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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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flex-lg-row flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <ul class="list-style-none f5" > <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_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 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":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </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":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </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":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </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":"code_review","context":"product","tag":"link","label":"code_review_link_product_navbar"}" href="https://github.com/features/code-review"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-review color-fg-subtle mr-3"> <path d="M10.3 6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 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data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="product-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"all_features","context":"product","tag":"link","label":"all_features_link_product_navbar"}" href="https://github.com/features"> All features </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":"documentation","context":"product","tag":"link","label":"documentation_link_product_navbar"}" href="https://docs.github.com"> Documentation <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 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 12.25v-8.5C2 2.784 2.784 2 3.75 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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" 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dir=\"auto\"\u003eColossal-AI\u003c/h1\u003e\u003ca id=\"user-content-colossal-ai\" class=\"anchor\" aria-label=\"Permalink: Colossal-AI\" href=\"#colossal-ai\"\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 id=\"user-content-top\" align=\"center\" dir=\"auto\"\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://www.colossalai.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/colossal-ai_logo_vertical.png\" alt=\"logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eColossal-AI: Making large AI models cheaper, faster, and more accessible\u003c/p\u003e\n \u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e \u003ca href=\"https://arxiv.org/abs/2110.14883\" rel=\"nofollow\"\u003e Paper \u003c/a\u003e |\n \u003ca href=\"https://www.colossalai.org/\" rel=\"nofollow\"\u003e Documentation \u003c/a\u003e |\n \u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples\"\u003e Examples \u003c/a\u003e |\n \u003ca href=\"https://github.com/hpcaitech/ColossalAI/discussions\"\u003e Forum \u003c/a\u003e |\n \u003ca 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data-canonical-src=\"https://img.shields.io/badge/%E5%BE%AE%E4%BF%A1-%E5%8A%A0%E5%85%A5-green?logo=wechat\u0026amp;amp\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e| \u003ca href=\"/hpcaitech/ColossalAI/blob/main/README.md\"\u003eEnglish\u003c/a\u003e | \u003ca href=\"/hpcaitech/ColossalAI/blob/main/docs/README-zh-Hans.md\"\u003e中文\u003c/a\u003e |\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGet Started with Colossal-AI Without Setup\u003c/h2\u003e\u003ca id=\"user-content-get-started-with-colossal-ai-without-setup\" class=\"anchor\" aria-label=\"Permalink: Get Started with Colossal-AI Without Setup\" href=\"#get-started-with-colossal-ai-without-setup\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAccess high-end, on-demand compute for your research instantly—no setup needed.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSign up now and get $10 in credits!\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eLimited Academic Bonuses:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTop up $1,000 and receive 300 credits\u003c/li\u003e\n\u003cli\u003eTop up $500 and receive 100 credits\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca href=\"https://hpc-ai.com/?utm_source=github\u0026amp;utm_medium=social\u0026amp;utm_campaign=promotion-colossalai\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://github.com/hpcaitech/public_assets/raw/main/colossalai/img/2.gif\" width=\"850\" data-animated-image=\"\" style=\"max-width: 100%;\"\u003e\n \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[2025/02] \u003ca href=\"https://company.hpc-ai.com/blog/shocking-release-deepseek-671b-fine-tuning-guide-revealed-unlock-the-upgraded-deepseek-suite-with-one-click-ai-players-ecstatic\" rel=\"nofollow\"\u003eDeepSeek 671B Fine-Tuning Guide Revealed—Unlock the Upgraded DeepSeek Suite with One Click, AI Players Ecstatic!\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/12] \u003ca href=\"https://company.hpc-ai.com/blog/the-development-cost-of-video-generation-models-has-saved-by-50-open-source-solutions-are-now-available-with-h200-gpu-vouchers\" rel=\"nofollow\"\u003eThe development cost of video generation models has saved by 50%! Open-source solutions are now available with H200 GPU vouchers\u003c/a\u003e \u003ca href=\"https://github.com/hpcaitech/Open-Sora/blob/main/scripts/train.py\"\u003e[code]\u003c/a\u003e \u003ca href=\"https://colossalai.org/zh-Hans/docs/get_started/bonus/\" rel=\"nofollow\"\u003e[vouchers]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/10] \u003ca href=\"https://company.hpc-ai.com/blog/how-to-build-a-low-cost-sora-like-app-solutions-for-you\" rel=\"nofollow\"\u003eHow to build a low-cost Sora-like app? Solutions for you\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/09] \u003ca href=\"https://company.hpc-ai.com/blog/singapore-startup-hpc-ai-tech-secures-50-million-usd-in-series-a-funding-to-build-the-video-generation-ai-model-and-gpu-platform\" rel=\"nofollow\"\u003eSingapore Startup HPC-AI Tech Secures 50 Million USD in Series A Funding to Build the Video Generation AI Model and GPU Platform\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/09] \u003ca href=\"https://company.hpc-ai.com/blog/reducing-ai-large-model-training-costs-by-30-requires-just-a-single-line-of-code-from-fp8-mixed-precision-training-upgrades\" rel=\"nofollow\"\u003eReducing AI Large Model Training Costs by 30% Requires Just a Single Line of Code From FP8 Mixed Precision Training Upgrades\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/06] \u003ca href=\"https://hpc-ai.com/blog/open-sora-from-hpc-ai-tech-team-continues-open-source-generate-any-16-second-720p-hd-video-with-one-click-model-weights-ready-to-use\" rel=\"nofollow\"\u003eOpen-Sora Continues Open Source: Generate Any 16-Second 720p HD Video with One Click, Model Weights Ready to Use\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/05] \u003ca href=\"https://hpc-ai.com/blog/colossal-inference\" rel=\"nofollow\"\u003eLarge AI Models Inference Speed Doubled, Colossal-Inference Open Source Release\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/04] \u003ca href=\"https://hpc-ai.com/blog/open-soras-comprehensive-upgrade-unveiled-embracing-16-second-video-generation-and-720p-resolution-in-open-source\" rel=\"nofollow\"\u003eOpen-Sora Unveils Major Upgrade: Embracing Open Source with Single-Shot 16-Second Video Generation and 720p Resolution\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/04] \u003ca href=\"https://hpc-ai.com/blog/most-cost-effective-solutions-for-inference-fine-tuning-and-pretraining-tailored-to-llama3-series\" rel=\"nofollow\"\u003eMost cost-effective solutions for inference, fine-tuning and pretraining, tailored to LLaMA3 series\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\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\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\u003ca href=\"#Why-Colossal-AI\"\u003eWhy Colossal-AI\u003c/a\u003e \u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Features\"\u003eFeatures\u003c/a\u003e \u003c/li\u003e\n \u003cli\u003e\n \u003ca href=\"#Colossal-AI-in-the-Real-World\"\u003eColossal-AI for Real World Applications\u003c/a\u003e\n \u003cul dir=\"auto\"\u003e\n \u003cli\u003e\u003ca href=\"#Open-Sora\"\u003eOpen-Sora: Revealing Complete Model Parameters, Training Details, and Everything for Sora-like Video Generation Models\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Colossal-LLaMA-2\"\u003eColossal-LLaMA-2: One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific Llm Solution\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#ColossalChat\"\u003eColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#AIGC\"\u003eAIGC: Acceleration of Stable Diffusion\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Biomedicine\"\u003eBiomedicine: Acceleration of AlphaFold Protein Structure\u003c/a\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003ca href=\"#Parallel-Training-Demo\"\u003eParallel Training Demo\u003c/a\u003e\n \u003cul dir=\"auto\"\u003e\n \u003cli\u003e\u003ca href=\"#LLaMA3\"\u003eLLaMA 1/2/3 \u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#MoE\"\u003eMoE\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#GPT-3\"\u003eGPT-3\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#GPT-2\"\u003eGPT-2\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#BERT\"\u003eBERT\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#PaLM\"\u003ePaLM\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#OPT\"\u003eOPT\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#ViT\"\u003eViT\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Recommendation-System-Models\"\u003eRecommendation System Models\u003c/a\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003ca href=\"#Single-GPU-Training-Demo\"\u003eSingle GPU Training Demo\u003c/a\u003e\n \u003cul dir=\"auto\"\u003e\n \u003cli\u003e\u003ca href=\"#GPT-2-Single\"\u003eGPT-2\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#PaLM-Single\"\u003ePaLM\u003c/a\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003ca href=\"#Inference\"\u003eInference\u003c/a\u003e\n \u003cul dir=\"auto\"\u003e\n \u003cli\u003e\u003ca href=\"#Colossal-Inference\"\u003eColossal-Inference: Large AI Models Inference Speed Doubled\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Grok-1\"\u003eGrok-1: 314B model of PyTorch + HuggingFace Inference\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#SwiftInfer\"\u003eSwiftInfer:Breaks the Length Limit of LLM for Multi-Round Conversations with 46% Acceleration\u003c/a\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003ca href=\"#Installation\"\u003eInstallation\u003c/a\u003e\n \u003cul dir=\"auto\"\u003e\n \u003cli\u003e\u003ca href=\"#PyPI\"\u003ePyPI\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Install-From-Source\"\u003eInstall From Source\u003c/a\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Use-Docker\"\u003eUse Docker\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Community\"\u003eCommunity\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003ca href=\"#Cite-Us\"\u003eCite Us\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\"\u003eWhy Colossal-AI\u003c/h2\u003e\u003ca id=\"user-content-why-colossal-ai\" class=\"anchor\" aria-label=\"Permalink: Why Colossal-AI\" href=\"#why-colossal-ai\"\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 align=\"center\" dir=\"auto\"\u003e\n \u003ca href=\"https://youtu.be/KnXSfjqkKN0\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/JamesDemmel_Colossal-AI.png\" width=\"600\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n\u003cp dir=\"auto\"\u003eProf. James Demmel (UC Berkeley): Colossal-AI makes training AI models efficient, easy, and scalable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\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\"\u003eColossal-AI provides a collection of parallel components for you. We aim to support you to write your\ndistributed deep learning models just like how you write your model on your laptop. We provide user-friendly tools to kickstart\ndistributed training and inference in a few lines.