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GitHub - bobazooba/xllm: 🦖 X—LLM: Cutting Edge & Easy LLM Finetuning
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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":"blog","context":"product","tag":"link","label":"blog_link_product_navbar"}" href="https://github.blog"> Blog <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> </ul> </div> </div> </div> </li> <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"> Solutions <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 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 pb-lg-3 mb-3 mb-lg-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-company-size-heading">By company size</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-company-size-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"enterprises","context":"solutions","tag":"link","label":"enterprises_link_solutions_navbar"}" href="https://github.com/enterprise"> Enterprises </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"small_and_medium_teams","context":"solutions","tag":"link","label":"small_and_medium_teams_link_solutions_navbar"}" href="https://github.com/team"> Small and medium teams </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"startups","context":"solutions","tag":"link","label":"startups_link_solutions_navbar"}" href="https://github.com/enterprise/startups"> Startups </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"nonprofits","context":"solutions","tag":"link","label":"nonprofits_link_solutions_navbar"}" href="/solutions/industry/nonprofits"> Nonprofits </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-use-case-heading">By use case</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-use-case-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"devsecops","context":"solutions","tag":"link","label":"devsecops_link_solutions_navbar"}" href="/solutions/use-case/devsecops"> DevSecOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"devops","context":"solutions","tag":"link","label":"devops_link_solutions_navbar"}" href="/solutions/use-case/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"ci_cd","context":"solutions","tag":"link","label":"ci_cd_link_solutions_navbar"}" href="/solutions/use-case/ci-cd"> CI/CD </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_use_cases","context":"solutions","tag":"link","label":"view_all_use_cases_link_solutions_navbar"}" href="/solutions/use-case"> View all use cases </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"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-industry-heading">By industry</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-industry-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"healthcare","context":"solutions","tag":"link","label":"healthcare_link_solutions_navbar"}" href="/solutions/industry/healthcare"> Healthcare </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"financial_services","context":"solutions","tag":"link","label":"financial_services_link_solutions_navbar"}" href="/solutions/industry/financial-services"> Financial services </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"manufacturing","context":"solutions","tag":"link","label":"manufacturing_link_solutions_navbar"}" href="/solutions/industry/manufacturing"> Manufacturing </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"government","context":"solutions","tag":"link","label":"government_link_solutions_navbar"}" href="/solutions/industry/government"> Government </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 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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"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul class="list-style-none f5" aria-labelledby="resources-topics-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"ai","context":"resources","tag":"link","label":"ai_link_resources_navbar"}" href="/resources/articles/ai"> AI </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"devops","context":"resources","tag":"link","label":"devops_link_resources_navbar"}" href="/resources/articles/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"security","context":"resources","tag":"link","label":"security_link_resources_navbar"}" href="/resources/articles/security"> Security </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"software_development","context":"resources","tag":"link","label":"software_development_link_resources_navbar"}" href="/resources/articles/software-development"> Software Development </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all","context":"resources","tag":"link","label":"view_all_link_resources_navbar"}" href="/resources/articles"> View all </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="resources-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="resources-explore-heading"> <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":"learning_pathways","context":"resources","tag":"link","label":"learning_pathways_link_resources_navbar"}" href="https://resources.github.com/learn/pathways"> Learning Pathways <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":"events_amp_webinars","context":"resources","tag":"link","label":"events_amp_webinars_link_resources_navbar"}" href="https://resources.github.com"> Events & Webinars <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" data-analytics-event="{"location":"navbar","action":"ebooks_amp_whitepapers","context":"resources","tag":"link","label":"ebooks_amp_whitepapers_link_resources_navbar"}" href="https://github.com/resources/whitepapers"> Ebooks & Whitepapers </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"customer_stories","context":"resources","tag":"link","label":"customer_stories_link_resources_navbar"}" href="https://github.com/customer-stories"> Customer Stories </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":"partners","context":"resources","tag":"link","label":"partners_link_resources_navbar"}" href="https://partner.github.com"> Partners <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" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <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"> Open Source <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 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <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" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <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" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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src=\"https://camo.githubusercontent.com/a6f4ebc9714ebd22e00354d3bfc03171a72577bdf34479ae5b9f78479454551e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7072652d2d636f6d6d69742d656e61626c65642d627269676874677265656e3f6c6f676f3d7072652d636f6d6d6974266c6f676f436f6c6f723d676f6c6426636f6c6f723d363343343632267374796c653d666c61742d737175617265\" alt=\"Pre-commit\" data-canonical-src=\"https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit\u0026amp;logoColor=gold\u0026amp;color=63C462\u0026amp;style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/psf/black\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7d7a1cff87afce92c01c43d96236db595edb2395e6a0826c0b294345797c0bef/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e7376673f6c6f676f436f6c6f723d676f6c6426636f6c6f723d363343343632267374796c653d666c61742d737175617265\" alt=\"Code style: black\" 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rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fafd33db5d8e943cd3e4cb21a26456ccefd294267c53943facc151dffee8951b/687474703a2f2f7777772e6d7970792d6c616e672e6f72672f7374617469632f6d7970795f62616467652e737667\" alt=\"Checked with mypy\" data-canonical-src=\"http://www.mypy-lang.org/static/mypy_badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/BobaZooba/xllm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3cd4970323fa001e53d4cfd5e7fdd246e145488a9e764aecc9ae32570250a388/68747470733a2f2f636f6465636f762e696f2f67682f426f62615a6f6f62612f786c6c6d2f67726170682f62616467652e7376673f746f6b656e3d5a4f424d445856573442\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/BobaZooba/xllm/graph/badge.svg?token=ZOBMDXVW4B\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://discord.gg/5znbxBgwZP\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ae7281cc58c433672fa3a78740983c6604590eb1b0664f84ff81bd53edc03993/68747470733a2f2f646362616467652e76657263656c2e6170702f6170692f7365727665722f357a6e62784267775a50\" alt=\"\" data-canonical-src=\"https://dcbadge.vercel.app/api/server/5znbxBgwZP\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCutting Edge \u0026amp; Easy LLM Finetuning using the most advanced methods (QLoRA, DeepSpeed, GPTQ, Flash Attention 2, FSDP,\netc)\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDeveloped\nby \u003ca href=\"https://t.me/BobaZooba\" rel=\"nofollow\"\u003eBoris Zubarev\u003c/a\u003e | \u003ca href=\"https://docs.google.com/document/d/1BhFvIHQ1mpm81P-n2A-lhNac-U2wOGc6F2uS9gKvk88/edit?usp=sharing\" rel=\"nofollow\"\u003eCV\u003c/a\u003e | \u003ca href=\"https://www.linkedin.com/in/boriszubarev/\" rel=\"nofollow\"\u003eLinkedIn\u003c/a\u003e | \u003ca href=\"mailto:bobazooba@gmail.com\"\u003ebobazooba@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhy you should use X—LLM 🪄\u003c/h1\u003e\u003ca id=\"user-content-why-you-should-use-xllm-\" class=\"anchor\" aria-label=\"Permalink: Why you should use X—LLM 🪄\" href=\"#why-you-should-use-xllm-\"\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\"\u003eAre you using \u003cstrong\u003eLarge Language Models (LLMs)\u003c/strong\u003e for your work and want to train them more efficiently with advanced\nmethods? Wish to focus on the data and improvements rather than repetitive and time-consuming coding for LLM training?\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eX—LLM\u003c/strong\u003e is your solution. It's a user-friendly library that streamlines training optimization, so you can \u003cstrong\u003efocus on\nenhancing your models and data\u003c/strong\u003e. Equipped with \u003cstrong\u003ecutting-edge training techniques\u003c/strong\u003e, X—LLM is engineered for efficiency\nby engineers who understand your needs.