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d="M12 1C5.9225 1 1 5.9225 1 12C1 16.8675 4.14875 20.9787 8.52125 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 12 1Z"></path> </svg> </a> <div class="flex-1 flex-order-2 text-right"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2FInternLM%2Flmdeploy" class="HeaderMenu-link HeaderMenu-button d-inline-flex d-lg-none flex-order-1 f5 no-underline border color-border-default rounded-2 px-2 py-1 color-fg-inherit js-prevent-focus-on-mobile-nav" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/InternLM/lmdeploy","user_id":null}}" data-hydro-click-hmac="199c19800057faefeb977d01266ca4fca0968dc0621e26804b0092b7d547186a" data-analytics-event="{"category":"Marketing nav","action":"click to Sign in","label":"ref_page:Marketing;ref_cta:Sign in;ref_loc:Header"}" > Sign in </a> </div> </div> <div class="HeaderMenu js-header-menu height-fit position-lg-relative d-lg-flex flex-column flex-auto top-0"> <div class="HeaderMenu-wrapper d-flex flex-column flex-self-start 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 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class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Security</div> Find and fix vulnerabilities </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":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" 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 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width="24" 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" 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0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 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rel=\"nofollow\"\u003e📘Documentation\u003c/a\u003e |\n\u003ca href=\"https://lmdeploy.readthedocs.io/en/latest/get_started/get_started.html\" rel=\"nofollow\"\u003e🛠️Quick Start\u003c/a\u003e |\n\u003ca href=\"https://github.com/InternLM/lmdeploy/issues/new/choose\"\u003e🤔Reporting Issues\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eEnglish | \u003ca href=\"/InternLM/lmdeploy/blob/main/README_zh-CN.md\"\u003e简体中文\u003c/a\u003e | \u003ca href=\"/InternLM/lmdeploy/blob/main/README_ja.md\"\u003e日本語\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e👋 join us on \u003ca href=\"https://cdn.vansin.top/internlm/lmdeploy.jpg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/254e29b62a71970426674b59d2be949e70ec590996f55fbc1874127b015799b1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d677265793f7374796c653d736f6369616c266c6f676f3d776563686174266c6162656c3d576543686174\" alt=\"Static Badge\" data-canonical-src=\"https://img.shields.io/badge/-grey?style=social\u0026amp;logo=wechat\u0026amp;label=WeChat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://twitter.com/intern_lm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/24e5e0fce9e05cf581be41241af21ccb3532cd2e6d1693dae78603ee5c9d831c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d677265793f7374796c653d736f6369616c266c6f676f3d74776974746572266c6162656c3d54776974746572\" alt=\"Static Badge\" data-canonical-src=\"https://img.shields.io/badge/-grey?style=social\u0026amp;logo=twitter\u0026amp;label=Twitter\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://discord.gg/xa29JuW87d\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/81db13b8443a0500322d0d4f3ee7a5941b77affcafb3de3b3ef1c39ce4ed1a93/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d677265793f7374796c653d736f6369616c266c6f676f3d646973636f7264266c6162656c3d446973636f7264\" alt=\"Static Badge\" data-canonical-src=\"https://img.shields.io/badge/-grey?style=social\u0026amp;logo=discord\u0026amp;label=Discord\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003chr\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\u003cdetails open=\"\"\u003e\n\u003csummary\u003e\u003cb\u003e2025\u003c/b\u003e\u003c/summary\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/summary\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2024/11] Support Mono-InternVL with PyTorch engine\u003c/li\u003e\n\u003cli\u003e[2024/10] PyTorchEngine supports graph mode on ascend platform, doubling the inference speed\u003c/li\u003e\n\u003cli\u003e[2024/09] LMDeploy PyTorchEngine adds support for \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/get_started/ascend/get_started.md\"\u003eHuawei Ascend\u003c/a\u003e. See supported models \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/supported_models/supported_models.md\"\u003ehere\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/09] LMDeploy PyTorchEngine achieves 1.3x faster on Llama3-8B inference by introducing CUDA graph\u003c/li\u003e\n\u003cli\u003e[2024/08] LMDeploy is integrated into \u003ca href=\"https://github.com/modelscope/swift\"\u003emodelscope/swift\u003c/a\u003e as the default accelerator for VLMs inference\u003c/li\u003e\n\u003cli\u003e[2024/07] Support Llama3.1 8B, 70B and its TOOLS CALLING\u003c/li\u003e\n\u003cli\u003e[2024/07] Support \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/multi_modal/internvl.md\"\u003eInternVL2\u003c/a\u003e full-series models, \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/multi_modal/xcomposer2d5.md\"\u003eInternLM-XComposer2.5\u003c/a\u003e and \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/llm/api_server_tools.md\"\u003efunction call\u003c/a\u003e of InternLM2.5\u003c/li\u003e\n\u003cli\u003e[2024/06] PyTorch engine support DeepSeek-V2 and several VLMs, such as CogVLM2, Mini-InternVL, LlaVA-Next\u003c/li\u003e\n\u003cli\u003e[2024/05] Balance vision model when deploying VLMs with multiple GPUs\u003c/li\u003e\n\u003cli\u003e[2024/05] Support 4-bits weight-only quantization and inference on VLMs, such as InternVL v1.5, LLaVa, InternLMXComposer2\u003c/li\u003e\n\u003cli\u003e[2024/04] Support Llama3 and more VLMs, such as InternVL v1.1, v1.2, MiniGemini, InternLMXComposer2.