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eParallelism strategies\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eData Parallelism\u003c/li\u003e\n\u003cli\u003ePipeline Parallelism\u003c/li\u003e\n\u003cli\u003e1D, \u003ca href=\"https://arxiv.org/abs/2104.05343\" rel=\"nofollow\"\u003e2D\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2105.14500\" rel=\"nofollow\"\u003e2.5D\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2105.14450\" rel=\"nofollow\"\u003e3D\u003c/a\u003e Tensor Parallelism\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2105.13120\" rel=\"nofollow\"\u003eSequence Parallelism\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1910.02054\" rel=\"nofollow\"\u003eZero Redundancy Optimizer (ZeRO)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2302.02599\" rel=\"nofollow\"\u003eAuto-Parallelism\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eHeterogeneous Memory Management\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2108.05818\" rel=\"nofollow\"\u003ePatrickStar\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eFriendly Usage\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eParallelism based on the configuration file\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eColossal-AI in the Real World\u003c/h2\u003e\u003ca id=\"user-content-colossal-ai-in-the-real-world\" class=\"anchor\" aria-label=\"Permalink: Colossal-AI in the Real World\" href=\"#colossal-ai-in-the-real-world\"\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 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOpen-Sora\u003c/h3\u003e\u003ca id=\"user-content-open-sora\" class=\"anchor\" aria-label=\"Permalink: Open-Sora\" href=\"#open-sora\"\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/hpcaitech/Open-Sora\"\u003eOpen-Sora\u003c/a\u003e：Revealing Complete Model Parameters, Training Details, and Everything for Sora-like Video Generation Models\n\u003ca href=\"https://github.com/hpcaitech/Open-Sora\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://hpc-ai.com/blog/open-sora-from-hpc-ai-tech-team-continues-open-source-generate-any-16-second-720p-hd-video-with-one-click-model-weights-ready-to-use\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\n\u003ca href=\"https://github.com/hpcaitech/Open-Sora?tab=readme-ov-file#model-weights\"\u003e[Model weights]\u003c/a\u003e\n\u003ca href=\"https://github.com/hpcaitech/Open-Sora?tab=readme-ov-file#-latest-demo\"\u003e[Demo]\u003c/a\u003e\n\u003ca href=\"https://cloud.luchentech.com/\" rel=\"nofollow\"\u003e[GPU Cloud Playground]\u003c/a\u003e\n\u003ca href=\"https://cloud.luchentech.com/doc/docs/image/open-sora/\" rel=\"nofollow\"\u003e[OpenSora Image]\u003c/a\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca href=\"https://youtu.be/ilMQpU71ddI?si=J4JSPzZ03ycYmlki\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/sora/opensora-v1.2.png\" width=\"700\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n\u003c/div\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eColossal-LLaMA-2\u003c/h3\u003e\u003ca id=\"user-content-colossal-llama-2\" class=\"anchor\" aria-label=\"Permalink: Colossal-LLaMA-2\" href=\"#colossal-llama-2\"\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://cloud.luchentech.com/\" rel=\"nofollow\"\u003e[GPU Cloud Playground]\u003c/a\u003e\n\u003ca href=\"https://cloud.luchentech.com/doc/docs/image/llama\" rel=\"nofollow\"\u003e[LLaMA3 Image]\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e7B: One half-day of training using a few hundred dollars yields similar results to mainstream large models, open-source and commercial-free domain-specific LLM solution.\n\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\n\u003ca href=\"https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-7b-base\" rel=\"nofollow\"\u003e[HuggingFace model weights]\u003c/a\u003e\n\u003ca href=\"https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-7b-base/summary\" rel=\"nofollow\"\u003e[Modelscope model weights]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e13B: Construct refined 13B private model with just $5000 USD.\n\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://hpc-ai.com/blog/colossal-llama-2-13b\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\n\u003ca href=\"https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-13b-base\" rel=\"nofollow\"\u003e[HuggingFace model weights]\u003c/a\u003e\n\u003ca href=\"https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-13b-base/summary\" rel=\"nofollow\"\u003e[Modelscope model weights]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eModel\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eTokens Consumed\u003c/th\u003e\n\u003cth align=\"center\"\u003eMMLU (5-shot)\u003c/th\u003e\n\u003cth align=\"center\"\u003eCMMLU (5-shot)\u003c/th\u003e\n\u003cth align=\"center\"\u003eAGIEval (5-shot)\u003c/th\u003e\n\u003cth align=\"center\"\u003eGAOKAO (0-shot)\u003c/th\u003e\n\u003cth align=\"center\"\u003eCEval (5-shot)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBaichuan-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1.2T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.32 (42.30)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e44.53 (44.02)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.72\u003c/td\u003e\n\u003ctd align=\"center\"\u003e36.74\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.80\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBaichuan-13B-Base\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1.4T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50.51 (51.60)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e55.73 (55.30)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e47.20\u003c/td\u003e\n\u003ctd align=\"center\"\u003e51.41\u003c/td\u003e\n\u003ctd align=\"center\"\u003e53.60\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBaichuan2-7B-Base\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2.6T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e46.97 (54.16)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e57.67 (57.07)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.76\u003c/td\u003e\n\u003ctd align=\"center\"\u003e52.60\u003c/td\u003e\n\u003ctd align=\"center\"\u003e54.00\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBaichuan2-13B-Base\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2.6T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e54.84 (59.17)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e62.62 (61.97)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e52.08\u003c/td\u003e\n\u003ctd align=\"center\"\u003e58.25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e58.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eChatGLM-6B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1.0T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e39.67 (40.63)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e41.17 (-)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.10\u003c/td\u003e\n\u003ctd align=\"center\"\u003e36.53\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.90\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eChatGLM2-6B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1.4T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e44.74 (45.46)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e49.40 (-)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e46.36\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.49\u003c/td\u003e\n\u003ctd align=\"center\"\u003e51.70\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eInternLM-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1.6T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e46.70 (51.00)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e52.00 (-)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e44.77\u003c/td\u003e\n\u003ctd align=\"center\"\u003e61.64\u003c/td\u003e\n\u003ctd align=\"center\"\u003e52.80\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eQwen-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2.2T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e54.29 (56.70)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e56.03 (58.80)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e52.47\u003c/td\u003e\n\u003ctd align=\"center\"\u003e56.42\u003c/td\u003e\n\u003ctd align=\"center\"\u003e59.60\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eLlama-2-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2.0T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e44.47 (45.30)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e32.97 (-)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e32.60\u003c/td\u003e\n\u003ctd align=\"center\"\u003e25.46\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eLinly-AI/Chinese-LLaMA-2-7B-hf\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLlama-2-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1.0T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e37.43\u003c/td\u003e\n\u003ctd align=\"center\"\u003e29.92\u003c/td\u003e\n\u003ctd align=\"center\"\u003e32.00\u003c/td\u003e\n\u003ctd align=\"center\"\u003e27.57\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ewenge-research/yayi-7b-llama2\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLlama-2-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.56\u003c/td\u003e\n\u003ctd align=\"center\"\u003e31.52\u003c/td\u003e\n\u003ctd align=\"center\"\u003e30.99\u003c/td\u003e\n\u003ctd align=\"center\"\u003e25.95\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eziqingyang/chinese-llama-2-7b\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLlama-2-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.86\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.69\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.52\u003c/td\u003e\n\u003ctd align=\"center\"\u003e25.18\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.2\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eTigerResearch/tigerbot-7b-base\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLlama-2-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.3T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e43.73\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.04\u003c/td\u003e\n\u003ctd align=\"center\"\u003e37.64\u003c/td\u003e\n\u003ctd align=\"center\"\u003e30.61\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eLinkSoul/Chinese-Llama-2-7b\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLlama-2-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003e48.41\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.31\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.45\u003c/td\u003e\n\u003ctd align=\"center\"\u003e27.72\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eFlagAlpha/Atom-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLlama-2-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.1T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e49.96\u003c/td\u003e\n\u003ctd align=\"center\"\u003e41.10\u003c/td\u003e\n\u003ctd align=\"center\"\u003e39.83\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.00\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eIDEA-CCNL/Ziya-LLaMA-13B-v1.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLlama-13B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.11T\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50.25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.99\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.04\u003c/td\u003e\n\u003ctd align=\"center\"\u003e30.54\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eColossal-LLaMA-2-7b-base\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLlama-2-7B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003e0.0085T\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e53.06\u003c/td\u003e\n\u003ctd align=\"center\"\u003e49.89\u003c/td\u003e\n\u003ctd align=\"center\"\u003e51.48\u003c/td\u003e\n\u003ctd align=\"center\"\u003e58.82\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50.2\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eColossal-LLaMA-2-13b-base\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLlama-2-13B\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003e0.025T\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e56.42\u003c/td\u003e\n\u003ctd align=\"center\"\u003e61.80\u003c/td\u003e\n\u003ctd align=\"center\"\u003e54.69\u003c/td\u003e\n\u003ctd align=\"center\"\u003e69.53\u003c/td\u003e\n\u003ctd align=\"center\"\u003e60.3\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\"\u003eColossalChat\u003c/h3\u003e\u003ca id=\"user-content-colossalchat\" class=\"anchor\" aria-label=\"Permalink: ColossalChat\" href=\"#colossalchat\"\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 align=\"center\" dir=\"auto\"\u003e\n \u003ca href=\"https://www.youtube.com/watch?v=HcTiHzApHm0\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20YouTube.png\" width=\"700\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat\"\u003eColossalChat\u003c/a\u003e: An open-source solution for cloning \u003ca href=\"https://openai.com/blog/chatgpt/\" rel=\"nofollow\"\u003eChatGPT\u003c/a\u003e with a complete RLHF pipeline.\n\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\n\u003ca href=\"https://www.youtube.com/watch?v=HcTiHzApHm0\" rel=\"nofollow\"\u003e[demo]\u003c/a\u003e\n\u003ca href=\"https://www.youtube.com/watch?v=-qFBZFmOJfg\" rel=\"nofollow\"\u003e[tutorial]\u003c/a\u003e\u003c/p\u003e\n\u003cp id=\"user-content-colossalchat-speed\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20Speed.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20Speed.jpg\" width=\"450/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUp to 10 times faster for RLHF PPO Stage3 Training\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp id=\"user-content-colossalchat_scaling\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT%20scaling.