"},{"level":2,"text":"Deploy as service with Docker","anchor":"deploy-as-service-with-docker","htmlText":"Deploy as service with Docker"},{"level":3,"text":"Step1. Download the mlora image and install the mlora_cli","anchor":"step1-download-the-mlora-image-and-install-the-mlora_cli","htmlText":"Step1. Download the mlora image and install the mlora_cli"},{"level":3,"text":"Step2. Start the mlora server with Docker","anchor":"step2-start-the-mlora-server-with-docker","htmlText":"Step2. Start the mlora server with Docker"},{"level":3,"text":"Step3. use mlora_cli tool link to mlora server","anchor":"step3-use-mlora_cli-tool-link-to-mlora-server","htmlText":"Step3. use mlora_cli tool link to mlora server"},{"level":3,"text":"Step4. upload some data file for train.","anchor":"step4-upload-some-data-file-for-train","htmlText":"Step4. upload some data file for train."},{"level":3,"text":"Step5. upload some template to provide a structured format for generating prompts","anchor":"step5-upload-some-template-to-provide-a-structured-format-for-generating-prompts","htmlText":"Step5. upload some template to provide a structured format for generating prompts"},{"level":3,"text":"Step6. create a dataset","anchor":"step6-create-a-dataset","htmlText":"Step6. create a dataset"},{"level":3,"text":"Step7. create a adapter","anchor":"step7-create-a-adapter","htmlText":"Step7. create a adapter"},{"level":3,"text":"Step8. !!!! submit task to train !!!!","anchor":"step8--submit-task-to-train-","htmlText":"Step8. !!!! submit task to train !!!!"},{"level":2,"text":"Why you should use mLoRA","anchor":"why-you-should-use-mlora","htmlText":"Why you should use mLoRA"},{"level":3,"text":"High performance on consumer hardware","anchor":"high-performance-on-consumer-hardware","htmlText":"High performance on consumer hardware"},{"level":3,"text":"Supported model","anchor":"supported-model","htmlText":"Supported model"},{"level":3,"text":"Supported LoRA variants","anchor":"supported-lora-variants","htmlText":"Supported LoRA variants"},{"level":3,"text":"Supported preference alignment algorithms","anchor":"supported-preference-alignment-algorithms","htmlText":"Supported preference alignment 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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 undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 align="center" tabindex="-1" class="heading-element" dir="auto"> <p dir="auto">mLoRA</p></h1><a id="user-content--mlora" class="anchor" aria-label="Permalink: mLoRA" href="#-mlora"><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="markdown-heading" dir="auto"><h3 align="center" tabindex="-1" class="heading-element" dir="auto"> <p dir="auto">An Efficient "Factory" to Build Multiple LoRA Adapters</p> </h3><a id="user-content---an-efficient-factory-to-build-multiple-lora-adapters" class="anchor" aria-label="Permalink: An Efficient "Factory" to Build Multiple LoRA Adapters" href="#--an-efficient-factory-to-build-multiple-lora-adapters"><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"><a href="https://github.com/TUDB-Labs/mLoRA/stargazers"><img src="https://camo.githubusercontent.com/a2c98decab3eff184e93422585b4628e83b4b3e8c815765f181aa2773df8a4cd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f545544422d4c6162732f6d4c6f52413f6c6f676f3d476974487562" alt="" data-canonical-src="https://img.shields.io/github/stars/TUDB-Labs/mLoRA?logo=GitHub" style="max-width: 100%;"></a> <a href="http://www.apache.org/licenses/LICENSE-2.0" rel="nofollow"><img src="https://camo.githubusercontent.com/3babc2b57a9c150f14b8e4c7996e15e22c4eec64d23da357140599063fdfd336/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f545544422d4c6162732f6d4c6f5241" alt="" data-canonical-src="https://img.shields.io/github/license/TUDB-Labs/mLoRA" style="max-width: 100%;"></a> <a href="https://www.python.org/" rel="nofollow"><img src="https://camo.githubusercontent.com/7fbabb7a4357fa738873542af7ce84d0fa2855d355ffb373c39e003c38bc8955/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f545544422d4c6162732f6d4c6f5241" alt="" data-canonical-src="https://img.shields.io/github/languages/top/TUDB-Labs/mLoRA" style="max-width: 100%;"></a> <a