\u003c/li\u003e\n\u003cli\u003e[2024/04] TurboMind adds online int8/int4 KV cache quantization and inference for all supported devices. Refer \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/quantization/kv_quant.md\"\u003ehere\u003c/a\u003e for detailed guide\u003c/li\u003e\n\u003cli\u003e[2024/04] TurboMind latest upgrade boosts GQA, rocketing the \u003ca href=\"https://huggingface.co/internlm/internlm2-20b\" rel=\"nofollow\"\u003einternlm2-20b\u003c/a\u003e model inference to 16+ RPS, about 1.8x faster than vLLM.\u003c/li\u003e\n\u003cli\u003e[2024/04] Support Qwen1.5-MOE and dbrx.\u003c/li\u003e\n\u003cli\u003e[2024/03] Support DeepSeek-VL offline inference pipeline and serving.\u003c/li\u003e\n\u003cli\u003e[2024/03] Support VLM offline inference pipeline and serving.\u003c/li\u003e\n\u003cli\u003e[2024/02] Support Qwen 1.5, Gemma, Mistral, Mixtral, Deepseek-MOE and so on.\u003c/li\u003e\n\u003cli\u003e[2024/01] \u003ca href=\"https://github.com/InternLM/OpenAOE\"\u003eOpenAOE\u003c/a\u003e seamless integration with \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/llm/api_server.md\"\u003eLMDeploy Serving Service\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/01] Support for multi-model, multi-machine, multi-card inference services. For usage instructions, please refer to \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/llm/proxy_server.md\"\u003ehere\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/01] Support \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/inference/pytorch.md\"\u003ePyTorch inference engine\u003c/a\u003e, developed entirely in Python, helping to lower the barriers for developers and enable rapid experimentation with new features and technologies.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/summary\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2023/12] Turbomind supports multimodal input.\u003c/li\u003e\n\u003cli\u003e[2023/11] Turbomind supports loading hf model directly. Click \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/inference/load_hf.md\"\u003ehere\u003c/a\u003e for details.\u003c/li\u003e\n\u003cli\u003e[2023/11] TurboMind major upgrades, including: Paged Attention, faster attention kernels without sequence length limitation, 2x faster KV8 kernels, Split-K decoding (Flash Decoding), and W4A16 inference for sm_75\u003c/li\u003e\n\u003cli\u003e[2023/09] TurboMind supports Qwen-14B\u003c/li\u003e\n\u003cli\u003e[2023/09] TurboMind supports InternLM-20B\u003c/li\u003e\n\u003cli\u003e[2023/09] TurboMind supports all features of Code Llama: code completion, infilling, chat / instruct, and python specialist. Click \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/llm/codellama.md\"\u003ehere\u003c/a\u003e for deployment guide\u003c/li\u003e\n\u003cli\u003e[2023/09] TurboMind supports Baichuan2-7B\u003c/li\u003e\n\u003cli\u003e[2023/08] TurboMind supports flash-attention2.\u003c/li\u003e\n\u003cli\u003e[2023/08] TurboMind supports Qwen-7B, dynamic NTK-RoPE scaling and dynamic logN scaling\u003c/li\u003e\n\u003cli\u003e[2023/08] TurboMind supports Windows (tp=1)\u003c/li\u003e\n\u003cli\u003e[2023/08] TurboMind supports 4-bit inference, 2.4x faster than FP16, the fastest open-source implementation. Check \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/quantization/w4a16.md\"\u003ethis\u003c/a\u003e guide for detailed info\u003c/li\u003e\n\u003cli\u003e[2023/08] LMDeploy has launched on the \u003ca href=\"https://huggingface.co/lmdeploy\" rel=\"nofollow\"\u003eHuggingFace Hub\u003c/a\u003e, providing ready-to-use 4-bit models.\u003c/li\u003e\n\u003cli\u003e[2023/08] LMDeploy supports 4-bit quantization using the \u003ca href=\"https://arxiv.org/abs/2306.00978\" rel=\"nofollow\"\u003eAWQ\u003c/a\u003e algorithm.\u003c/li\u003e\n\u003cli\u003e[2023/07] TurboMind supports Llama-2 70B with GQA.\u003c/li\u003e\n\u003cli\u003e[2023/07] TurboMind supports Llama-2 7B/13B.\u003c/li\u003e\n\u003cli\u003e[2023/07] TurboMind supports tensor-parallel inference of InternLM.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntroduction\u003c/h1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\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\"\u003eLMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the \u003ca href=\"https://github.com/open-mmlab/mmrazor\"\u003eMMRazor\u003c/a\u003e and \u003ca href=\"https://github.com/open-mmlab/mmdeploy\"\u003eMMDeploy\u003c/a\u003e teams. It has the following core features:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEfficient Inference\u003c/strong\u003e: LMDeploy delivers up to 1.8x higher request throughput than vLLM, by introducing key features like persistent batch(a.k.a. continuous batching), blocked KV cache, dynamic split\u0026amp;fuse, tensor parallelism, high-performance CUDA kernels and so on.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEffective Quantization\u003c/strong\u003e: LMDeploy supports weight-only and k/v quantization, and the 4-bit inference performance is 2.4x higher than FP16. The quantization quality has been confirmed via OpenCompass evaluation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEffortless Distribution Server\u003c/strong\u003e: Leveraging the request distribution service, LMDeploy facilitates an easy and efficient deployment of multi-model services across multiple machines and cards.