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT%20scaling.png\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUp to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp id=\"user-content-colossalchat-1gpu\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT-1GPU.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT-1GPU.jpg\" width=\"450/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUp to 10.3x growth in model capacity on one GPU\u003c/li\u003e\n\u003cli\u003eA mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp id=\"user-content-colossalchat-lora\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/LoRA%20data.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/LoRA%20data.jpg\" width=\"600/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIncrease the capacity of the fine-tuning model by up to 3.7 times on a single GPU\u003c/li\u003e\n\u003cli\u003eKeep at a sufficiently high running speed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAIGC\u003c/h3\u003e\u003ca id=\"user-content-aigc\" class=\"anchor\" aria-label=\"Permalink: AIGC\" href=\"#aigc\"\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\"\u003eAcceleration of AIGC (AI-Generated Content) models such as \u003ca href=\"https://github.com/CompVis/stable-diffusion\"\u003eStable Diffusion v1\u003c/a\u003e and \u003ca href=\"https://github.com/Stability-AI/stablediffusion\"\u003eStable Diffusion v2\u003c/a\u003e.\u003c/p\u003e\n\u003cp id=\"user-content-diffusion_train\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion\"\u003eTraining\u003c/a\u003e: Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp id=\"user-content-diffusion_demo\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth\"\u003eDreamBooth Fine-tuning\u003c/a\u003e: Personalize your model using just 3-5 images of the desired subject.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp id=\"user-content-inference-sd\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion\"\u003eInference\u003c/a\u003e: Reduce inference GPU memory consumption by 2.5x.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBiomedicine\u003c/h3\u003e\u003ca id=\"user-content-biomedicine\" class=\"anchor\" aria-label=\"Permalink: Biomedicine\" href=\"#biomedicine\"\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\"\u003eAcceleration of \u003ca href=\"https://alphafold.ebi.ac.uk/\" rel=\"nofollow\"\u003eAlphaFold Protein Structure\u003c/a\u003e\u003c/p\u003e\n\u003cp id=\"user-content-fastfold\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/FastFold.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/FastFold.jpg\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcaitech/FastFold\"\u003eFastFold\u003c/a\u003e: Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp id=\"user-content-fastfold-intel\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/data%20preprocessing%20with%20Intel.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/data%20preprocessing%20with%20Intel.jpg\" width=\"600/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcaitech/FastFold\"\u003eFastFold with Intel\u003c/a\u003e: 3x inference acceleration and 39% cost reduce.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp id=\"user-content-xtrimomultimer\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTrimoMultimer_Table.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTrimoMultimer_Table.jpg\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/biomap-research/xTrimoMultimer\"\u003exTrimoMultimer\u003c/a\u003e: accelerating structure prediction of protein monomers and multimer by 11x.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eParallel Training Demo\u003c/h2\u003e\u003ca id=\"user-content-parallel-training-demo\" class=\"anchor\" aria-label=\"Permalink: Parallel Training Demo\" href=\"#parallel-training-demo\"\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 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLLaMA3\u003c/h3\u003e\u003ca id=\"user-content-llama3\" class=\"anchor\" aria-label=\"Permalink: LLaMA3\" href=\"#llama3\"\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 align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA3-70B-H100.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA3-70B-H100.png\" width=\"600/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e70 billion parameter LLaMA3 model training accelerated by 18%\n\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/llama\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://cloud.luchentech.com/\" rel=\"nofollow\"\u003e[GPU Cloud Playground]\u003c/a\u003e\n\u003ca href=\"https://cloud.luchentech.com/doc/docs/image/llama\" rel=\"nofollow\"\u003e[LLaMA3 Image]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLLaMA2\u003c/h3\u003e\u003ca id=\"user-content-llama2\" class=\"anchor\" aria-label=\"Permalink: LLaMA2\" href=\"#llama2\"\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 align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/llama2_pretraining.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/llama2_pretraining.png\" width=\"600/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e70 billion parameter LLaMA2 model training accelerated by 195%\n\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/llama\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://www.hpc-ai.tech/blog/70b-llama2-training\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLLaMA1\u003c/h3\u003e\u003ca id=\"user-content-llama1\" class=\"anchor\" aria-label=\"Permalink: LLaMA1\" href=\"#llama1\"\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 align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA_pretraining.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA_pretraining.png\" width=\"600/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e65-billion-parameter large model pretraining accelerated by 38%\n\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/llama\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://www.hpc-ai.tech/blog/large-model-pretraining\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMoE\u003c/h3\u003e\u003ca id=\"user-content-moe\" class=\"anchor\" aria-label=\"Permalink: MoE\" href=\"#moe\"\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 align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/MOE_training.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/MOE_training.png\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eEnhanced MoE parallelism, Open-source MoE model training can be 9 times more efficient\n\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/openmoe\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://www.hpc-ai.tech/blog/enhanced-moe-parallelism-open-source-moe-model-training-can-be-9-times-more-efficient\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGPT-3\u003c/h3\u003e\u003ca id=\"user-content-gpt-3\" class=\"anchor\" aria-label=\"Permalink: GPT-3\" href=\"#gpt-3\"\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 align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3-v5.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3-v5.png\" width=\"700/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSave 50% GPU resources and 10.7% acceleration\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGPT-2\u003c/h3\u003e\u003ca id=\"user-content-gpt-2\" class=\"anchor\" aria-label=\"Permalink: GPT-2\" href=\"#gpt-2\"\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 target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2.png\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e11x lower GPU memory consumption, and superlinear scaling efficiency with Tensor Parallelism\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/(updated)GPT-2.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/(updated)GPT-2.png\" width=\"800\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e24x larger model size on the same hardware\u003c/li\u003e\n\u003cli\u003eover 3x acceleration\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBERT\u003c/h3\u003e\u003ca id=\"user-content-bert\" class=\"anchor\" aria-label=\"Permalink: BERT\" href=\"#bert\"\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 target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BERT.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BERT.png\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e2x faster training, or 50% longer sequence length\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePaLM\u003c/h3\u003e\u003ca id=\"user-content-palm\" class=\"anchor\" aria-label=\"Permalink: PaLM\" href=\"#palm\"\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\u003ca href=\"https://github.com/hpcaitech/PaLM-colossalai\"\u003ePaLM-colossalai\u003c/a\u003e: Scalable implementation of Google's Pathways Language Model (\u003ca href=\"https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html\" rel=\"nofollow\"\u003ePaLM\u003c/a\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOPT\u003c/h3\u003e\u003ca id=\"user-content-opt\" class=\"anchor\" aria-label=\"Permalink: OPT\" href=\"#opt\"\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 target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_update.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_update.png\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/facebookresearch/metaseq\"\u003eOpen Pretrained Transformer (OPT)\u003c/a\u003e, a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because of public pre-trained model weights.\u003c/li\u003e\n\u003cli\u003e45% speedup fine-tuning OPT at low cost in lines. \u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/opt\"\u003e[Example]\u003c/a\u003e \u003ca href=\"https://colossalai.org/docs/advanced_tutorials/opt_service\" rel=\"nofollow\"\u003e[Online Serving]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003ePlease visit our \u003ca href=\"https://www.colossalai.org/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e and \u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples\"\u003eexamples\u003c/a\u003e for more details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eViT\u003c/h3\u003e\u003ca id=\"user-content-vit\" class=\"anchor\" aria-label=\"Permalink: ViT\" href=\"#vit\"\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 align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png\" width=\"450\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e14x larger batch size, and 5x faster training for Tensor Parallelism = 64\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRecommendation System Models\u003c/h3\u003e\u003ca id=\"user-content-recommendation-system-models\" class=\"anchor\" aria-label=\"Permalink: Recommendation System Models\" href=\"#recommendation-system-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcaitech/CachedEmbedding\"\u003eCached Embedding\u003c/a\u003e, utilize software cache to train larger embedding tables with a smaller GPU memory budget.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSingle GPU Training Demo\u003c/h2\u003e\u003ca id=\"user-content-single-gpu-training-demo\" class=\"anchor\" aria-label=\"Permalink: Single GPU Training Demo\" href=\"#single-gpu-training-demo\"\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 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGPT-2\u003c/h3\u003e\u003ca id=\"user-content-gpt-2-1\" class=\"anchor\" aria-label=\"Permalink: GPT-2\" href=\"#gpt-2-1\"\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 id=\"user-content-gpt-2-single\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-GPU1.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-GPU1.png\" width=\"450/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e20x larger model size on the same hardware\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp id=\"user-content-gpt-2-nvme\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-NVME.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-NVME.png\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e120x larger model size on the same hardware (RTX 3080)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePaLM\u003c/h3\u003e\u003ca id=\"user-content-palm-1\" class=\"anchor\" aria-label=\"Permalink: PaLM\" href=\"#palm-1\"\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 id=\"user-content-palm-single\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/PaLM-GPU1.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/PaLM-GPU1.png\" width=\"450/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e34x larger model size on the same hardware\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInference\u003c/h2\u003e\u003ca id=\"user-content-inference\" class=\"anchor\" aria-label=\"Permalink: Inference\" href=\"#inference\"\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 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eColossal-Inference\u003c/h3\u003e\u003ca id=\"user-content-colossal-inference\" class=\"anchor\" aria-label=\"Permalink: Colossal-Inference\" href=\"#colossal-inference\"\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 align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/colossal-inference-v1-1.