href="https://github.com/TUDB-Labs/mLoRA/releases/latest"><img src="https://camo.githubusercontent.com/43a3bb5c620f29d9b223431308e33cab4b7bb70117a34414969022ed750a4181/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f545544422d4c6162732f6d4c6f5241" alt="" data-canonical-src="https://img.shields.io/github/v/release/TUDB-Labs/mLoRA" style="max-width: 100%;"></a> <a href="https://github.com/TUDB-Labs/mLoRA/actions/workflows/main-finetune-test.yaml"><img src="https://github.com/TUDB-Labs/mLoRA/actions/workflows/main-finetune-test.yaml/badge.svg" alt="" style="max-width: 100%;"></a></p> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/TUDB-Labs/mLoRA/blob/main/docs/assets/theme.webp"><img src="/TUDB-Labs/mLoRA/raw/main/docs/assets/theme.webp" width="50%" style="max-width: 100%;"></a> </div> <p dir="auto">mLoRA (a.k.a Multi-LoRA Fine-Tune) is an open-source framework designed for efficient fine-tuning of multiple Large Language Models (LLMs) using LoRA and its variants. Key features of mLoRA include:</p> <ul dir="auto"> <li> <p dir="auto">Concurrent fine-tuning of multiple LoRA adapters.</p> </li> <li> <p dir="auto">Shared base model among multiple LoRA adapters.</p> </li> <li> <p dir="auto">Efficient pipeline parallelism algorithm.</p> </li> <li> <p dir="auto">Support for multiple LoRA variant algorithms and various base models.</p> </li> <li> <p dir="auto">Support for multiple reinforcement learning preference alignment algorithms.</p> </li> </ul> <p dir="auto">The end-to-end architecture of the mLoRA is shown in the figure:</p> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/TUDB-Labs/mLoRA/blob/main/docs/assets/architecture.jpg"><img src="/TUDB-Labs/mLoRA/raw/main/docs/assets/architecture.jpg" width="70%"" style="max-width: 100%;"></a> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Latest News</h2><a id="user-content-latest-news" class="anchor" aria-label="Permalink: Latest News" href="#latest-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>[2025/01] mLoRA has been accepted by VLDB'25</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quickstart</h2><a id="user-content-quickstart" class="anchor" aria-label="Permalink: Quickstart" href="#quickstart"><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">Firstly, you should clone this repository and install dependencies (or use our image):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Clone Repository git clone https://github.com/TUDB-Labs/mLoRA cd mLoRA # Install requirements need the Python >= 3.12 pip install ."><pre><span class="pl-c"><span class="pl-c">#</span> Clone Repository</span> git clone https://github.com/TUDB-Labs/mLoRA <span class="pl-c1">cd</span> mLoRA <span class="pl-c"><span class="pl-c">#</span> Install requirements need the Python >= 3.12</span> pip install <span class="pl-c1">.</span></pre></div> <p dir="auto">The <code>mlora_train.py</code> code is a starting point for batch fine-tuning LoRA adapters.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python mlora_train.py \ --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \ --config demo/lora/lora_case_1.yaml"><pre>python mlora_train.py \ --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \ --config demo/lora/lora_case_1.yaml</pre></div> <p dir="auto">You can check the adapters' configuration in <a href="/TUDB-Labs/mLoRA/blob/main/demo">demo</a> folder, there are some configuration regarding the use of different LoRA variants and reinforcement learning preference alignment algorithms.</p> <p dir="auto">For further detailed usage information, please use <code>--help</code> option:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python mlora_train.py --help"><pre>python mlora_train.py --help</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Deployment using pipeline parallelism</h2><a id="user-content-deployment-using-pipeline-parallelism" class="anchor" aria-label="Permalink: Deployment using pipeline parallelism" href="#deployment-using-pipeline-parallelism"><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">Similar to Quickstart, the command to start in a two-node environment is as follows:</p> <p dir="auto">NOTE1: Use environment variables <code>MASTER_ADDR/MASTER_PORT</code> to set the master node.