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInteractive Inference Mode\u003c/strong\u003e: By caching the k/v of attention during multi-round dialogue processes, the engine remembers dialogue history, thus avoiding repetitive processing of historical sessions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eExcellent Compatibility\u003c/strong\u003e: LMDeploy supports \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/quantization/kv_quant.md\"\u003eKV Cache Quant\u003c/a\u003e, \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/quantization/w4a16.md\"\u003eAWQ\u003c/a\u003e and \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/inference/turbomind_config.md\"\u003eAutomatic Prefix Caching\u003c/a\u003e to be used simultaneously.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePerformance\u003c/h1\u003e\u003ca id=\"user-content-performance\" class=\"anchor\" aria-label=\"Permalink: Performance\" href=\"#performance\"\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\" href=\"https://private-user-images.githubusercontent.com/4560679/293825277-8e455cf1-a792-4fa8-91a2-75df96a2a5ba.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.3wZWJ-iwmJQO1YmG8SCa8XtVGMXtyha2qnjoqLsvb34\"\u003e\u003cimg src=\"https://private-user-images.githubusercontent.com/4560679/293825277-8e455cf1-a792-4fa8-91a2-75df96a2a5ba.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NDI3NjU2MTMsIm5iZiI6MTc0Mjc2NTMxMywicGF0aCI6Ii80NTYwNjc5LzI5MzgyNTI3Ny04ZTQ1NWNmMS1hNzkyLTRmYTgtOTFhMi03NWRmOTZhMmE1YmEucG5nP1gtQW16LUFsZ29yaXRobT1BV1M0LUhNQUMtU0hBMjU2JlgtQW16LUNyZWRlbnRpYWw9QUtJQVZDT0RZTFNBNTNQUUs0WkElMkYyMDI1MDMyMyUyRnVzLWVhc3QtMSUyRnMzJTJGYXdzNF9yZXF1ZXN0JlgtQW16LURhdGU9MjAyNTAzMjNUMjEyODMzWiZYLUFtei1FeHBpcmVzPTMwMCZYLUFtei1TaWduYXR1cmU9MTFmNjk3YTc2OWJiMGU2ZWY0ZjM3NzMxMGVhMjk5MGY0ZjBiYmIyNTA0NmQ4YjIwYzVjMTM1NDAwMGUyNTlmOCZYLUFtei1TaWduZWRIZWFkZXJzPWhvc3QifQ.3wZWJ-iwmJQO1YmG8SCa8XtVGMXtyha2qnjoqLsvb34\" alt=\"v0 1 0-benchmark\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSupported Models\u003c/h1\u003e\u003ca id=\"user-content-supported-models\" class=\"anchor\" aria-label=\"Permalink: Supported Models\" href=\"#supported-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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr align=\"center\" valign=\"middle\"\u003e\n\u003ctd\u003e\n \u003cb\u003eLLMs\u003c/b\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n \u003cb\u003eVLMs\u003c/b\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\u003ctr valign=\"top\"\u003e\n\u003ctd align=\"left\" valign=\"top\"\u003e\n\u003cul dir=\"auto\"\u003e\n \u003cli\u003eLlama (7B - 65B)\u003c/li\u003e\n \u003cli\u003eLlama2 (7B - 70B)\u003c/li\u003e\n \u003cli\u003eLlama3 (8B, 70B)\u003c/li\u003e\n \u003cli\u003eLlama3.1 (8B, 70B)\u003c/li\u003e\n \u003cli\u003eLlama3.2 (1B, 3B)\u003c/li\u003e\n \u003cli\u003eInternLM (7B - 20B)\u003c/li\u003e\n \u003cli\u003eInternLM2 (7B - 20B)\u003c/li\u003e\n \u003cli\u003eInternLM3 (8B)\u003c/li\u003e\n \u003cli\u003eInternLM2.5 (7B)\u003c/li\u003e\n \u003cli\u003eQwen (1.8B - 72B)\u003c/li\u003e\n \u003cli\u003eQwen1.5 (0.5B - 110B)\u003c/li\u003e\n \u003cli\u003eQwen1.5 - MoE (0.5B - 72B)\u003c/li\u003e\n \u003cli\u003eQwen2 (0.5B - 72B)\u003c/li\u003e\n \u003cli\u003eQwen2-MoE (57BA14B)\u003c/li\u003e\n \u003cli\u003eQwen2.5 (0.5B - 32B)\u003c/li\u003e\n \u003cli\u003eBaichuan (7B)\u003c/li\u003e\n \u003cli\u003eBaichuan2 (7B-13B)\u003c/li\u003e\n \u003cli\u003eCode Llama (7B - 34B)\u003c/li\u003e\n \u003cli\u003eChatGLM2 (6B)\u003c/li\u003e\n \u003cli\u003eGLM4 (9B)\u003c/li\u003e\n \u003cli\u003eCodeGeeX4 (9B)\u003c/li\u003e\n \u003cli\u003eFalcon (7B - 180B)\u003c/li\u003e\n \u003cli\u003eYI (6B-34B)\u003c/li\u003e\n \u003cli\u003eMistral (7B)\u003c/li\u003e\n \u003cli\u003eDeepSeek-MoE (16B)\u003c/li\u003e\n \u003cli\u003eDeepSeek-V2 (16B, 236B)\u003c/li\u003e\n \u003cli\u003eDeepSeek-V2.5 (236B)\u003c/li\u003e\n \u003cli\u003eMixtral (8x7B, 8x22B)\u003c/li\u003e\n \u003cli\u003eGemma (2B - 7B)\u003c/li\u003e\n \u003cli\u003eDbrx (132B)\u003c/li\u003e\n \u003cli\u003eStarCoder2 (3B - 15B)\u003c/li\u003e\n \u003cli\u003ePhi-3-mini (3.8B)\u003c/li\u003e\n \u003cli\u003ePhi-3.5-mini (3.8B)\u003c/li\u003e\n \u003cli\u003ePhi-3.5-MoE (16x3.8B)\u003c/li\u003e\n \u003cli\u003eMiniCPM3 (4B)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cul dir=\"auto\"\u003e\n \u003cli\u003eLLaVA(1.5,1.6) (7B-34B)\u003c/li\u003e\n \u003cli\u003eInternLM-XComposer2 (7B, 4khd-7B)\u003c/li\u003e\n \u003cli\u003eInternLM-XComposer2.5 (7B)\u003c/li\u003e\n \u003cli\u003eQwen-VL (7B)\u003c/li\u003e\n \u003cli\u003eQwen2-VL (2B, 7B, 72B)\u003c/li\u003e\n \u003cli\u003eQwen2.5-VL (3B, 7B, 72B)\u003c/li\u003e\n \u003cli\u003eDeepSeek-VL (7B)\u003c/li\u003e\n \u003cli\u003eDeepSeek-VL2 (3B, 16B, 27B)\u003c/li\u003e\n \u003cli\u003eInternVL-Chat (v1.1-v1.5)\u003c/li\u003e\n \u003cli\u003eInternVL2 (1B-76B)\u003c/li\u003e\n \u003cli\u003eInternVL2.5(MPO) (1B-78B)\u003c/li\u003e\n \u003cli\u003eMono-InternVL (2B)\u003c/li\u003e\n \u003cli\u003eChemVLM (8B-26B)\u003c/li\u003e\n \u003cli\u003eMiniGeminiLlama (7B)\u003c/li\u003e\n \u003cli\u003eCogVLM-Chat (17B)\u003c/li\u003e\n \u003cli\u003eCogVLM2-Chat (19B)\u003c/li\u003e\n \u003cli\u003eMiniCPM-Llama3-V-2_5\u003c/li\u003e\n \u003cli\u003eMiniCPM-V-2_6\u003c/li\u003e\n \u003cli\u003ePhi-3-vision (4.2B)\u003c/li\u003e\n \u003cli\u003ePhi-3.5-vision (4.2B)\u003c/li\u003e\n \u003cli\u003eGLM-4V (9B)\u003c/li\u003e\n \u003cli\u003eLlama3.2-vision (11B, 90B)\u003c/li\u003e\n \u003cli\u003eMolmo (7B-D,72B)\u003c/li\u003e\n \u003cli\u003eGemma3 (1B - 27B)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eLMDeploy has developed two inference engines - \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/inference/turbomind.md\"\u003eTurboMind\u003c/a\u003e and \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/inference/pytorch.md\"\u003ePyTorch\u003c/a\u003e, each with a different focus. The former strives for ultimate optimization of inference performance, while the latter, developed purely in Python, aims to decrease the barriers for developers.