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/colossal-inference-v1-1.png\" width=\"1000/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/colossal-inference-v1-2.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/colossal-inference-v1-2.png\" width=\"1000/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLarge AI models inference speed doubled, compared to the offline inference performance of vLLM in some cases.\n\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/inference\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://hpc-ai.com/blog/colossal-inference\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\n\u003ca href=\"https://cloud.luchentech.com/\" rel=\"nofollow\"\u003e[GPU Cloud Playground]\u003c/a\u003e\n\u003ca href=\"https://cloud.luchentech.com/doc/docs/image/llama\" rel=\"nofollow\"\u003e[LLaMA3 Image]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGrok-1\u003c/h3\u003e\u003ca id=\"user-content-grok-1\" class=\"anchor\" aria-label=\"Permalink: Grok-1\" href=\"#grok-1\"\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 id=\"user-content-grok-1\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/grok-1-inference.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/grok-1-inference.jpg\" width=\"600/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e314 Billion Parameter Grok-1 Inference Accelerated by 3.8x, an easy-to-use Python + PyTorch + HuggingFace version for Inference.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/grok-1\"\u003e[code]\u003c/a\u003e\n\u003ca href=\"https://hpc-ai.com/blog/314-billion-parameter-grok-1-inference-accelerated-by-3.8x-efficient-and-easy-to-use-pytorchhuggingface-version-is-here\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\n\u003ca href=\"https://huggingface.co/hpcai-tech/grok-1\" rel=\"nofollow\"\u003e[HuggingFace Grok-1 PyTorch model weights]\u003c/a\u003e\n\u003ca href=\"https://www.modelscope.cn/models/colossalai/grok-1-pytorch/summary\" rel=\"nofollow\"\u003e[ModelScope Grok-1 PyTorch model weights]\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSwiftInfer\u003c/h3\u003e\u003ca id=\"user-content-swiftinfer\" class=\"anchor\" aria-label=\"Permalink: SwiftInfer\" href=\"#swiftinfer\"\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 id=\"user-content-swiftinfer\" align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/SwiftInfer.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/SwiftInfer.jpg\" width=\"800/\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcaitech/SwiftInfer\"\u003eSwiftInfer\u003c/a\u003e: Inference performance improved by 46%, open source solution breaks the length limit of LLM for multi-round conversations\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\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\"\u003eRequirements:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePyTorch \u0026gt;= 2.2\u003c/li\u003e\n\u003cli\u003ePython \u0026gt;= 3.7\u003c/li\u003e\n\u003cli\u003eCUDA \u0026gt;= 11.0\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/cuda-gpus\" rel=\"nofollow\"\u003eNVIDIA GPU Compute Capability\u003c/a\u003e \u0026gt;= 7.0 (V100/RTX20 and higher)\u003c/li\u003e\n\u003cli\u003eLinux OS\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eIf you encounter any problem with installation, you may want to raise an \u003ca href=\"https://github.com/hpcaitech/ColossalAI/issues/new/choose\"\u003eissue\u003c/a\u003e in this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall from PyPI\u003c/h3\u003e\u003ca id=\"user-content-install-from-pypi\" class=\"anchor\" aria-label=\"Permalink: Install from PyPI\" href=\"#install-from-pypi\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can easily install Colossal-AI with the following command. \u003cstrong\u003eBy default, we do not build PyTorch extensions during installation.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install colossalai\"\u003e\u003cpre\u003epip install colossalai\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote: only Linux is supported for now.\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHowever, if you want to build the PyTorch extensions during installation, you can set \u003ccode\u003eBUILD_EXT=1\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"BUILD_EXT=1 pip install colossalai\"\u003e\u003cpre\u003eBUILD_EXT=1 pip install colossalai\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOtherwise, CUDA kernels will be built during runtime when you actually need them.\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe also keep releasing the nightly version to PyPI every week. This allows you to access the unreleased features and bug fixes in the main branch.\nInstallation can be made via\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install colossalai-nightly\"\u003e\u003cpre\u003epip install colossalai-nightly\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDownload From Source\u003c/h3\u003e\u003ca id=\"user-content-download-from-source\" class=\"anchor\" aria-label=\"Permalink: Download From Source\" href=\"#download-from-source\"\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\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eThe version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problems. :)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/hpcaitech/ColossalAI.git\ncd ColossalAI\n\n# install colossalai\npip install .\"\u003e\u003cpre\u003egit clone https://github.com/hpcaitech/ColossalAI.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ColossalAI\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install colossalai\u003c/span\u003e\npip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBy default, we do not compile CUDA/C++ kernels. ColossalAI will build them during runtime.\nIf you want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"BUILD_EXT=1 pip install .\"\u003e\u003cpre\u003eBUILD_EXT=1 pip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor Users with CUDA 10.2, you can still build ColossalAI from source. However, you need to manually download the cub library and copy it to the corresponding directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# clone the repository\ngit clone https://github.com/hpcaitech/ColossalAI.git\ncd ColossalAI\n\n# download the cub library\nwget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip\nunzip 1.8.0.zip\ncp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/\n\n# install\nBUILD_EXT=1 pip install .\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e clone the repository\u003c/span\u003e\ngit clone https://github.com/hpcaitech/ColossalAI.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ColossalAI\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e download the cub library\u003c/span\u003e\nwget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip\nunzip 1.8.0.zip\ncp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install\u003c/span\u003e\nBUILD_EXT=1 pip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUse Docker\u003c/h2\u003e\u003ca id=\"user-content-use-docker\" class=\"anchor\" aria-label=\"Permalink: Use Docker\" href=\"#use-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=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePull from DockerHub\u003c/h3\u003e\u003ca id=\"user-content-pull-from-dockerhub\" class=\"anchor\" aria-label=\"Permalink: Pull from DockerHub\" href=\"#pull-from-dockerhub\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can directly pull the docker image from our \u003ca href=\"https://hub.docker.com/r/hpcaitech/colossalai\" rel=\"nofollow\"\u003eDockerHub page\u003c/a\u003e. The image is automatically uploaded upon release.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuild On Your Own\u003c/h3\u003e\u003ca id=\"user-content-build-on-your-own\" class=\"anchor\" aria-label=\"Permalink: Build On Your Own\" href=\"#build-on-your-own\"\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\"\u003eRun the following command to build a docker image from Dockerfile provided.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eBuilding Colossal-AI from scratch requires GPU support, you need to use Nvidia Docker Runtime as the default when doing \u003ccode\u003edocker build\u003c/code\u003e. More details can be found \u003ca href=\"https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nWe recommend you install Colossal-AI from our \u003ca href=\"https://www.colossalai.org\" rel=\"nofollow\"\u003eproject page\u003c/a\u003e directly.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd ColossalAI\ndocker build -t colossalai ./docker\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ColossalAI\ndocker build -t colossalai ./docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eRun the following command to start the docker container in interactive mode.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker run -ti --gpus all --rm --ipc=host colossalai bash\"\u003e\u003cpre\u003edocker run -ti --gpus all --rm --ipc=host colossalai bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCommunity\u003c/h2\u003e\u003ca id=\"user-content-community\" class=\"anchor\" aria-label=\"Permalink: Community\" href=\"#community\"\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\"\u003eJoin the Colossal-AI community on \u003ca href=\"https://github.com/hpcaitech/ColossalAI/discussions\"\u003eForum\u003c/a\u003e,\n\u003ca href=\"https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w\" rel=\"nofollow\"\u003eSlack\u003c/a\u003e,\nand \u003ca href=\"https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png\" title=\"qrcode\" rel=\"nofollow\"\u003eWeChat(微信)\u003c/a\u003e to share your suggestions, feedback, and questions with our engineering team.\u003c/p\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\"\u003eReferring to the successful attempts of \u003ca href=\"https://bigscience.huggingface.co/\" rel=\"nofollow\"\u003eBLOOM\u003c/a\u003e and \u003ca href=\"https://en.wikipedia.org/wiki/Stable_Diffusion\" rel=\"nofollow\"\u003eStable Diffusion\u003c/a\u003e, any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models!\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou may contact us or participate in the following ways:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcaitech/ColossalAI/stargazers\"\u003eLeaving a Star ⭐\u003c/a\u003e to show your like and support. Thanks!\u003c/li\u003e\n\u003cli\u003ePosting an \u003ca href=\"https://github.com/hpcaitech/ColossalAI/issues/new/choose\"\u003eissue\u003c/a\u003e, or submitting a PR on GitHub follow the guideline in \u003ca href=\"https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSend your official proposal to email \u003ca href=\"mailto:contact@hpcaitech.com\"\u003econtact@hpcaitech.com\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eThanks so much to all of our amazing contributors!\u003c/p\u003e\n\u003ca href=\"https://github.com/hpcaitech/ColossalAI/graphs/contributors\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/a6ad33dd3a21ef5e6ceaf6cae9dcff84ea915d84857dd9c855bd05523cc1941e/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d6870636169746563682f436f6c6f7373616c4149\" width=\"800px\" data-canonical-src=\"https://contrib.rocks/image?repo=hpcaitech/ColossalAI\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCI/CD\u003c/h2\u003e\u003ca id=\"user-content-cicd\" class=\"anchor\" aria-label=\"Permalink: CI/CD\" href=\"#cicd\"\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 leverage the power of \u003ca href=\"https://github.com/features/actions\"\u003eGitHub Actions\u003c/a\u003e to automate our development, release and deployment workflows. Please check out this \u003ca href=\"/hpcaitech/ColossalAI/blob/main/.github/workflows/README.md\"\u003edocumentation\u003c/a\u003e on how the automated workflows are operated.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCite Us\u003c/h2\u003e\u003ca id=\"user-content-cite-us\" class=\"anchor\" aria-label=\"Permalink: Cite Us\" href=\"#cite-us\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis project is inspired by some related projects (some by our team and some by other organizations). We would like to credit these amazing projects as listed in the \u003ca href=\"/hpcaitech/ColossalAI/blob/main/docs/REFERENCE.md\"\u003eReference List\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo cite this project, you can use the following BibTeX citation.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{10.1145/3605573.3605613,\nauthor = {Li, Shenggui and Liu, Hongxin and Bian, Zhengda and Fang, Jiarui and Huang, Haichen and Liu, Yuliang and Wang, Boxiang and You, Yang},\ntitle = {Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},\nyear = {2023},\nisbn = {9798400708435},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3605573.3605613},\ndoi = {10.1145/3605573.3605613},\nabstract = {The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expertise in both deep learning and parallel computing. The Colossal-AI system addressed the above challenge by introducing a unified interface to scale your sequential code of model training to distributed environments. It supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer. Compared to the baseline system, Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.