</p> <p dir="auto">NOTE2: Set balance, indicating the number of decoder layers allocated to each rank.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# in the first node export MASTER_ADDR=master.svc.cluster.local export MASTER_PORT=12355 python mlora_pp_train.py \ --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \ --config demo/lora/lora_case_1.yaml \ --pipeline \ --device "cuda:0" \ --rank 0 \ --balance 12 13 \ --no-recompute \ --precision fp32 # in the second node export MASTER_ADDR=master.svc.cluster.local export MASTER_PORT=12355 python mlora_pp_train.py \ --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \ --config demo/lora/lora_case_1.yaml \ --pipeline \ --device "cuda:1" \ --rank 1 \ --balance 12 13 \ --no-recompute \ --precision fp32"><pre><span class="pl-c"><span class="pl-c">#</span> in the first node</span> <span class="pl-k">export</span> MASTER_ADDR=master.svc.cluster.local <span class="pl-k">export</span> MASTER_PORT=12355 python mlora_pp_train.py \ --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \ --config demo/lora/lora_case_1.yaml \ --pipeline \ --device <span class="pl-s"><span class="pl-pds">"</span>cuda:0<span class="pl-pds">"</span></span> \ --rank 0 \ --balance 12 13 \ --no-recompute \ --precision fp32 <span class="pl-c"><span class="pl-c">#</span> in the second node</span> <span class="pl-k">export</span> MASTER_ADDR=master.svc.cluster.local <span class="pl-k">export</span> MASTER_PORT=12355 python mlora_pp_train.py \ --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \ --config demo/lora/lora_case_1.yaml \ --pipeline \ --device <span class="pl-s"><span class="pl-pds">"</span>cuda:1<span class="pl-pds">"</span></span> \ --rank 1 \ --balance 12 13 \ --no-recompute \ --precision fp32</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quickstart with Docker</h2><a id="user-content-quickstart-with-docker" class="anchor" aria-label="Permalink: Quickstart with Docker" href="#quickstart-with-docker"><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">mLoRA offers an official Docker image for quick start and development, The image is available on Dockerhub Packages registry.</p> <p dir="auto">First, you should pull the latest image (the image also use for development):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker pull yezhengmaolove/mlora:latest"><pre>docker pull yezhengmaolove/mlora:latest</pre></div> <p dir="auto">Deploy and enter a container to run mLoRA:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker run -itd --runtime nvidia --gpus all \ -v ~/your_dataset_dir:/dataset \ -v ~/your_model_dir:/model \ -p <host_port>:22 \ --name mlora \ yezhengmaolove/mlora:latest # when the container started, use the ssh to login # the default password is mlora@123 ssh root@localhost -p <host_port> # pull the latest code and run the mlora cd /mLoRA git pull python mlora_train.py \ --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \ --config demo/lora/lora_case_1.yaml"><pre>docker run -itd --runtime nvidia --gpus all \ -v <span class="pl-k">~</span>/your_dataset_dir:/dataset \ -v <span class="pl-k">~</span>/your_model_dir:/model \ -p <span class="pl-k"><</span>host_port<span class="pl-k">></span>:22 \ --name mlora \ yezhengmaolove/mlora:latest <span class="pl-c"><span class="pl-c">#</span> when the container started, use the ssh to login</span> <span class="pl-c"><span class="pl-c">#</span> the default password is mlora@123</span> ssh root@localhost -p <span class="pl-k"><</span>host_port<span class="pl-k">></span> <span class="pl-c"><span class="pl-c">#</span> pull the latest code and run the mlora</span> <span class="pl-c1">cd</span> /mLoRA git pull python mlora_train.py \ --base_model TinyLlama/TinyLlama-1.1B-Chat-v0.4 \ --config demo/lora/lora_case_1.yaml</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Deploy as service with Docker</h2><a id="user-content-deploy-as-service-with-docker" class="anchor" aria-label="Permalink: Deploy as service with Docker" href="#deploy-as-service-with-docker"><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 can deploy mLoAR as a service to continuously receive user requests and perform fine-tuning task.