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThey differ in the types of supported models and the inference data type. Please refer to \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/supported_models/supported_models.md\"\u003ethis table\u003c/a\u003e for each engine's capability and choose the proper one that best fits your actual needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick Start \u003ca href=\"https://colab.research.google.com/drive/1Dh-YlSwg78ZO3AlleO441NF_QP2shs95#scrollTo=YALmXnwCG1pQ\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\u003ca id=\"user-content-quick-start-\" class=\"anchor\" aria-label=\"Permalink: Quick Start \" href=\"#quick-start-\"\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\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\"\u003eIt is recommended installing lmdeploy using pip in a conda environment (python 3.8 - 3.12):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda create -n lmdeploy python=3.8 -y\nconda activate lmdeploy\npip install lmdeploy\"\u003e\u003cpre\u003econda create -n lmdeploy python=3.8 -y\nconda activate lmdeploy\npip install lmdeploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe default prebuilt package is compiled on \u003cstrong\u003eCUDA 12\u003c/strong\u003e since v0.3.0.\nFor more information on installing on CUDA 11+ platform, or for instructions on building from source, please refer to the \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/get_started/installation.md\"\u003einstallation guide\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOffline Batch Inference\u003c/h2\u003e\u003ca id=\"user-content-offline-batch-inference\" class=\"anchor\" aria-label=\"Permalink: Offline Batch Inference\" href=\"#offline-batch-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=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import lmdeploy\nwith lmdeploy.pipeline(\u0026quot;internlm/internlm3-8b-instruct\u0026quot;) as pipe:\n response = pipe([\u0026quot;Hi, pls intro yourself\u0026quot;, \u0026quot;Shanghai is\u0026quot;])\n print(response)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elmdeploy\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elmdeploy\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epipeline\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"internlm/internlm3-8b-instruct\"\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipe\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eresponse\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003epipe\u003c/span\u003e([\u003cspan class=\"pl-s\"\u003e\"Hi, pls intro yourself\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"Shanghai is\"\u003c/span\u003e])\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eresponse\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-note\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-info mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z\"\u003e\u003c/path\u003e\u003c/svg\u003eNote\u003c/p\u003e\u003cp dir=\"auto\"\u003eBy default, LMDeploy downloads model from HuggingFace. If you would like to use models from ModelScope, please install ModelScope by \u003ccode\u003epip install modelscope\u003c/code\u003e and set the environment variable:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eexport LMDEPLOY_USE_MODELSCOPE=True\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you would like to use models from openMind Hub, please install openMind Hub by \u003ccode\u003epip install openmind_hub\u003c/code\u003e and set the environment variable:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eexport LMDEPLOY_USE_OPENMIND_HUB=True\u003c/code\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor more information about inference pipeline, please refer to \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/llm/pipeline.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTutorials\u003c/h1\u003e\u003ca id=\"user-content-tutorials\" class=\"anchor\" aria-label=\"Permalink: Tutorials\" href=\"#tutorials\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease review \u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/get_started/get_started.md\"\u003egetting_started\u003c/a\u003e section for the basic usage of LMDeploy.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor detailed user guides and advanced guides, please refer to our \u003ca href=\"https://lmdeploy.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003etutorials\u003c/a\u003e:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUser Guide\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/llm/pipeline.md\"\u003eLLM Inference pipeline\u003c/a\u003e \u003ca href=\"https://colab.research.google.com/drive/1Dh-YlSwg78ZO3AlleO441NF_QP2shs95#scrollTo=YALmXnwCG1pQ\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/multi_modal/vl_pipeline.md\"\u003eVLM Inference pipeline\u003c/a\u003e \u003ca href=\"https://colab.research.google.com/drive/1nKLfnPeDA3p-FMNw2NhI-KOpk7-nlNjF?usp=sharing\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/llm/api_server.md\"\u003eLLM Serving\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/multi_modal/api_server_vl.md\"\u003eVLM Serving\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/quantization\"\u003eQuantization\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAdvance Guide\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/inference/turbomind.md\"\u003eInference Engine - TurboMind\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/inference/pytorch.md\"\u003eInference Engine - PyTorch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/advance/chat_template.md\"\u003eCustomize chat templates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/advance/pytorch_new_model.md\"\u003eAdd a new model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003egemm