},\nbooktitle = {Proceedings of the 52nd International Conference on Parallel Processing},\npages = {766–775},\nnumpages = {10},\nkeywords = {datasets, gaze detection, text tagging, neural networks},\nlocation = {Salt Lake City, UT, USA},\nseries = {ICPP '23}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{10.1145/3605573.3605613,\nauthor = {Li, Shenggui and Liu, Hongxin and Bian, Zhengda and Fang, Jiarui and Huang, Haichen and Liu, Yuliang and Wang, Boxiang and You, Yang},\ntitle = {Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},\nyear = {2023},\nisbn = {9798400708435},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3605573.3605613},\ndoi = {10.1145/3605573.3605613},\nabstract = {The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expertise in both deep learning and parallel computing. The Colossal-AI system addressed the above challenge by introducing a unified interface to scale your sequential code of model training to distributed environments. It supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer. Compared to the baseline system, Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.},\nbooktitle = {Proceedings of the 52nd International Conference on Parallel Processing},\npages = {766–775},\nnumpages = {10},\nkeywords = {datasets, gaze detection, text tagging, neural networks},\nlocation = {Salt Lake City, UT, USA},\nseries = {ICPP '23}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eColossal-AI has been accepted as official tutorial by top conferences \u003ca href=\"https://nips.cc/\" rel=\"nofollow\"\u003eNeurIPS\u003c/a\u003e, \u003ca href=\"https://sc22.supercomputing.org/\" rel=\"nofollow\"\u003eSC\u003c/a\u003e, \u003ca href=\"https://aaai.org/Conferences/AAAI-23/\" rel=\"nofollow\"\u003eAAAI\u003c/a\u003e,\n\u003ca href=\"https://ppopp23.sigplan.org/\" rel=\"nofollow\"\u003ePPoPP\u003c/a\u003e, \u003ca href=\"https://cvpr2023.thecvf.com/\" rel=\"nofollow\"\u003eCVPR\u003c/a\u003e, \u003ca href=\"https://www.isc-hpc.com/\" rel=\"nofollow\"\u003eISC\u003c/a\u003e, \u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcspring23-S51482/\" rel=\"nofollow\"\u003eNVIDIA GTC\u003c/a\u003e ,etc.\u003c/p\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Colossal-AI","anchor":"colossal-ai","htmlText":"Colossal-AI"},{"level":3,"text":" Paper |\n Documentation |\n Examples |\n Forum |\n GPU Cloud Playground |\n Blog ","anchor":"--paper------documentation------examples------forum-----gpu-cloud-playground------blog-","htmlText":" Paper |\n Documentation |\n Examples |\n Forum |\n GPU Cloud Playground |\n Blog "},{"level":2,"text":"Get Started with Colossal-AI Without Setup","anchor":"get-started-with-colossal-ai-without-setup","htmlText":"Get Started with Colossal-AI Without Setup"},{"level":2,"text":"Latest News","anchor":"latest-news","htmlText":"Latest News"},{"level":2,"text":"Table of Contents","anchor":"table-of-contents","htmlText":"Table of Contents"},{"level":2,"text":"Why Colossal-AI","anchor":"why-colossal-ai","htmlText":"Why Colossal-AI"},{"level":2,"text":"Features","anchor":"features","htmlText":"Features"},{"level":2,"text":"Colossal-AI in the Real World","anchor":"colossal-ai-in-the-real-world","htmlText":"Colossal-AI in the Real World"},{"level":3,"text":"Open-Sora","anchor":"open-sora","htmlText":"Open-Sora"},{"level":3,"text":"Colossal-LLaMA-2","anchor":"colossal-llama-2","htmlText":"Colossal-LLaMA-2"},{"level":3,"text":"ColossalChat","anchor":"colossalchat","htmlText":"ColossalChat"},{"level":3,"text":"AIGC","anchor":"aigc","htmlText":"AIGC"},{"level":3,"text":"Biomedicine","anchor":"biomedicine","htmlText":"Biomedicine"},{"level":2,"text":"Parallel Training Demo","anchor":"parallel-training-demo","htmlText":"Parallel Training 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Demo"},{"level":3,"text":"GPT-2","anchor":"gpt-2-1","htmlText":"GPT-2"},{"level":3,"text":"PaLM","anchor":"palm-1","htmlText":"PaLM"},{"level":2,"text":"Inference","anchor":"inference","htmlText":"Inference"},{"level":3,"text":"Colossal-Inference","anchor":"colossal-inference","htmlText":"Colossal-Inference"},{"level":3,"text":"Grok-1","anchor":"grok-1","htmlText":"Grok-1"},{"level":3,"text":"SwiftInfer","anchor":"swiftinfer","htmlText":"SwiftInfer"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"Install from PyPI","anchor":"install-from-pypi","htmlText":"Install from PyPI"},{"level":3,"text":"Download From Source","anchor":"download-from-source","htmlText":"Download From Source"},{"level":2,"text":"Use Docker","anchor":"use-docker","htmlText":"Use Docker"},{"level":3,"text":"Pull from DockerHub","anchor":"pull-from-dockerhub","htmlText":"Pull from DockerHub"},{"level":3,"text":"Build On Your Own","anchor":"build-on-your-own","htmlText":"Build On Your Own"},{"level":2,"text":"Community","anchor":"community","htmlText":"Community"},{"level":2,"text":"Contributing","anchor":"contributing","htmlText":"Contributing"},{"level":2,"text":"CI/CD","anchor":"cicd","htmlText":"CI/CD"},{"level":2,"text":"Cite Us","anchor":"cite-us","htmlText":"Cite Us"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fhpcaitech%2FColossalAI"}},{"displayName":"LICENSE","repoName":"ColossalAI","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fhpcaitech%2FColossalAI"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> <div data-target="react-partial.reactRoot"><style data-styled="true" 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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 id="user-content-top" align="center" dir="auto"> <p dir="auto"><a href="https://www.colossalai.org/" rel="nofollow"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/colossal-ai_logo_vertical.png" alt="logo" style="max-width: 100%;"></a></p> <p dir="auto">Colossal-AI: Making large AI models cheaper, faster, and more accessible</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto"> <a href="https://arxiv.org/abs/2110.14883" rel="nofollow"> Paper </a> | <a href="https://www.colossalai.org/" rel="nofollow"> Documentation </a> | <a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples"> Examples </a> | <a href="https://github.com/hpcaitech/ColossalAI/discussions"> Forum </a> | <a href="https://colossalai.org/zh-Hans/docs/get_started/bonus/" rel="nofollow">GPU Cloud Playground </a> | <a href="https://hpc-ai.com/blog" rel="nofollow"> Blog </a></h3><a id="user-content---paper------documentation------examples------forum-----gpu-cloud-playground------blog-" class="anchor" aria-label="Permalink: Paper | Documentation | Examples | Forum | GPU Cloud Playground | Blog " href="#--paper------documentation------examples------forum-----gpu-cloud-playground------blog-"><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 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rel="nofollow"><img src="https://camo.githubusercontent.com/26b9a59166866575a2ae839fe8deb323efc2726dada66510ce73c019383d9950/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2545352542452541452545342542462541312d2545352538412541302545352538352541352d677265656e3f6c6f676f3d77656368617426616d70" alt="WeChat badge" data-canonical-src="https://img.shields.io/badge/%E5%BE%AE%E4%BF%A1-%E5%8A%A0%E5%85%A5-green?logo=wechat&amp" style="max-width: 100%;"></a></p> <p dir="auto">| <a href="/hpcaitech/ColossalAI/blob/main/README.md">English</a> | <a href="/hpcaitech/ColossalAI/blob/main/docs/README-zh-Hans.md">中文</a> |</p> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Get Started with Colossal-AI Without Setup</h2><a id="user-content-get-started-with-colossal-ai-without-setup" class="anchor" aria-label="Permalink: Get Started with Colossal-AI Without Setup" href="#get-started-with-colossal-ai-without-setup"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Access high-end, on-demand compute for your research instantly—no setup needed.</p> <p dir="auto">Sign up now and get $10 in credits!</p> <p dir="auto">Limited Academic Bonuses:</p> <ul dir="auto"> <li>Top up $1,000 and receive 300 credits</li> <li>Top up $500 and receive 100 credits</li> </ul> <div align="center" dir="auto"> <a href="https://hpc-ai.com/?utm_source=github&utm_medium=social&utm_campaign=promotion-colossalai" rel="nofollow"> <img src="https://github.com/hpcaitech/public_assets/raw/main/colossalai/img/2.gif" width="850" data-animated-image="" 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>[2025/02] <a href="https://company.hpc-ai.com/blog/shocking-release-deepseek-671b-fine-tuning-guide-revealed-unlock-the-upgraded-deepseek-suite-with-one-click-ai-players-ecstatic" rel="nofollow">DeepSeek 671B Fine-Tuning Guide Revealed—Unlock the Upgraded DeepSeek Suite with One Click, AI Players Ecstatic!</a></li> <li>[2024/12] <a href="https://company.hpc-ai.com/blog/the-development-cost-of-video-generation-models-has-saved-by-50-open-source-solutions-are-now-available-with-h200-gpu-vouchers" rel="nofollow">The development cost of video generation models has saved by 50%! Open-source solutions are now available with H200 GPU vouchers</a> <a href="https://github.com/hpcaitech/Open-Sora/blob/main/scripts/train.py">[code]</a> <a href="https://colossalai.org/zh-Hans/docs/get_started/bonus/" rel="nofollow">[vouchers]</a></li> <li>[2024/10] <a href="https://company.hpc-ai.com/blog/how-to-build-a-low-cost-sora-like-app-solutions-for-you" rel="nofollow">How to build a low-cost Sora-like app? Solutions for you</a></li> <li>[2024/09] <a href="https://company.hpc-ai.com/blog/singapore-startup-hpc-ai-tech-secures-50-million-usd-in-series-a-funding-to-build-the-video-generation-ai-model-and-gpu-platform" rel="nofollow">Singapore Startup HPC-AI Tech Secures 50 Million USD in Series A Funding to Build the Video Generation AI Model and GPU Platform</a></li> <li>[2024/09] <a href="https://company.hpc-ai.com/blog/reducing-ai-large-model-training-costs-by-30-requires-just-a-single-line-of-code-from-fp8-mixed-precision-training-upgrades" rel="nofollow">Reducing AI Large Model Training Costs by 30% Requires Just a Single Line of Code From FP8 Mixed Precision Training Upgrades</a></li> <li>[2024/06] <a href="https://hpc-ai.com/blog/open-sora-from-hpc-ai-tech-team-continues-open-source-generate-any-16-second-720p-hd-video-with-one-click-model-weights-ready-to-use" rel="nofollow">Open-Sora Continues Open Source: Generate Any 16-Second 720p HD Video with One Click, Model Weights Ready to Use</a></li> <li>[2024/05] <a href="https://hpc-ai.com/blog/colossal-inference" rel="nofollow">Large AI Models Inference Speed Doubled, Colossal-Inference Open Source Release</a></li> <li>[2024/04] <a href="https://hpc-ai.com/blog/open-soras-comprehensive-upgrade-unveiled-embracing-16-second-video-generation-and-720p-resolution-in-open-source" rel="nofollow">Open-Sora Unveils Major Upgrade: Embracing Open Source with Single-Shot 16-Second Video Generation and 720p Resolution</a></li> <li>[2024/04] <a href="https://hpc-ai.com/blog/most-cost-effective-solutions-for-inference-fine-tuning-and-pretraining-tailored-to-llama3-series" rel="nofollow">Most cost-effective solutions for inference, fine-tuning and pretraining, tailored to LLaMA3 series</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Table of Contents</h2><a id="user-content-table-of-contents" class="anchor" aria-label="Permalink: Table of Contents" href="#table-of-contents"><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><a href="#Why-Colossal-AI">Why Colossal-AI</a> </li> <li><a href="#Features">Features</a> </li> <li> <a href="#Colossal-AI-in-the-Real-World">Colossal-AI for Real World Applications</a> <ul dir="auto"> <li><a href="#Open-Sora">Open-Sora: Revealing Complete Model Parameters, Training Details, and Everything for Sora-like Video Generation Models</a></li> <li><a href="#Colossal-LLaMA-2">Colossal-LLaMA-2: One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific Llm Solution</a></li> <li><a href="#ColossalChat">ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline</a></li> <li><a href="#AIGC">AIGC: Acceleration of Stable Diffusion</a></li> <li><a href="#Biomedicine">Biomedicine: Acceleration of AlphaFold Protein Structure</a></li> </ul> </li> <li> <a href="#Parallel-Training-Demo">Parallel Training Demo</a> <ul dir="auto"> <li><a href="#LLaMA3">LLaMA 1/2/3 </a></li> <li><a href="#MoE">MoE</a></li> <li><a href="#GPT-3">GPT-3</a></li> <li><a href="#GPT-2">GPT-2</a></li> <li><a href="#BERT">BERT</a></li> <li><a href="#PaLM">PaLM</a></li> <li><a href="#OPT">OPT</a></li> <li><a href="#ViT">ViT</a></li> <li><a href="#Recommendation-System-Models">Recommendation System Models</a></li> </ul> </li> <li> <a href="#Single-GPU-Training-Demo">Single GPU Training Demo</a> <ul dir="auto"> <li><a href="#GPT-2-Single">GPT-2</a></li> <li><a href="#PaLM-Single">PaLM</a></li> </ul> </li> <li> <a href="#Inference">Inference</a> <ul dir="auto"> <li><a href="#Colossal-Inference">Colossal-Inference: Large AI Models Inference Speed Doubled</a></li> <li><a href="#Grok-1">Grok-1: 314B model of PyTorch + HuggingFace Inference</a></li> <li><a href="#SwiftInfer">SwiftInfer:Breaks the Length Limit of LLM for Multi-Round Conversations with 46% Acceleration</a></li> </ul> </li> <li> <a href="#Installation">Installation</a> <ul dir="auto"> <li><a href="#PyPI">PyPI</a></li> <li><a href="#Install-From-Source">Install From Source</a></li> </ul> </li> <li><a href="#Use-Docker">Use Docker</a></li> <li><a href="#Community">Community</a></li> <li><a href="#Contributing">Contributing</a></li> <li><a href="#Cite-Us">Cite Us</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Why Colossal-AI</h2><a id="user-content-why-colossal-ai" class="anchor" aria-label="Permalink: Why Colossal-AI" href="#why-colossal-ai"><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 align="center" dir="auto"> <a href="https://youtu.be/KnXSfjqkKN0" rel="nofollow"> <img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/JamesDemmel_Colossal-AI.png" width="600" style="max-width: 100%;"> </a> <p dir="auto">Prof. James Demmel (UC Berkeley): Colossal-AI makes training AI models efficient, easy, and scalable.