</p> <p dir="auto">First, you should pull the latest image (use same image for deploy):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker pull yezhengmaolove/mlora:latest"><pre>docker pull yezhengmaolove/mlora:latest</pre></div> <p dir="auto">Deploy our mLoRA server:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker run -itd --runtime nvidia --gpus all \ -v ~/your_dataset_cache_dir:/cache \ -v ~/your_model_dir:/model \ -p <host_port>:8000 \ --name mlora_server \ -e "BASE_MODEL=TinyLlama/TinyLlama-1.1B-Chat-v0.4" \ -e "STORAGE_DIR=/cache" \ yezhengmaolove/mlora:latest /bin/bash /opt/deploy.sh"><pre>docker run -itd --runtime nvidia --gpus all \ -v <span class="pl-k">~</span>/your_dataset_cache_dir:/cache \ -v <span class="pl-k">~</span>/your_model_dir:/model \ -p <span class="pl-k"><</span>host_port<span class="pl-k">></span>:8000 \ --name mlora_server \ -e <span class="pl-s"><span class="pl-pds">"</span>BASE_MODEL=TinyLlama/TinyLlama-1.1B-Chat-v0.4<span class="pl-pds">"</span></span> \ -e <span class="pl-s"><span class="pl-pds">"</span>STORAGE_DIR=/cache<span class="pl-pds">"</span></span> \ yezhengmaolove/mlora:latest /bin/bash /opt/deploy.sh</pre></div> <p dir="auto">Once the service is deployed, install and use <code>mlora_cli.py</code> to interact with the server.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# install the client tools pip install mlora-cli # use the mlora cli tool to connect to mlora server mlora_cli (mLoRA) set port <host_port> (mLoRA) set host http://<host_ip> # and enjoy it!!"><pre><span class="pl-c"><span class="pl-c">#</span> install the client tools</span> pip install mlora-cli <span class="pl-c"><span class="pl-c">#</span> use the mlora cli tool to connect to mlora server</span> mlora_cli (mLoRA) <span class="pl-c1">set</span> port <span class="pl-k"><</span>host_port<span class="pl-k">></span> (mLoRA) <span class="pl-c1">set</span> host http://<span class="pl-k"><</span>host_ip<span class="pl-k">></span> <span class="pl-c"><span class="pl-c">#</span> and enjoy it!!</span></pre></div> <details> <summary>Step-by-step</summary> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Step1. Download the mlora image and install the mlora_cli</h3><a id="user-content-step1-download-the-mlora-image-and-install-the-mlora_cli" class="anchor" aria-label="Permalink: Step1. Download the mlora image and install the mlora_cli" href="#step1-download-the-mlora-image-and-install-the-mlora_cli"><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="docker pull yezhengmaolove/mlora:latest pip install mlora-cli"><pre>docker pull yezhengmaolove/mlora:latest pip install mlora-cli</pre></div> <p dir="auto"><a href="https://asciinema.org/a/TfYrIsXgfZeMxPRrzOkWZ7T4b" rel="nofollow"><img src="https://camo.githubusercontent.com/96834a6fe71bdf8f5dbe03c534589a708601c7392b87e9a60c030438e911c668/68747470733a2f2f61736369696e656d612e6f72672f612f5466597249735867665a654d785052727a4f6b575a375434622e737667" alt="asciicast" data-canonical-src="https://asciinema.org/a/TfYrIsXgfZeMxPRrzOkWZ7T4b.svg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Step2. Start the mlora server with Docker</h3><a id="user-content-step2-start-the-mlora-server-with-docker" class="anchor" aria-label="Permalink: Step2. Start the mlora server with Docker" href="#step2-start-the-mlora-server-with-docker"><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="# first, we create a cache dir in host for cache some file mkdir ~/cache # second, we manually download the model weights from Hugging Face. mkdir ~/model && cd ~/model git clone https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 # we map port 8000 used by the mlora server to port 1288 on the host machine. # the BASE_MODEL environment variable indicates the path of the base model used by mlora. # the STORAGE_DIR environment variable indicates the path where datasets and lora adapters are stored. # we use the script /opt/deploy.sh in container to start the server. docker run -itd --runtime nvidia --gpus all \ -v ~/cache:/cache \ -v ~/model:/model \ -p 1288:8000 \ --name mlora_server \ -e "BASE_MODEL=/model/TinyLlama-1.1B-Chat-v1.0" \ -e "STORAGE_DIR=/cache" \ yezhengmaolove/mlora:latest /bin/bash /opt/deploy.sh"><pre><span class="pl-c"><span class="pl-c">#</span> first, we create a cache dir in host for cache some file</span> mkdir <span class="pl-k">~</span>/cache <span class="pl-c"><span class="pl-c">#</span> second, we manually download the model weights from Hugging Face.