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eX—LLM\u003c/strong\u003e is ideal whether you're \u003cstrong\u003egearing up for production\u003c/strong\u003e or need a \u003cstrong\u003efast prototyping tool\u003c/strong\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHassle-free training for Large Language Models\u003c/li\u003e\n\u003cli\u003eSeamless integration of new data and data processing\u003c/li\u003e\n\u003cli\u003eEffortless expansion of the library\u003c/li\u003e\n\u003cli\u003eSpeed up your training, while simultaneously reducing model sizes\u003c/li\u003e\n\u003cli\u003eEach checkpoint is saved to the 🤗 HuggingFace Hub\u003c/li\u003e\n\u003cli\u003eEasy-to-use integration with your existing project\u003c/li\u003e\n\u003cli\u003eCustomize almost any part of your training with ease\u003c/li\u003e\n\u003cli\u003eTrack your training progress using \u003ccode\u003eW\u0026amp;B\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eSupported many 🤗 Transformers models\nlike \u003ccode\u003eYi-34B\u003c/code\u003e, \u003ccode\u003eMistal AI\u003c/code\u003e, \u003ccode\u003eLlama 2\u003c/code\u003e, \u003ccode\u003eZephyr\u003c/code\u003e, \u003ccode\u003eOpenChat\u003c/code\u003e, \u003ccode\u003eFalcon\u003c/code\u003e, \u003ccode\u003ePhi\u003c/code\u003e, \u003ccode\u003eQwen\u003c/code\u003e, \u003ccode\u003eMPT\u003c/code\u003e and many more\u003c/li\u003e\n\u003cli\u003eBenefit from cutting-edge advancements in LLM training optimization\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eQLoRA and fusing\u003c/li\u003e\n\u003cli\u003eFlash Attention 2\u003c/li\u003e\n\u003cli\u003eGradient checkpointing\u003c/li\u003e\n\u003cli\u003ebitsandbytes\u003c/li\u003e\n\u003cli\u003eGPTQ (including post-training quantization)\u003c/li\u003e\n\u003cli\u003eDeepSpeed\u003c/li\u003e\n\u003cli\u003eFSDP\u003c/li\u003e\n\u003cli\u003eAnd many more\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\"\u003eQuickstart 🦖\u003c/h1\u003e\u003ca id=\"user-content-quickstart-\" class=\"anchor\" aria-label=\"Permalink: Quickstart 🦖\" href=\"#quickstart-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h3\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\"\u003e\u003ccode\u003eX—LLM\u003c/code\u003e is tested on Python 3.8+, PyTorch 2.0.1+ and CUDA 11.8.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install xllm\"\u003e\u003cpre\u003epip install xllm\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eVersion which include \u003ccode\u003edeepspeed\u003c/code\u003e, \u003ccode\u003eflash-attn\u003c/code\u003e and \u003ccode\u003eauto-gptq\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=\"pip install \u0026quot;xllm[train]\u0026quot;\"\u003e\u003cpre\u003epip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003exllm[train]\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDefault \u003ccode\u003exllm\u003c/code\u003e version recommended for local development, \u003ccode\u003exllm[train]\u003c/code\u003e recommended for training.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining recommended environment\u003c/h4\u003e\u003ca id=\"user-content-training-recommended-environment\" class=\"anchor\" aria-label=\"Permalink: Training recommended environment\" href=\"#training-recommended-environment\"\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\"\u003eCUDA version: \u003ccode\u003e11.8\u003c/code\u003e\nDocker: \u003ccode\u003ehuggingface/transformers-pytorch-gpu:latest\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFast prototyping ⚡\u003c/h2\u003e\u003ca id=\"user-content-fast-prototyping-\" class=\"anchor\" aria-label=\"Permalink: Fast prototyping ⚡\" href=\"#fast-prototyping-\"\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=\"from xllm import Config\nfrom xllm.datasets import GeneralDataset\nfrom xllm.experiments import Experiment\n\n# Init Config which controls the internal logic of xllm\n# QLoRA example\nconfig = Config(\n model_name_or_path=\u0026quot;HuggingFaceH4/zephyr-7b-beta\u0026quot;,\n apply_lora=True,\n load_in_4bit=True,\n)\n\n# Prepare the data\ntrain_data = [\u0026quot;Hello!\u0026quot;] * 100\ntrain_dataset = GeneralDataset.from_list(data=train_data)\n\n# Build Experiment from Config: init tokenizer and model, apply LoRA and so on\nexperiment = Experiment(config=config, train_dataset=train_dataset)\nexperiment.build()\n\n# Run Experiment (training)\nexperiment.run()\n\n# # [Optional] Fuse LoRA layers\n# experiment.fuse_lora()\n\n# [Optional] Or push LoRA weights to HuggingFace Hub\nexperiment.push_to_hub(repo_id=\u0026quot;YOUR_NAME/MODEL_NAME\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGeneralDataset\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexperiments\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eExperiment\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Init Config which controls the internal logic of xllm\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# QLoRA example\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"HuggingFaceH4/zephyr-7b-beta\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eapply_lora\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eload_in_4bit\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n)\n\n\u003cspan class=\"pl-c\"\u003e# Prepare the data\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s\"\u003e\"Hello!\"\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGeneralDataset\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_list\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Build Experiment from Config: init tokenizer and model, apply LoRA and so on\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eexperiment\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eExperiment\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eexperiment\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebuild\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# Run Experiment (training)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eexperiment\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erun\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# # [Optional] Fuse LoRA layers\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# experiment.fuse_lora()\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# [Optional] Or