</p> </div> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Features</h2><a id="user-content-features" class="anchor" aria-label="Permalink: Features" href="#features"><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">Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. We provide user-friendly tools to kickstart distributed training and inference in a few lines.</p> <ul dir="auto"> <li> <p dir="auto">Parallelism strategies</p> <ul dir="auto"> <li>Data Parallelism</li> <li>Pipeline Parallelism</li> <li>1D, <a href="https://arxiv.org/abs/2104.05343" rel="nofollow">2D</a>, <a href="https://arxiv.org/abs/2105.14500" rel="nofollow">2.5D</a>, <a href="https://arxiv.org/abs/2105.14450" rel="nofollow">3D</a> Tensor Parallelism</li> <li><a href="https://arxiv.org/abs/2105.13120" rel="nofollow">Sequence Parallelism</a></li> <li><a href="https://arxiv.org/abs/1910.02054" rel="nofollow">Zero Redundancy Optimizer (ZeRO)</a></li> <li><a href="https://arxiv.org/abs/2302.02599" rel="nofollow">Auto-Parallelism</a></li> </ul> </li> <li> <p dir="auto">Heterogeneous Memory Management</p> <ul dir="auto"> <li><a href="https://arxiv.org/abs/2108.05818" rel="nofollow">PatrickStar</a></li> </ul> </li> <li> <p dir="auto">Friendly Usage</p> <ul dir="auto"> <li>Parallelism based on the configuration file</li> </ul> </li> </ul> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Colossal-AI in the Real World</h2><a id="user-content-colossal-ai-in-the-real-world" class="anchor" aria-label="Permalink: Colossal-AI in the Real World" href="#colossal-ai-in-the-real-world"><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 tabindex="-1" class="heading-element" dir="auto">Open-Sora</h3><a id="user-content-open-sora" class="anchor" aria-label="Permalink: Open-Sora" href="#open-sora"><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/hpcaitech/Open-Sora">Open-Sora</a>：Revealing Complete Model Parameters, Training Details, and Everything for Sora-like Video Generation Models <a href="https://github.com/hpcaitech/Open-Sora">[code]</a> <a href="https://hpc-ai.com/blog/open-sora-from-hpc-ai-tech-team-continues-open-source-generate-any-16-second-720p-hd-video-with-one-click-model-weights-ready-to-use" rel="nofollow">[blog]</a> <a href="https://github.com/hpcaitech/Open-Sora?tab=readme-ov-file#model-weights">[Model weights]</a> <a href="https://github.com/hpcaitech/Open-Sora?tab=readme-ov-file#-latest-demo">[Demo]</a> <a href="https://cloud.luchentech.com/" rel="nofollow">[GPU Cloud Playground]</a> <a href="https://cloud.luchentech.com/doc/docs/image/open-sora/" rel="nofollow">[OpenSora Image]</a></p> <div align="center" dir="auto"> <a href="https://youtu.be/ilMQpU71ddI?si=J4JSPzZ03ycYmlki" rel="nofollow"> <img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/sora/opensora-v1.2.png" width="700" style="max-width: 100%;"> </a> </div> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Colossal-LLaMA-2</h3><a id="user-content-colossal-llama-2" class="anchor" aria-label="Permalink: Colossal-LLaMA-2" href="#colossal-llama-2"><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://cloud.luchentech.com/" rel="nofollow">[GPU Cloud Playground]</a> <a href="https://cloud.luchentech.com/doc/docs/image/llama" rel="nofollow">[LLaMA3 Image]</a></p> <ul dir="auto"> <li> <p dir="auto">7B: One half-day of training using a few hundred dollars yields similar results to mainstream large models, open-source and commercial-free domain-specific LLM solution. <a href="https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2">[code]</a> <a href="https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution" rel="nofollow">[blog]</a> <a href="https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-7b-base" rel="nofollow">[HuggingFace model weights]</a> <a href="https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-7b-base/summary" rel="nofollow">[Modelscope model weights]</a></p> </li> <li> <p dir="auto">13B: Construct refined 13B private model with just $5000 USD. <a href="https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2">[code]</a> <a href="https://hpc-ai.com/blog/colossal-llama-2-13b" rel="nofollow">[blog]</a> <a href="https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-13b-base" rel="nofollow">[HuggingFace model weights]</a> <a href="https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-13b-base/summary" rel="nofollow">[Modelscope model weights]</a></p> </li> </ul> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Model</th> <th align="center">Backbone</th> <th align="center">Tokens Consumed</th> <th align="center">MMLU (5-shot)</th> <th align="center">CMMLU (5-shot)</th> <th align="center">AGIEval (5-shot)</th> <th align="center">GAOKAO (0-shot)</th> <th align="center">CEval (5-shot)</th> </tr> </thead> <tbody> <tr> <td align="center">Baichuan-7B</td> <td align="center">-</td> <td align="center">1.2T</td> <td align="center">42.32 (42.30)</td> <td align="center">44.53 (44.02)</td> <td align="center">38.72</td> <td align="center">36.74</td> <td align="center">42.80</td> </tr> <tr> <td align="center">Baichuan-13B-Base</td> <td align="center">-</td> <td align="center">1.4T</td> <td align="center">50.51 (51.60)</td> <td align="center">55.73 (55.30)</td> <td align="center">47.20</td> <td align="center">51.41</td> <td align="center">53.60</td> </tr> <tr> <td align="center">Baichuan2-7B-Base</td> <td align="center">-</td> <td align="center">2.6T</td> <td align="center">46.97 (54.16)</td> <td align="center">57.67 (57.07)</td> <td align="center">45.76</td> <td align="center">52.60</td> <td align="center">54.00</td> </tr> <tr> <td align="center">Baichuan2-13B-Base</td> <td align="center">-</td> <td align="center">2.6T</td> <td align="center">54.84 (59.17)</td> <td align="center">62.62 (61.97)</td> <td align="center">52.08</td> <td align="center">58.25</td> <td align="center">58.10</td> </tr> <tr> <td align="center">ChatGLM-6B</td> <td align="center">-</td> <td align="center">1.0T</td> <td align="center">39.67 (40.63)</td> <td align="center">41.17 (-)</td> <td align="center">40.10</td> <td align="center">36.53</td> <td align="center">38.90</td> </tr> <tr> <td align="center">ChatGLM2-6B</td> <td align="center">-</td> <td align="center">1.4T</td> <td align="center">44.74 (45.46)</td> <td align="center">49.40 (-)</td> <td align="center">46.36</td> <td align="center">45.49</td> <td align="center">51.70</td> </tr> <tr> <td align="center">InternLM-7B</td> <td align="center">-</td> <td align="center">1.6T</td> <td align="center">46.70 (51.00)</td> <td align="center">52.00 (-)</td> <td align="center">44.77</td> <td align="center">61.64</td> <td align="center">52.80</td> </tr> <tr> <td align="center">Qwen-7B</td> <td align="center">-</td> <td align="center">2.2T</td> <td align="center">54.29 (56.70)</td> <td align="center">56.03 (58.80)</td> <td align="center">52.47</td> <td align="center">56.42</td> <td align="center">59.60</td> </tr> <tr> <td align="center">Llama-2-7B</td> <td align="center">-</td> <td align="center">2.0T</td> <td align="center">44.47 (45.30)</td> <td align="center">32.97 (-)</td> <td align="center">32.60</td> <td align="center">25.46</td> <td align="center">-</td> </tr> <tr> <td align="center">Linly-AI/Chinese-LLaMA-2-7B-hf</td> <td align="center">Llama-2-7B</td> <td align="center">1.0T</td> <td align="center">37.43</td> <td align="center">29.92</td> <td align="center">32.00</td> <td align="center">27.57</td> <td align="center">-</td> </tr> <tr> <td align="center">wenge-research/yayi-7b-llama2</td> <td align="center">Llama-2-7B</td> <td align="center">-</td> <td align="center">38.56</td> <td align="center">31.52</td> <td align="center">30.99</td> <td align="center">25.95</td> <td align="center">-</td> </tr> <tr> <td align="center">ziqingyang/chinese-llama-2-7b</td> <td align="center">Llama-2-7B</td> <td align="center">-</td> <td align="center">33.86</td> <td align="center">34.69</td> <td align="center">34.52</td> <td align="center">25.18</td> <td align="center">34.2</td> </tr> <tr> <td align="center">TigerResearch/tigerbot-7b-base</td> <td align="center">Llama-2-7B</td> <td align="center">0.3T</td> <td align="center">43.73</td> <td align="center">42.04</td> <td align="center">37.64</td> <td align="center">30.61</td> <td align="center">-</td> </tr> <tr> <td align="center">LinkSoul/Chinese-Llama-2-7b</td> <td align="center">Llama-2-7B</td> <td align="center">-</td> <td align="center">48.41</td> <td align="center">38.31</td> <td align="center">38.45</td> <td align="center">27.72</td> <td align="center">-</td> </tr> <tr> <td align="center">FlagAlpha/Atom-7B</td> <td align="center">Llama-2-7B</td> <td align="center">0.1T</td> <td align="center">49.96</td> <td align="center">41.10</td> <td align="center">39.83</td> <td align="center">33.00</td> <td align="center">-</td> </tr> <tr> <td align="center">IDEA-CCNL/Ziya-LLaMA-13B-v1.1</td> <td align="center">Llama-13B</td> <td align="center">0.11T</td> <td align="center">50.25</td> <td align="center">40.99</td> <td align="center">40.04</td> <td align="center">30.54</td> <td align="center">-</td> </tr> <tr> <td align="center"><strong>Colossal-LLaMA-2-7b-base</strong></td> <td align="center">Llama-2-7B</td> <td align="center"><strong>0.0085T</strong></td> <td align="center">53.06</td> <td align="center">49.89</td> <td align="center">51.48</td> <td align="center">58.82</td> <td align="center">50.2</td> </tr> <tr> <td align="center"><strong>Colossal-LLaMA-2-13b-base</strong></td> <td align="center">Llama-2-13B</td> <td align="center"><strong>0.025T</strong></td> <td align="center">56.42</td> <td align="center">61.80</td> <td align="center">54.69</td> <td align="center">69.53</td> <td align="center">60.3</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ColossalChat</h3><a id="user-content-colossalchat" class="anchor" aria-label="Permalink: ColossalChat" href="#colossalchat"><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 align="center" dir="auto"> <a href="https://www.youtube.com/watch?v=HcTiHzApHm0" rel="nofollow"> <img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20YouTube.png" width="700" style="max-width: 100%;"> </a> </div> <p dir="auto"><a href="https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat">ColossalChat</a>: An open-source solution for cloning <a href="https://openai.com/blog/chatgpt/" rel="nofollow">ChatGPT</a> with a complete RLHF pipeline. <a href="https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat">[code]</a> <a href="https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b" rel="nofollow">[blog]</a> <a href="https://www.youtube.com/watch?v=HcTiHzApHm0" rel="nofollow">[demo]</a> <a href="https://www.youtube.com/watch?v=-qFBZFmOJfg" rel="nofollow">[tutorial]</a></p> <p id="user-content-colossalchat-speed" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20Speed.jpg"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20Speed.jpg" width="450/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>Up to 10 times faster for RLHF PPO Stage3 Training</li> </ul> <p id="user-content-colossalchat_scaling" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT%20scaling.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT%20scaling.png" width="800/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference</li> </ul> <p id="user-content-colossalchat-1gpu" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT-1GPU.jpg"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT-1GPU.jpg" width="450/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>Up to 10.3x growth in model capacity on one GPU</li> <li>A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU)</li> </ul> <p id="user-content-colossalchat-lora" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/LoRA%20data.jpg"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/LoRA%20data.jpg" width="600/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU</li> <li>Keep at a sufficiently high running speed</li> </ul> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">AIGC</h3><a id="user-content-aigc" class="anchor" aria-label="Permalink: AIGC" href="#aigc"><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">Acceleration of AIGC (AI-Generated Content) models such as <a href="https://github.com/CompVis/stable-diffusion">Stable Diffusion v1</a> and <a href="https://github.com/Stability-AI/stablediffusion">Stable Diffusion v2</a>.