</span> mkdir <span class="pl-k">~</span>/model <span class="pl-k">&&</span> <span class="pl-c1">cd</span> <span class="pl-k">~</span>/model git clone https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 <span class="pl-c"><span class="pl-c">#</span> we map port 8000 used by the mlora server to port 1288 on the host machine.</span> <span class="pl-c"><span class="pl-c">#</span> the BASE_MODEL environment variable indicates the path of the base model used by mlora.</span> <span class="pl-c"><span class="pl-c">#</span> the STORAGE_DIR environment variable indicates the path where datasets and lora adapters are stored.</span> <span class="pl-c"><span class="pl-c">#</span> we use the script /opt/deploy.sh in container to start the server.</span> docker run -itd --runtime nvidia --gpus all \ -v <span class="pl-k">~</span>/cache:/cache \ -v <span class="pl-k">~</span>/model:/model \ -p 1288:8000 \ --name mlora_server \ -e <span class="pl-s"><span class="pl-pds">"</span>BASE_MODEL=/model/TinyLlama-1.1B-Chat-v1.0<span class="pl-pds">"</span></span> \ -e <span class="pl-s"><span class="pl-pds">"</span>STORAGE_DIR=/cache<span class="pl-pds">"</span></span> \ yezhengmaolove/mlora:latest /bin/bash /opt/deploy.sh</pre></div> <p dir="auto"><a href="https://asciinema.org/a/LrLH0jU176NQNfawHpCITaLGx" rel="nofollow"><img src="https://camo.githubusercontent.com/e5ea7ff6046c75f7cc81fb3c357fab918f4ce7247956443e83241a8f86465f31/68747470733a2f2f61736369696e656d612e6f72672f612f4c724c48306a553137364e514e6661774870434954614c47782e737667" alt="asciicast" data-canonical-src="https://asciinema.org/a/LrLH0jU176NQNfawHpCITaLGx.svg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Step3. use mlora_cli tool link to mlora server</h3><a id="user-content-step3-use-mlora_cli-tool-link-to-mlora-server" class="anchor" aria-label="Permalink: Step3. use mlora_cli tool link to mlora server" href="#step3-use-mlora_cli-tool-link-to-mlora-server"><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 use mlora_cli link to the server <a href="http://127.0.0.1:1288" rel="nofollow">http://127.0.0.1:1288</a> (must use the http protocal)</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(mLoRA) set port 1288 (mLoRA) set host http://127.0.0.1"><pre>(mLoRA) <span class="pl-c1">set</span> port 1288 (mLoRA) <span class="pl-c1">set</span> host http://127.0.0.1</pre></div> <p dir="auto"><a href="https://asciinema.org/a/GN1NBc2MEN8GrmcIasmIMDjNa" rel="nofollow"><img src="https://camo.githubusercontent.com/a3539094e002773a5219315302754848b89d4a558456a558a6f4e701fddce606/68747470733a2f2f61736369696e656d612e6f72672f612f474e314e4263324d454e3847726d634961736d494d446a4e612e737667" alt="asciicast" data-canonical-src="https://asciinema.org/a/GN1NBc2MEN8GrmcIasmIMDjNa.svg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Step4. upload some data file for train.</h3><a id="user-content-step4-upload-some-data-file-for-train" class="anchor" aria-label="Permalink: Step4. upload some data file for train." href="#step4-upload-some-data-file-for-train"><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 use the Stanford Alpaca dataset as a demo, the data just like below:</p> <div class="highlight highlight-source-json notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="[{"instruction": "", "input": "", "output": }, {...}]"><pre>[{<span class="pl-ent">"instruction"</span>: <span class="pl-s"><span class="pl-pds">"</span><span class="pl-pds">"</span></span>, <span class="pl-ent">"input"</span>: <span class="pl-s"><span class="pl-pds">"</span><span class="pl-pds">"</span></span>, <span class="pl-ent">"output"</span>: }, {<span class="pl-ii">...</span>}]</pre></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(mLoRA) file upload ? file type: train data ? name: alpaca ? file path: /home/yezhengmao/alpaca-lora/alpaca_data.json"><pre>(mLoRA) file upload <span class="pl-k">?