tuning\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/advance/long_context.md\"\u003eLong context inference\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/InternLM/lmdeploy/blob/main/docs/en/llm/proxy_server.md\"\u003eMulti-model inference service\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eThird-party projects\u003c/h1\u003e\u003ca id=\"user-content-third-party-projects\" class=\"anchor\" aria-label=\"Permalink: Third-party projects\" href=\"#third-party-projects\"\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\n\u003cp dir=\"auto\"\u003eDeploying LLMs offline on the NVIDIA Jetson platform by LMDeploy: \u003ca href=\"https://github.com/BestAnHongjun/LMDeploy-Jetson\"\u003eLMDeploy-Jetson\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eExample project for deploying LLMs using LMDeploy and BentoML: \u003ca href=\"https://github.com/bentoml/BentoLMDeploy\"\u003eBentoLMDeploy\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributing\u003c/h1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe appreciate all contributions to LMDeploy. Please refer to \u003ca href=\"/InternLM/lmdeploy/blob/main/.github/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e for the contributing guideline.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgement\u003c/h1\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\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/NVIDIA/FasterTransformer\"\u003eFasterTransformer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mit-han-lab/llm-awq\"\u003ellm-awq\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/vllm-project/vllm\"\u003evLLM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/microsoft/DeepSpeed-MII\"\u003eDeepSpeed-MII\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@misc{2023lmdeploy,\n title={LMDeploy: A Toolkit for Compressing, Deploying, and Serving LLM},\n author={LMDeploy Contributors},\n howpublished = {\\url{https://github.com/InternLM/lmdeploy}},\n year={2023}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@misc\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003e2023lmdeploy\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eLMDeploy: A Toolkit for Compressing, Deploying, and Serving LLM\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eLMDeploy Contributors\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ehowpublished\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e\\url{https://github.com/InternLM/lmdeploy}\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2023\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\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 released under the \u003ca href=\"/InternLM/lmdeploy/blob/main/LICENSE\"\u003eApache 2.0 license\u003c/a\u003e.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"Latest News 🎉","anchor":"latest-news-","htmlText":"Latest News 🎉"},{"level":1,"text":"Introduction","anchor":"introduction","htmlText":"Introduction"},{"level":1,"text":"Performance","anchor":"performance","htmlText":"Performance"},{"level":1,"text":"Supported Models","anchor":"supported-models","htmlText":"Supported Models"},{"level":1,"text":"Quick Start ","anchor":"quick-start-","htmlText":"Quick Start 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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> <details open=""> <summary><b>2025</b></summary> </details> <details> <summary><b>2024</b></summary> <ul dir="auto"> <li>[2024/11] Support Mono-InternVL with PyTorch engine</li> <li>[2024/10] PyTorchEngine supports graph mode on ascend platform, doubling the inference speed</li> <li>[2024/09] LMDeploy PyTorchEngine adds support for <a href="/InternLM/lmdeploy/blob/main/docs/en/get_started/ascend/get_started.md">Huawei Ascend</a>. See supported models <a href="/InternLM/lmdeploy/blob/main/docs/en/supported_models/supported_models.md">here</a></li> <li>[2024/09] LMDeploy PyTorchEngine achieves 1.3x faster on Llama3-8B inference by introducing CUDA graph</li> <li>[2024/08] LMDeploy is integrated into <a href="https://github.com/modelscope/swift">modelscope/swift</a> as the default accelerator for VLMs inference</li> <li>[2024/07] Support Llama3.1 8B, 70B and its TOOLS CALLING</li> <li>[2024/07] Support <a href="/InternLM/lmdeploy/blob/main/docs/en/multi_modal/internvl.md">InternVL2</a> full-series models, <a href="/InternLM/lmdeploy/blob/main/docs/en/multi_modal/xcomposer2d5.md">InternLM-XComposer2.5</a> and <a href="/InternLM/lmdeploy/blob/main/docs/en/llm/api_server_tools.md">function call</a> of InternLM2.5</li> <li>[2024/06] PyTorch engine support DeepSeek-V2 and several VLMs, such as CogVLM2, Mini-InternVL, LlaVA-Next</li> <li>[2024/05] Balance vision model when deploying VLMs with multiple GPUs</li> <li>[2024/05] Support 4-bits weight-only quantization and inference on VLMs, such as InternVL v1.5, LLaVa, InternLMXComposer2</li> <li>[2024/04] Support Llama3 and more VLMs, such as InternVL v1.1, v1.2, MiniGemini, InternLMXComposer2.</li> <li>[2024/04] TurboMind adds online int8/int4 KV cache quantization and inference for all supported devices. Refer <a href="/InternLM/lmdeploy/blob/main/docs/en/quantization/kv_quant.md">here</a> for detailed guide</li> <li>[2024/04] TurboMind latest upgrade boosts GQA, rocketing the <a href="https://huggingface.co/internlm/internlm2-20b" rel="nofollow">internlm2-20b</a> model inference to 16+ RPS, about 1.8x faster than vLLM.</li> <li>[2024/04] Support Qwen1.5-MOE and dbrx.</li> <li>[2024/03] Support DeepSeek-VL offline inference pipeline and serving.</li> <li>[2024/03] Support VLM offline inference pipeline and serving.</li> <li>[2024/02] Support Qwen 1.5, Gemma, Mistral, Mixtral, Deepseek-MOE and so on.