</p> <p id="user-content-diffusion_train" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png" width="800/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion">Training</a>: Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).</li> </ul> <p id="user-content-diffusion_demo" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png" width="800/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth">DreamBooth Fine-tuning</a>: Personalize your model using just 3-5 images of the desired subject.</li> </ul> <p id="user-content-inference-sd" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg" width="800/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion">Inference</a>: Reduce inference GPU memory consumption by 2.5x.</li> </ul> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Biomedicine</h3><a id="user-content-biomedicine" class="anchor" aria-label="Permalink: Biomedicine" href="#biomedicine"><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">Acceleration of <a href="https://alphafold.ebi.ac.uk/" rel="nofollow">AlphaFold Protein Structure</a></p> <p id="user-content-fastfold" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/FastFold.jpg"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/FastFold.jpg" width="800/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><a href="https://github.com/hpcaitech/FastFold">FastFold</a>: Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.</li> </ul> <p id="user-content-fastfold-intel" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/data%20preprocessing%20with%20Intel.jpg"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/data%20preprocessing%20with%20Intel.jpg" width="600/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><a href="https://github.com/hpcaitech/FastFold">FastFold with Intel</a>: 3x inference acceleration and 39% cost reduce.</li> </ul> <p id="user-content-xtrimomultimer" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTrimoMultimer_Table.jpg"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTrimoMultimer_Table.jpg" width="800/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><a href="https://github.com/biomap-research/xTrimoMultimer">xTrimoMultimer</a>: accelerating structure prediction of protein monomers and multimer by 11x.</li> </ul> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Parallel Training Demo</h2><a id="user-content-parallel-training-demo" class="anchor" aria-label="Permalink: Parallel Training Demo" href="#parallel-training-demo"><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 tabindex="-1" class="heading-element" dir="auto">LLaMA3</h3><a id="user-content-llama3" class="anchor" aria-label="Permalink: LLaMA3" href="#llama3"><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 align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA3-70B-H100.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA3-70B-H100.png" width="600/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>70 billion parameter LLaMA3 model training accelerated by 18% <a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/llama">[code]</a> <a href="https://cloud.luchentech.com/" rel="nofollow">[GPU Cloud Playground]</a> <a href="https://cloud.luchentech.com/doc/docs/image/llama" rel="nofollow">[LLaMA3 Image]</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">LLaMA2</h3><a id="user-content-llama2" class="anchor" aria-label="Permalink: LLaMA2" href="#llama2"><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 align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/llama2_pretraining.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/llama2_pretraining.png" width="600/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>70 billion parameter LLaMA2 model training accelerated by 195% <a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/llama">[code]</a> <a href="https://www.hpc-ai.tech/blog/70b-llama2-training" rel="nofollow">[blog]</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">LLaMA1</h3><a id="user-content-llama1" class="anchor" aria-label="Permalink: LLaMA1" href="#llama1"><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 align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA_pretraining.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA_pretraining.png" width="600/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>65-billion-parameter large model pretraining accelerated by 38% <a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/llama">[code]</a> <a href="https://www.hpc-ai.tech/blog/large-model-pretraining" rel="nofollow">[blog]</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">MoE</h3><a id="user-content-moe" class="anchor" aria-label="Permalink: MoE" href="#moe"><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 align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/MOE_training.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/MOE_training.png" width="800/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>Enhanced MoE parallelism, Open-source MoE model training can be 9 times more efficient <a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/openmoe">[code]</a> <a href="https://www.hpc-ai.tech/blog/enhanced-moe-parallelism-open-source-moe-model-training-can-be-9-times-more-efficient" rel="nofollow">[blog]</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">GPT-3</h3><a id="user-content-gpt-3" class="anchor" aria-label="Permalink: GPT-3" href="#gpt-3"><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 align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3-v5.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3-v5.png" width="700/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>Save 50% GPU resources and 10.7% acceleration</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">GPT-2</h3><a id="user-content-gpt-2" class="anchor" aria-label="Permalink: GPT-2" href="#gpt-2"><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 target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2.png" width="800/" style="max-width: 100%;"></a></p> <ul dir="auto"> <li>11x lower GPU memory consumption, and superlinear scaling efficiency with Tensor Parallelism</li> </ul> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/(updated)GPT-2.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/(updated)GPT-2.png" width="800" style="max-width: 100%;"></a></p> <ul dir="auto"> <li>24x larger model size on the same hardware</li> <li>over 3x acceleration</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">BERT</h3><a id="user-content-bert" class="anchor" aria-label="Permalink: BERT" href="#bert"><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 target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BERT.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BERT.png" width="800/" style="max-width: 100%;"></a></p> <ul dir="auto"> <li>2x faster training, or 50% longer sequence length</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">PaLM</h3><a id="user-content-palm" class="anchor" aria-label="Permalink: PaLM" href="#palm"><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><a href="https://github.com/hpcaitech/PaLM-colossalai">PaLM-colossalai</a>: Scalable implementation of Google's Pathways Language Model (<a href="https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html" rel="nofollow">PaLM</a>).</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">OPT</h3><a id="user-content-opt" class="anchor" aria-label="Permalink: OPT" href="#opt"><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 target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_update.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_update.png" width="800/" style="max-width: 100%;"></a></p> <ul dir="auto"> <li><a href="https://github.com/facebookresearch/metaseq">Open Pretrained Transformer (OPT)</a>, a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because of public pre-trained model weights.</li> <li>45% speedup fine-tuning OPT at low cost in lines. <a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/opt">[Example]</a> <a href="https://colossalai.org/docs/advanced_tutorials/opt_service" rel="nofollow">[Online Serving]</a></li> </ul> <p dir="auto">Please visit our <a href="https://www.colossalai.org/" rel="nofollow">documentation</a> and <a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples">examples</a> for more details.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ViT</h3><a id="user-content-vit" class="anchor" aria-label="Permalink: ViT" href="#vit"><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 align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png" width="450" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>14x larger batch size, and 5x faster training for Tensor Parallelism = 64</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Recommendation System Models</h3><a id="user-content-recommendation-system-models" class="anchor" aria-label="Permalink: Recommendation System Models" href="#recommendation-system-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/hpcaitech/CachedEmbedding">Cached Embedding</a>, utilize software cache to train larger embedding tables with a smaller GPU memory budget.</li> </ul> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Single GPU Training Demo</h2><a id="user-content-single-gpu-training-demo" class="anchor" aria-label="Permalink: Single GPU Training Demo" href="#single-gpu-training-demo"><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 tabindex="-1" class="heading-element" dir="auto">GPT-2</h3><a id="user-content-gpt-2-1" class="anchor" aria-label="Permalink: GPT-2" href="#gpt-2-1"><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 id="user-content-gpt-2-single" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-GPU1.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-GPU1.png" width="450/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>20x larger model size on the same hardware</li> </ul> <p id="user-content-gpt-2-nvme" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-NVME.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-NVME.png" width="800/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>120x larger model size on the same hardware (RTX 3080)</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">PaLM</h3><a id="user-content-palm-1" class="anchor" aria-label="Permalink: PaLM" href="#palm-1"><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 id="user-content-palm-single" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/PaLM-GPU1.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/PaLM-GPU1.png" width="450/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>34x larger model size on the same hardware</li> </ul> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Inference</h2><a id="user-content-inference" class="anchor" aria-label="Permalink: Inference" href="#inference"><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 tabindex="-1" class="heading-element" dir="auto">Colossal-Inference</h3><a id="user-content-colossal-inference" class="anchor" aria-label="Permalink: Colossal-Inference" href="#colossal-inference"><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 align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/colossal-inference-v1-1.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/colossal-inference-v1-1.png" width="1000/" style="max-width: 100%;"></a> </p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/colossal-inference-v1-2.png"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/colossal-inference-v1-2.png" width="1000/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>Large AI models inference speed doubled, compared to the offline inference performance of vLLM in some cases. <a href="https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/inference">[code]</a> <a href="https://hpc-ai.com/blog/colossal-inference" rel="nofollow">[blog]</a> <a href="https://cloud.luchentech.com/" rel="nofollow">[GPU Cloud Playground]</a> <a href="https://cloud.luchentech.com/doc/docs/image/llama" rel="nofollow">[LLaMA3 Image]</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Grok-1</h3><a id="user-content-grok-1" class="anchor" aria-label="Permalink: Grok-1" href="#grok-1"><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 id="user-content-grok-1" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/grok-1-inference.jpg"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/grok-1-inference.jpg" width="600/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li>314 Billion Parameter Grok-1 Inference Accelerated by 3.8x, an easy-to-use Python + PyTorch + HuggingFace version for Inference.