</span> file type: train data <span class="pl-k">?</span> name: alpaca <span class="pl-k">?</span> file path: /home/yezhengmao/alpaca-lora/alpaca_data.json</pre></div> <p dir="auto"><a href="https://asciinema.org/a/KN41mnlMShZWDs3dIrd64L4nS" rel="nofollow"><img src="https://camo.githubusercontent.com/34c0311b12da341514c95c98bf83611d16f1160f74a4984f04f7a86757a28cf5/68747470733a2f2f61736369696e656d612e6f72672f612f4b4e34316d6e6c4d53685a574473336449726436344c346e532e737667" alt="asciicast" data-canonical-src="https://asciinema.org/a/KN41mnlMShZWDs3dIrd64L4nS.svg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Step5. upload some template to provide a structured format for generating prompts</h3><a id="user-content-step5-upload-some-template-to-provide-a-structured-format-for-generating-prompts" class="anchor" aria-label="Permalink: Step5. upload some template to provide a structured format for generating prompts" href="#step5-upload-some-template-to-provide-a-structured-format-for-generating-prompts"><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">the template in a yaml file, and write by templating language Jinja2, see the demo/prompt.yaml file</p> <p dir="auto">the data file you upload can be considered as array data, with the elements in the array being of dictionary type. we consider each element as a data point in the template.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(mLoRA) file upload ? file type: prompt template ? name: simple_prompt ? file path: /home/yezhengmao/mLoRA/demo/prompt.yaml"><pre>(mLoRA) file upload <span class="pl-k">?</span> file type: prompt template <span class="pl-k">?</span> name: simple_prompt <span class="pl-k">?</span> file path: /home/yezhengmao/mLoRA/demo/prompt.yaml</pre></div> <p dir="auto"><a href="https://asciinema.org/a/SFY8H0K4DppVvqmQCVuOuThoz" rel="nofollow"><img src="https://camo.githubusercontent.com/6ebee91269e0c6a9594f10038de7af2a1232edb0f0ab621ac326db18c905d2d9/68747470733a2f2f61736369696e656d612e6f72672f612f5346593848304b344470705676716d514356754f7554686f7a2e737667" alt="asciicast" data-canonical-src="https://asciinema.org/a/SFY8H0K4DppVvqmQCVuOuThoz.svg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Step6. create a dataset</h3><a id="user-content-step6-create-a-dataset" class="anchor" aria-label="Permalink: Step6. create a dataset" href="#step6-create-a-dataset"><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 create a dataset, the dataset consists of data, a template, and the corresponding prompter. we can use <code>dataset showcase</code> command to check the if the prompts are generated correctly.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="(mLoRA) dataset create ? name: alpaca_dataset ? train data file: alpaca ? prompt template file: simple_prompt ? prompter: instruction ? data preprocessing: default (mLoRA) dataset showcase ? dataset name: alpaca_dataset"><pre>(mLoRA) dataset create <span class="pl-k">?</span> name: alpaca_dataset <span class="pl-k">?</span> train data file: alpaca <span class="pl-k">?</span> prompt template file: simple_prompt <span class="pl-k">?</span> prompter: instruction <span class="pl-k">?</span> data preprocessing: default (mLoRA) dataset showcase <span class="pl-k">?</span> dataset name: alpaca_dataset</pre></div> <p dir="auto"><a href="https://asciinema.org/a/mxpwo6gWihjEEsJ0dfcXG98cM" rel="nofollow"><img src="https://camo.githubusercontent.com/eb8c685349e406543349909e3bdaec2f277f14dc03d7333d77d06bbff9d4dcaa/68747470733a2f2f61736369696e656d612e6f72672f612f6d7870776f36675769686a4545734a3064666358473938634d2e737667" alt="asciicast" data-canonical-src="https://asciinema.org/a/mxpwo6gWihjEEsJ0dfcXG98cM.svg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Step7. create a adapter</h3><a id="user-content-step7-create-a-adapter" class="anchor" aria-label="Permalink: Step7. create a adapter" href="#step7-create-a-adapter"><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">now we can use <code>adapter create</code> command to create a adapter for train.