</li> <li>[2024/01] <a href="https://github.com/InternLM/OpenAOE">OpenAOE</a> seamless integration with <a href="/InternLM/lmdeploy/blob/main/docs/en/llm/api_server.md">LMDeploy Serving Service</a>.</li> <li>[2024/01] Support for multi-model, multi-machine, multi-card inference services. For usage instructions, please refer to <a href="/InternLM/lmdeploy/blob/main/docs/en/llm/proxy_server.md">here</a></li> <li>[2024/01] Support <a href="/InternLM/lmdeploy/blob/main/docs/en/inference/pytorch.md">PyTorch inference engine</a>, developed entirely in Python, helping to lower the barriers for developers and enable rapid experimentation with new features and technologies.</li> </ul> </details> <details> <summary><b>2023</b></summary> <ul dir="auto"> <li>[2023/12] Turbomind supports multimodal input.</li> <li>[2023/11] Turbomind supports loading hf model directly. Click <a href="/InternLM/lmdeploy/blob/main/docs/en/inference/load_hf.md">here</a> for details.</li> <li>[2023/11] TurboMind major upgrades, including: Paged Attention, faster attention kernels without sequence length limitation, 2x faster KV8 kernels, Split-K decoding (Flash Decoding), and W4A16 inference for sm_75</li> <li>[2023/09] TurboMind supports Qwen-14B</li> <li>[2023/09] TurboMind supports InternLM-20B</li> <li>[2023/09] TurboMind supports all features of Code Llama: code completion, infilling, chat / instruct, and python specialist. Click <a href="/InternLM/lmdeploy/blob/main/docs/en/llm/codellama.md">here</a> for deployment guide</li> <li>[2023/09] TurboMind supports Baichuan2-7B</li> <li>[2023/08] TurboMind supports flash-attention2.</li> <li>[2023/08] TurboMind supports Qwen-7B, dynamic NTK-RoPE scaling and dynamic logN scaling</li> <li>[2023/08] TurboMind supports Windows (tp=1)</li> <li>[2023/08] TurboMind supports 4-bit inference, 2.4x faster than FP16, the fastest open-source implementation. Check <a href="/InternLM/lmdeploy/blob/main/docs/en/quantization/w4a16.md">this</a> guide for detailed info</li> <li>[2023/08] LMDeploy has launched on the <a href="https://huggingface.co/lmdeploy" rel="nofollow">HuggingFace Hub</a>, providing ready-to-use 4-bit models.</li> <li>[2023/08] LMDeploy supports 4-bit quantization using the <a href="https://arxiv.org/abs/2306.00978" rel="nofollow">AWQ</a> algorithm.</li> <li>[2023/07] TurboMind supports Llama-2 70B with GQA.</li> <li>[2023/07] TurboMind supports Llama-2 7B/13B.</li> <li>[2023/07] TurboMind supports tensor-parallel inference of InternLM.</li> </ul> </details> <hr> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Introduction</h1><a id="user-content-introduction" class="anchor" aria-label="Permalink: Introduction" href="#introduction"><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">LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the <a href="https://github.com/open-mmlab/mmrazor">MMRazor</a> and <a href="https://github.com/open-mmlab/mmdeploy">MMDeploy</a> teams. It has the following core features:</p> <ul dir="auto"> <li> <p dir="auto"><strong>Efficient Inference</strong>: LMDeploy delivers up to 1.8x higher request throughput than vLLM, by introducing key features like persistent batch(a.k.a. continuous batching), blocked KV cache, dynamic split&fuse, tensor parallelism, high-performance CUDA kernels and so on.</p> </li> <li> <p dir="auto"><strong>Effective Quantization</strong>: LMDeploy supports weight-only and k/v quantization, and the 4-bit inference performance is 2.4x higher than FP16. The quantization quality has been confirmed via OpenCompass evaluation.</p> </li> <li> <p dir="auto"><strong>Effortless Distribution Server</strong>: Leveraging the request distribution service, LMDeploy facilitates an easy and efficient deployment of multi-model services across multiple machines and cards.</p> </li> <li> <p dir="auto"><strong>Interactive Inference Mode</strong>: By caching the k/v of attention during multi-round dialogue processes, the engine remembers dialogue history, thus avoiding repetitive processing of historical sessions.</p> </li> <li> <p dir="auto"><strong>Excellent Compatibility</strong>: LMDeploy supports <a href="/InternLM/lmdeploy/blob/main/docs/en/quantization/kv_quant.md">KV Cache Quant</a>, <a href="/InternLM/lmdeploy/blob/main/docs/en/quantization/w4a16.md">AWQ</a> and <a href="/InternLM/lmdeploy/blob/main/docs/en/inference/turbomind_config.md">Automatic Prefix Caching</a> to be used simultaneously.</p> </li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Performance</h1><a id="user-content-performance" class="anchor" aria-label="Permalink: Performance" href="#performance"><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" href="https://private-user-images.githubusercontent.com/4560679/293825277-8e455cf1-a792-4fa8-91a2-75df96a2a5ba.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.3wZWJ-iwmJQO1YmG8SCa8XtVGMXtyha2qnjoqLsvb34"><img src="https://private-user-images.githubusercontent.com/4560679/293825277-8e455cf1-a792-4fa8-91a2-75df96a2a5ba.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.3wZWJ-iwmJQO1YmG8SCa8XtVGMXtyha2qnjoqLsvb34" alt="v0 1 0-benchmark" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Supported Models</h1><a id="user-content-supported-models" class="anchor" aria-label="Permalink: Supported Models" href="#supported-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> <markdown-accessiblity-table><table> <tbody> <tr align="center" valign="middle"> <td> <b>LLMs</b> </td> <td> <b>VLMs</b> </td> </tr><tr valign="top"> <td align="left" valign="top"> <ul dir="auto"> <li>Llama (7B - 65B)</li> <li>Llama2 (7B - 70B)</li> <li>Llama3 (8B, 70B)</li> <li>Llama3.1 (8B, 70B)</li> <li>Llama3.2 (1B, 3B)</li> <li>InternLM (7B - 20B)</li> <li>InternLM2 (7B - 20B)</li> <li>InternLM3 (8B)</li> <li>InternLM2.5 (7B)</li> <li>Qwen (1.8B - 72B)</li> <li>Qwen1.5 (0.5B - 110B)</li> <li>Qwen1.5 - MoE (0.5B - 72B)</li> <li>Qwen2 (0.5B - 72B)</li> <li>Qwen2-MoE (57BA14B)</li> <li>Qwen2.5 (0.5B - 32B)</li> <li>Baichuan (7B)</li> <li>Baichuan2 (7B-13B)</li> <li>Code Llama (7B - 34B)</li> <li>ChatGLM2 (6B)</li> <li>GLM4 (9B)</li> <li>CodeGeeX4 (9B)</li> <li>Falcon (7B - 180B)</li> <li>YI (6B-34B)</li> <li>Mistral (7B)</li> <li>DeepSeek-MoE (16B)</li> <li>DeepSeek-V2 (16B, 236B)</li> <li>DeepSeek-V2.5 (236B)</li> <li>Mixtral (8x7B, 8x22B)</li> <li>Gemma (2B - 7B)</li> <li>Dbrx (132B)</li> <li>StarCoder2 (3B - 15B)</li> <li>Phi-3-mini (3.8B)</li> <li>Phi-3.5-mini (3.8B)</li> <li>Phi-3.5-MoE (16x3.8B)</li> <li>MiniCPM3 (4B)</li> </ul> </td> <td> <ul dir="auto"> <li>LLaVA(1.5,1.6) (7B-34B)</li> <li>InternLM-XComposer2 (7B, 4khd-7B)</li> <li>InternLM-XComposer2.5 (7B)</li> <li>Qwen-VL (7B)</li> <li>Qwen2-VL (2B, 7B, 72B)</li> <li>Qwen2.5-VL (3B, 7B, 72B)</li> <li>DeepSeek-VL (7B)</li> <li>DeepSeek-VL2 (3B, 16B, 27B)</li> <li>InternVL-Chat (v1.1-v1.5)</li> <li>InternVL2 (1B-76B)</li> <li>InternVL2.5(MPO) (1B-78B)</li> <li>Mono-InternVL (2B)</li> <li>ChemVLM (8B-26B)</li> <li>MiniGeminiLlama (7B)</li> <li>CogVLM-Chat (17B)</li> <li>CogVLM2-Chat (19B)</li> <li>MiniCPM-Llama3-V-2_5</li> <li>MiniCPM-V-2_6</li> <li>Phi-3-vision (4.2B)</li> <li>Phi-3.5-vision (4.2B)</li> <li>GLM-4V (9B)</li> <li>Llama3.2-vision (11B, 90B)</li> <li>Molmo (7B-D,72B)</li> <li>Gemma3 (1B - 27B)</li> </ul> </td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">LMDeploy has developed two inference engines - <a href="/InternLM/lmdeploy/blob/main/docs/en/inference/turbomind.md">TurboMind</a> and <a href="/InternLM/lmdeploy/blob/main/docs/en/inference/pytorch.md">PyTorch</a>, each with a different focus. The former strives for ultimate optimization of inference performance, while the latter, developed purely in Python, aims to decrease the barriers for developers.</p> <p dir="auto">They differ in the types of supported models and the inference data type. Please refer to <a href="/InternLM/lmdeploy/blob/main/docs/en/supported_models/supported_models.md">this table</a> for each engine's capability and choose the proper one that best fits your actual needs.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Quick Start <a href="https://colab.research.google.com/drive/1Dh-YlSwg78ZO3AlleO441NF_QP2shs95#scrollTo=YALmXnwCG1pQ" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></h1><a id="user-content-quick-start-" class="anchor" aria-label="Permalink: Quick Start " href="#quick-start-"><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"><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">It is recommended installing lmdeploy using pip in a conda environment (python 3.8 - 3.12):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda create -n lmdeploy python=3.8 -y conda activate lmdeploy pip install lmdeploy"><pre>conda create -n lmdeploy python=3.8 -y conda activate lmdeploy pip install lmdeploy</pre></div> <p dir="auto">The default prebuilt package is compiled on <strong>CUDA 12</strong> since v0.3.0. For more information on installing on CUDA 11+ platform, or for instructions on building from source, please refer to the <a href="/InternLM/lmdeploy/blob/main/docs/en/get_started/installation.md">installation guide</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Offline Batch Inference</h2><a id="user-content-offline-batch-inference" class="anchor" aria-label="Permalink: Offline Batch Inference" href="#offline-batch-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="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import lmdeploy with lmdeploy.pipeline("internlm/internlm3-8b-instruct") as pipe: response = pipe(["Hi, pls intro yourself", "Shanghai is"]) print(response)"><pre><span class="pl-k">import</span> <span class="pl-s1">lmdeploy</span> <span class="pl-k">with</span> <span class="pl-s1">lmdeploy</span>.<span class="pl-c1">pipeline</span>(<span class="pl-s">"internlm/internlm3-8b-instruct"</span>) <span class="pl-k">as</span> <span class="pl-s1">pipe</span>: <span class="pl-s1">response</span> <span class="pl-c1">=</span> <span class="pl-en">pipe</span>([<span class="pl-s">"Hi, pls intro yourself"</span>, <span class="pl-s">"Shanghai is"</span>]) <span class="pl-en">print</span>(<span class="pl-s1">response</span>)</pre></div> <div class="markdown-alert markdown-alert-note" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-info mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg>Note</p><p dir="auto">By default, LMDeploy downloads model from HuggingFace. If you would like to use models from ModelScope, please install ModelScope by <code>pip install modelscope</code> and set the environment variable:</p> <p dir="auto"><code>export LMDEPLOY_USE_MODELSCOPE=True</code></p> <p dir="auto">If you would like to use models from openMind Hub, please install openMind Hub by <code>pip install openmind_hub</code> and set the environment variable:</p> <p dir="auto"><code>export LMDEPLOY_USE_OPENMIND_HUB=True</code></p> </div> <p dir="auto">For more information about inference pipeline, please refer to <a href="/InternLM/lmdeploy/blob/main/docs/en/llm/pipeline.md">here</a>.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Tutorials</h1><a id="user-content-tutorials" class="anchor" aria-label="Permalink: Tutorials" href="#tutorials"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please review <a href="/InternLM/lmdeploy/blob/main/docs/en/get_started/get_started.md">getting_started</a> section for the basic usage of LMDeploy.