</li> </ul> <p dir="auto"><a href="https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/grok-1">[code]</a> <a href="https://hpc-ai.com/blog/314-billion-parameter-grok-1-inference-accelerated-by-3.8x-efficient-and-easy-to-use-pytorchhuggingface-version-is-here" rel="nofollow">[blog]</a> <a href="https://huggingface.co/hpcai-tech/grok-1" rel="nofollow">[HuggingFace Grok-1 PyTorch model weights]</a> <a href="https://www.modelscope.cn/models/colossalai/grok-1-pytorch/summary" rel="nofollow">[ModelScope Grok-1 PyTorch model weights]</a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SwiftInfer</h3><a id="user-content-swiftinfer" class="anchor" aria-label="Permalink: SwiftInfer" href="#swiftinfer"><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 id="user-content-swiftinfer" align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/SwiftInfer.jpg"><img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/SwiftInfer.jpg" width="800/" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><a href="https://github.com/hpcaitech/SwiftInfer">SwiftInfer</a>: Inference performance improved by 46%, open source solution breaks the length limit of LLM for multi-round conversations</li> </ul> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><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">Requirements:</p> <ul dir="auto"> <li>PyTorch >= 2.2</li> <li>Python >= 3.7</li> <li>CUDA >= 11.0</li> <li><a href="https://developer.nvidia.com/cuda-gpus" rel="nofollow">NVIDIA GPU Compute Capability</a> >= 7.0 (V100/RTX20 and higher)</li> <li>Linux OS</li> </ul> <p dir="auto">If you encounter any problem with installation, you may want to raise an <a href="https://github.com/hpcaitech/ColossalAI/issues/new/choose">issue</a> in this repository.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Install from PyPI</h3><a id="user-content-install-from-pypi" class="anchor" aria-label="Permalink: Install from PyPI" href="#install-from-pypi"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can easily install Colossal-AI with the following command. <strong>By default, we do not build PyTorch extensions during installation.</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install colossalai"><pre>pip install colossalai</pre></div> <p dir="auto"><strong>Note: only Linux is supported for now.</strong></p> <p dir="auto">However, if you want to build the PyTorch extensions during installation, you can set <code>BUILD_EXT=1</code>.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="BUILD_EXT=1 pip install colossalai"><pre>BUILD_EXT=1 pip install colossalai</pre></div> <p dir="auto"><strong>Otherwise, CUDA kernels will be built during runtime when you actually need them.</strong></p> <p dir="auto">We also keep releasing the nightly version to PyPI every week. This allows you to access the unreleased features and bug fixes in the main branch. Installation can be made via</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install colossalai-nightly"><pre>pip install colossalai-nightly</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Download From Source</h3><a id="user-content-download-from-source" class="anchor" aria-label="Permalink: Download From Source" href="#download-from-source"><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> <blockquote> <p dir="auto">The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problems. :)</p> </blockquote> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/hpcaitech/ColossalAI.git cd ColossalAI # install colossalai pip install ."><pre>git clone https://github.com/hpcaitech/ColossalAI.git <span class="pl-c1">cd</span> ColossalAI <span class="pl-c"><span class="pl-c">#</span> install colossalai</span> pip install <span class="pl-c1">.</span></pre></div> <p dir="auto">By default, we do not compile CUDA/C++ kernels. ColossalAI will build them during runtime. If you want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="BUILD_EXT=1 pip install ."><pre>BUILD_EXT=1 pip install <span class="pl-c1">.</span></pre></div> <p dir="auto">For Users with CUDA 10.2, you can still build ColossalAI from source. However, you need to manually download the cub library and copy it to the corresponding directory.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# clone the repository git clone https://github.com/hpcaitech/ColossalAI.git cd ColossalAI # download the cub library wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip unzip 1.8.0.zip cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/ # install BUILD_EXT=1 pip install ."><pre><span class="pl-c"><span class="pl-c">#</span> clone the repository</span> git clone https://github.com/hpcaitech/ColossalAI.git <span class="pl-c1">cd</span> ColossalAI <span class="pl-c"><span class="pl-c">#</span> download the cub library</span> wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip unzip 1.8.0.zip cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/ <span class="pl-c"><span class="pl-c">#</span> install</span> BUILD_EXT=1 pip install <span class="pl-c1">.</span></pre></div> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Use Docker</h2><a id="user-content-use-docker" class="anchor" aria-label="Permalink: Use Docker" href="#use-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="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Pull from DockerHub</h3><a id="user-content-pull-from-dockerhub" class="anchor" aria-label="Permalink: Pull from DockerHub" href="#pull-from-dockerhub"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can directly pull the docker image from our <a href="https://hub.docker.com/r/hpcaitech/colossalai" rel="nofollow">DockerHub page</a>. The image is automatically uploaded upon release.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Build On Your Own</h3><a id="user-content-build-on-your-own" class="anchor" aria-label="Permalink: Build On Your Own" href="#build-on-your-own"><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">Run the following command to build a docker image from Dockerfile provided.</p> <blockquote> <p dir="auto">Building Colossal-AI from scratch requires GPU support, you need to use Nvidia Docker Runtime as the default when doing <code>docker build</code>. More details can be found <a href="https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime" rel="nofollow">here</a>. We recommend you install Colossal-AI from our <a href="https://www.colossalai.org" rel="nofollow">project page</a> directly.</p> </blockquote> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cd ColossalAI docker build -t colossalai ./docker"><pre><span class="pl-c1">cd</span> ColossalAI docker build -t colossalai ./docker</pre></div> <p dir="auto">Run the following command to start the docker container in interactive mode.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker run -ti --gpus all --rm --ipc=host colossalai bash"><pre>docker run -ti --gpus all --rm --ipc=host colossalai bash</pre></div> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Community</h2><a id="user-content-community" class="anchor" aria-label="Permalink: Community" href="#community"><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">Join the Colossal-AI community on <a href="https://github.com/hpcaitech/ColossalAI/discussions">Forum</a>, <a href="https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w" rel="nofollow">Slack</a>, and <a href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png" title="qrcode" rel="nofollow">WeChat(微信)</a> to share your suggestions, feedback, and questions with our engineering team.</p> <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">Referring to the successful attempts of <a href="https://bigscience.huggingface.co/" rel="nofollow">BLOOM</a> and <a href="https://en.wikipedia.org/wiki/Stable_Diffusion" rel="nofollow">Stable Diffusion</a>, any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models!</p> <p dir="auto">You may contact us or participate in the following ways:</p> <ol dir="auto"> <li><a href="https://github.com/hpcaitech/ColossalAI/stargazers">Leaving a Star ⭐</a> to show your like and support. Thanks!</li> <li>Posting an <a href="https://github.com/hpcaitech/ColossalAI/issues/new/choose">issue</a>, or submitting a PR on GitHub follow the guideline in <a href="https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md">Contributing</a></li> <li>Send your official proposal to email <a href="mailto:contact@hpcaitech.com">contact@hpcaitech.com</a></li> </ol> <p dir="auto">Thanks so much to all of our amazing contributors!</p> <a href="https://github.com/hpcaitech/ColossalAI/graphs/contributors"> <img src="https://camo.githubusercontent.com/a6ad33dd3a21ef5e6ceaf6cae9dcff84ea915d84857dd9c855bd05523cc1941e/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d6870636169746563682f436f6c6f7373616c4149" width="800px" data-canonical-src="https://contrib.rocks/image?repo=hpcaitech/ColossalAI" style="max-width: 100%;"> </a> <p align="right" dir="auto">(<a href="#top">back to top</a>)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">CI/CD</h2><a id="user-content-cicd" class="anchor" aria-label="Permalink: CI/CD" href="#cicd"><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 leverage the power of <a href="https://github.com/features/actions">GitHub Actions</a> to automate our development, release and deployment workflows. Please check out this <a href="/hpcaitech/ColossalAI/blob/main/.github/workflows/README.md">documentation</a> on how the automated workflows are operated.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Cite Us</h2><a id="user-content-cite-us" class="anchor" aria-label="Permalink: Cite Us" href="#cite-us"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This project is inspired by some related projects (some by our team and some by other organizations). We would like to credit these amazing projects as listed in the <a href="/hpcaitech/ColossalAI/blob/main/docs/REFERENCE.md">Reference List</a>.</p> <p dir="auto">To cite this project, you can use the following BibTeX citation.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{10.1145/3605573.3605613, author = {Li, Shenggui and Liu, Hongxin and Bian, Zhengda and Fang, Jiarui and Huang, Haichen and Liu, Yuliang and Wang, Boxiang and You, Yang}, title = {Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training}, year = {2023}, isbn = {9798400708435}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3605573.3605613}, doi = {10.1145/3605573.3605613}, abstract = {The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expertise in both deep learning and parallel computing. The Colossal-AI system addressed the above challenge by introducing a unified interface to scale your sequential code of model training to distributed environments. It supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer. Compared to the baseline system, Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.}, booktitle = {Proceedings of the 52nd International Conference on Parallel Processing}, pages = {766–775}, numpages = {10}, keywords = {datasets, gaze detection, text tagging, neural networks}, location = {Salt Lake City, UT, USA}, series = {ICPP '23} }"><pre class="notranslate"><code>@inproceedings{10.1145/3605573.3605613, author = {Li, Shenggui and Liu, Hongxin and Bian, Zhengda and Fang, Jiarui and Huang, Haichen and Liu, Yuliang and Wang, Boxiang and You, Yang}, title = {Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training}, year = {2023}, isbn = {9798400708435}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3605573.3605613}, doi = {10.1145/3605573.3605613}, abstract = {The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expertise in both deep learning and parallel computing. The Colossal-AI system addressed the above challenge by introducing a unified interface to scale your sequential code of model training to distributed environments. It supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer. Compared to the baseline system, Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.}, booktitle = {Proceedings of the 52nd International Conference on Parallel Processing}, pages = {766–775}, numpages = {10}, keywords = {datasets, gaze detection, text tagging, neural networks}, location = {Salt Lake City, UT, USA}, series = {ICPP '23} } </code></pre></div> <p dir="auto">Colossal-AI has been accepted as official tutorial by top conferences <a href="https://nips.cc/" rel="nofollow">NeurIPS</a>, <a href="https://sc22.supercomputing.org/" rel="nofollow">SC</a>, <a href="https://aaai.org/Conferences/AAAI-23/" rel="nofollow">AAAI</a>, <a href="https://ppopp23.sigplan.org/" rel="nofollow">PPoPP</a>, <a href="https://cvpr2023.thecvf.com/" rel="nofollow">CVPR</a>, <a href="https://www.isc-hpc.com/" rel="nofollow">ISC</a>, <a href="https://www.nvidia.com/en-us/on-demand/session/gtcspring23-S51482/" rel="nofollow">NVIDIA GTC</a> ,etc.</p> <p align="right" 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