</p> <p dir="auto"><a href="https://asciinema.org/a/Wf4PHfoGC0PCcciGHOXCkX0xj" rel="nofollow"><img src="https://camo.githubusercontent.com/64a818667ef93076edf24c52360ab3f186c397a51e866ee1a26e98be4d799419/68747470733a2f2f61736369696e656d612e6f72672f612f5766345048666f474330504363636947484f58436b5830786a2e737667" alt="asciicast" data-canonical-src="https://asciinema.org/a/Wf4PHfoGC0PCcciGHOXCkX0xj.svg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Step8. !!!! submit task to train !!!!</h3><a id="user-content-step8--submit-task-to-train-" class="anchor" aria-label="Permalink: Step8. !!!! submit task to train !!!!" href="#step8--submit-task-to-train-"><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">Finally, we can submit the task to train our adapter using the defined dataset. NOTE: you can continuously submit or terminal training tasks. use the <code>adapter ls</code> or <code>task ls</code> to check the tasks' status</p> <p dir="auto"><a href="https://asciinema.org/a/vr8f1XtA0CBULGIP81w2bakkE" rel="nofollow"><img src="https://camo.githubusercontent.com/297f6c1a9017e0ca72c955bfae77e042331d1a6d3c1c1afc72336e88b3d92797/68747470733a2f2f61736369696e656d612e6f72672f612f7672386631587441304342554c4749503831773262616b6b452e737667" alt="asciicast" data-canonical-src="https://asciinema.org/a/vr8f1XtA0CBULGIP81w2bakkE.svg" style="max-width: 100%;"></a></p> </details> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Why you should use mLoRA</h2><a id="user-content-why-you-should-use-mlora" class="anchor" aria-label="Permalink: Why you should use mLoRA" href="#why-you-should-use-mlora"><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">Using mLoRA can save significant computational and memory resources when training multiple adapters simultaneously.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">High performance on consumer hardware</h3><a id="user-content-high-performance-on-consumer-hardware" class="anchor" aria-label="Permalink: High performance on consumer hardware" href="#high-performance-on-consumer-hardware"><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 fine-tuned multiple LoRA adapters using four A6000 graphics cards with fp32 precision and without using checkpointing and any quantization techniques:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Model</th> <th>mLoRA (tokens/s)</th> <th>PEFT-LoRA with FSDP (tokens/s)</th> <th>PEFT-LoRA with TP (tokens/s)</th> </tr> </thead> <tbody> <tr> <td><a href="https://huggingface.co/meta-llama/llama-2-7b" rel="nofollow">llama-2-7b (32fp)</a></td> <td>2364</td> <td>1750</td> <td>1500</td> </tr> <tr> <td><a href="https://huggingface.co/meta-llama/llama-2-13b" rel="nofollow">llama-2-13b (32fp)</a></td> <td>1280</td> <td>OOM</td> <td>875</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Supported model</h3><a id="user-content-supported-model" class="anchor" aria-label="Permalink: Supported model" href="#supported-model"><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> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th>Model</th> </tr> </thead> <tbody> <tr> <td>✓</td> <td><a href="https://llama.meta.com/" rel="nofollow">LLaMA</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Supported LoRA variants</h3><a id="user-content-supported-lora-variants" class="anchor" aria-label="Permalink: Supported LoRA variants" href="#supported-lora-variants"><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> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th>Variant</th> </tr> </thead> <tbody> <tr> <td>✓</td> <td><a href="https://arxiv.org/abs/2305.14314" rel="nofollow">QLoRA</a>,NIPS,2023</td> </tr> <tr> <td>✓</td> <td><a href="https://arxiv.org/abs/2402.12354" rel="nofollow">LoRA+</a>,ICML,2024</td> </tr> <tr> <td>✓</td> <td><a href="https://arxiv.org/abs/2310.11454" rel="nofollow">VeRA</a>,ICLR,2024</td> </tr> <tr> <td>✓</td> <td><a href="https://arxiv.org/abs/2402.09353" rel="nofollow">DoRA</a>,ICML,2024</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Supported preference alignment algorithms</h3><a id="user-content-supported-preference-alignment-algorithms" class="anchor" aria-label="Permalink: Supported preference alignment algorithms" href="#supported-preference-alignment-algorithms"><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> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th>Variant</th> </tr> </thead> <tbody> <tr> <td>✓</td> <td><a