</p> <p dir="auto">For detailed user guides and advanced guides, please refer to our <a href="https://lmdeploy.readthedocs.io/en/latest/" rel="nofollow">tutorials</a>:</p> <ul dir="auto"> <li>User Guide <ul dir="auto"> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/llm/pipeline.md">LLM Inference pipeline</a> <a href="https://colab.research.google.com/drive/1Dh-YlSwg78ZO3AlleO441NF_QP2shs95#scrollTo=YALmXnwCG1pQ" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></li> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/multi_modal/vl_pipeline.md">VLM Inference pipeline</a> <a href="https://colab.research.google.com/drive/1nKLfnPeDA3p-FMNw2NhI-KOpk7-nlNjF?usp=sharing" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></li> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/llm/api_server.md">LLM Serving</a></li> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/multi_modal/api_server_vl.md">VLM Serving</a></li> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/quantization">Quantization</a></li> </ul> </li> <li>Advance Guide <ul dir="auto"> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/inference/turbomind.md">Inference Engine - TurboMind</a></li> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/inference/pytorch.md">Inference Engine - PyTorch</a></li> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/advance/chat_template.md">Customize chat templates</a></li> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/advance/pytorch_new_model.md">Add a new model</a></li> <li>gemm tuning</li> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/advance/long_context.md">Long context inference</a></li> <li><a href="/InternLM/lmdeploy/blob/main/docs/en/llm/proxy_server.md">Multi-model inference service</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Third-party projects</h1><a id="user-content-third-party-projects" class="anchor" aria-label="Permalink: Third-party projects" href="#third-party-projects"><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> <p dir="auto">Deploying LLMs offline on the NVIDIA Jetson platform by LMDeploy: <a href="https://github.com/BestAnHongjun/LMDeploy-Jetson">LMDeploy-Jetson</a></p> </li> <li> <p dir="auto">Example project for deploying LLMs using LMDeploy and BentoML: <a href="https://github.com/bentoml/BentoLMDeploy">BentoLMDeploy</a></p> </li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Contributing</h1><a id="user-content-contributing" class="anchor" aria-label="Permalink: Contributing" href="#contributing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We appreciate all contributions to LMDeploy. Please refer to <a href="/InternLM/lmdeploy/blob/main/.github/CONTRIBUTING.md">CONTRIBUTING.md</a> for the contributing guideline.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Acknowledgement</h1><a id="user-content-acknowledgement" class="anchor" aria-label="Permalink: Acknowledgement" href="#acknowledgement"><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/NVIDIA/FasterTransformer">FasterTransformer</a></li> <li><a href="https://github.com/mit-han-lab/llm-awq">llm-awq</a></li> <li><a href="https://github.com/vllm-project/vllm">vLLM</a></li> <li><a href="https://github.com/microsoft/DeepSpeed-MII">DeepSpeed-MII</a></li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Citation</h1><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@misc{2023lmdeploy, title={LMDeploy: A Toolkit for Compressing, Deploying, and Serving LLM}, author={LMDeploy Contributors}, howpublished = {\url{https://github.com/InternLM/lmdeploy}}, year={2023} }"><pre><span class="pl-k">@misc</span>{<span class="pl-en">2023lmdeploy</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>LMDeploy: A Toolkit for Compressing, Deploying, and Serving LLM<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>LMDeploy Contributors<span class="pl-pds">}</span></span>, <span class="pl-s">howpublished</span> = <span class="pl-s"><span class="pl-pds">{</span>\url{https://github.com/InternLM/lmdeploy}<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2023<span class="pl-pds">}</span></span> }</pre></div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">License</h1><a id="user-content-license" class="anchor" aria-label="Permalink: License" href="#license"><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 released under the <a href="/InternLM/lmdeploy/blob/main/LICENSE">Apache 2.0 license</a>.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="qznR/gZoOgyHPwaxv9hJ+jmhV0acHrY48atW0FJDyOV4IY4uwIBFu/zyL0zfoK0jpgHIN/GRTUO+2PnInVJ3Ag==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> LMDeploy is a toolkit for compressing, deploying, and serving LLMs. </p> <div class="my-3 d-flex flex-items-center"> <svg 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class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#3572A5 !important;;width: 52.3%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#f34b7d !important;;width: 29.7%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3A4E3A !important;;width: 16.7%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#DA3434 !important;;width: 1.0%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#89e051 !important;;width: 0.2%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#012456 !important;;width: 0.1%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/InternLM/lmdeploy/search?l=python" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>52.3%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/InternLM/lmdeploy/search?l=c%2B%2B" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f34b7d;" 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href="/InternLM/lmdeploy/search?l=cmake" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#DA3434;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">CMake</span> <span>1.0%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/InternLM/lmdeploy/search?l=shell" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#89e051;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Shell</span> <span>0.2%</span> </a> </li> 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