href="https://arxiv.org/abs/2305.18290" rel="nofollow">DPO</a>,NeurIPS,2024</td> </tr> <tr> <td>✓</td> <td><a href="https://arxiv.org/abs/2401.08417" rel="nofollow">CPO</a>,ICML,2024</td> </tr> <tr> <td>✓</td> <td><a href="https://arxiv.org/abs/2402.11138" rel="nofollow">CIT</a>,arXiv,2024</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Document</h2><a id="user-content-document" class="anchor" aria-label="Permalink: Document" href="#document"><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>Help Document[TODO]</li> <li><a href="/TUDB-Labs/mLoRA/blob/main/docs/design.md">Design Document</a></li> <li><a href="/TUDB-Labs/mLoRA/blob/main/docs/develop_new_adapter.md">How to develop a new adapter</a></li> <li><a href="/TUDB-Labs/mLoRA/blob/main/benchmarks/README.md">How to reproduce the paper</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributing</h2><a id="user-content-contributing" class="anchor" aria-label="Permalink: Contributing" href="#contributing"><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 welcome contributions to improve this repository! Please review the contribution guidelines before submitting pull requests or issues.</p> <p dir="auto">Fork the repository. Create a new branch for your feature or fix. Submit a pull request with a detailed explanation of your changes.</p> <p dir="auto">You can use the pre-commit to check your code.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Install requirements pip install .[ci_test] ln -s ../../.github/workflows/pre-commit .git/hooks/pre-commit"><pre><span class="pl-c"><span class="pl-c">#</span> Install requirements</span> pip install .[ci_test] ln -s ../../.github/workflows/pre-commit .git/hooks/pre-commit</pre></div> <p dir="auto">Or just call the script to check your code</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=".github/workflows/pre-commit"><pre>.github/workflows/pre-commit</pre></div> <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">Please cite the repo if you use the code in this repo.</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@misc{ye2024mlorafinetuningloraadapters, title={mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs}, author={Zhengmao Ye and Dengchun Li and Zetao Hu and Tingfeng Lan and Jian Sha and Sicong Zhang and Lei Duan and Jie Zuo and Hui Lu and Yuanchun Zhou and Mingjie Tang}, year={2024}, eprint={2312.02515}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2312.02515}, }"><pre><span class="pl-k">@misc</span>{<span class="pl-en">ye2024mlorafinetuningloraadapters</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Zhengmao Ye and Dengchun Li and Zetao Hu and Tingfeng Lan and Jian Sha and Sicong Zhang and Lei Duan and Jie Zuo and Hui Lu and Yuanchun Zhou and Mingjie Tang<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>2312.02515<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.LG<span class="pl-pds">}</span></span>, <span class="pl-s">url</span>=<span class="pl-s"><span class="pl-pds">{</span>https://arxiv.org/abs/2312.02515<span class="pl-pds">}</span></span>, }</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Copyright</h2><a id="user-content-copyright" class="anchor" aria-label="Permalink: Copyright" href="#copyright"><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">Copyright © 2024 All Rights Reserved.</p> <p dir="auto">This project is licensed under the <a href="https://www.apache.org/licenses/LICENSE-2.0" rel="nofollow">Apache 2.0 License</a>.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."><pre class="notranslate"><code>Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. </code></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="5qSf7S71S9mL9O1r9xFzp+CIM4i7JRfLQpRz0qnoS4Bhx1ENJ/MbSMpMKCkRbAUDVKqRx5ER1R3gABtH0/qtrQ==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> An Efficient "Factory" to Build Multiple LoRA Adapters </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/gpu" title="Topic: gpu" data-view-component="true" class="topic-tag topic-tag-link"> gpu </a> <a href="/topics/llama" title="Topic: llama" 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