push LoRA weights to HuggingFace Hub\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eexperiment\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epush_to_hub\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003erepo_id\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"YOUR_NAME/MODEL_NAME\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow \u003ccode\u003eConfig\u003c/code\u003e controls \u003ccode\u003exllm\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-how-config-controls-xllm\" class=\"anchor\" aria-label=\"Permalink: How Config controls xllm\" href=\"#how-config-controls-xllm\"\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/BobaZooba/xllm/blob/main/GUIDE.md#config\"\u003eMore about config\u003c/a\u003e\u003c/p\u003e\n\u003cdetails\u003e\n \u003csummary\u003eLoRA\u003c/summary\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimple\u003c/h4\u003e\u003ca id=\"user-content-simple\" class=\"anchor\" aria-label=\"Permalink: Simple\" href=\"#simple\"\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=\"config = Config(\n model_name_or_path=\u0026quot;openchat/openchat_3.5\u0026quot;,\n apply_lora=True,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"openchat/openchat_3.5\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eapply_lora\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdvanced\u003c/h4\u003e\u003ca id=\"user-content-advanced\" class=\"anchor\" aria-label=\"Permalink: Advanced\" href=\"#advanced\"\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=\"config = Config(\n model_name_or_path=\u0026quot;openchat/openchat_3.5\u0026quot;,\n apply_lora=True,\n lora_rank=8,\n lora_alpha=32,\n lora_dropout=0.05,\n raw_lora_target_modules=\u0026quot;all\u0026quot;,\n # Names of modules to apply LoRA. A comma-separated string, for example: \u0026quot;k,q,v\u0026quot; or \u0026quot;all\u0026quot;.\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"openchat/openchat_3.5\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eapply_lora\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elora_rank\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elora_alpha\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elora_dropout\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.05\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eraw_lora_target_modules\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"all\"\u003c/span\u003e,\n \u003cspan class=\"pl-c\"\u003e# Names of modules to apply LoRA. A comma-separated string, for example: \"k,q,v\" or \"all\".\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n \u003csummary\u003eQLoRA\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eTo train the \u003ccode\u003eQLoRA\u003c/code\u003e model, we need to load the backbone model using \u003ccode\u003ebitsandbytes\u003c/code\u003e library and int4 (or int8) weights.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimple\u003c/h4\u003e\u003ca id=\"user-content-simple-1\" class=\"anchor\" aria-label=\"Permalink: Simple\" href=\"#simple-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\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"config = Config(\n model_name_or_path=\u0026quot;01-ai/Yi-34B\u0026quot;,\n apply_lora=True,\n load_in_4bit=True,\n prepare_model_for_kbit_training=True,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"01-ai/Yi-34B\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eapply_lora\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eload_in_4bit\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eprepare_model_for_kbit_training\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdvanced\u003c/h4\u003e\u003ca id=\"user-content-advanced-1\" class=\"anchor\" aria-label=\"Permalink: Advanced\" href=\"#advanced-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\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"config = Config(\n model_name_or_path=\u0026quot;01-ai/Yi-34B\u0026quot;,\n apply_lora=True,\n load_in_4bit=True,\n prepare_model_for_kbit_training=True,\n llm_int8_threshold=6.0,\n llm_int8_has_fp16_weight=True,\n bnb_4bit_use_double_quant=True,\n bnb_4bit_quant_type=\u0026quot;nf4\u0026quot;,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"01-ai/Yi-34B\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eapply_lora\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eload_in_4bit\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eprepare_model_for_kbit_training\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ellm_int8_threshold\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e6.0\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ellm_int8_has_fp16_weight\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ebnb_4bit_use_double_quant\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ebnb_4bit_quant_type\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"nf4\"\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n \u003csummary\u003ePush checkpoints to the HuggingFace Hub\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eBefore that, you must log in to \u003ccode\u003eHuggingface Hub\u003c/code\u003e or add an \u003ccode\u003eAPI Token\u003c/code\u003e to the environment variables.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"config = Config(\n model_name_or_path=\u0026quot;HuggingFaceH4/zephyr-7b-beta\u0026quot;,\n push_to_hub=True,\n hub_private_repo=True,\n hub_model_id=\u0026quot;BobaZooba/AntModel-7B-XLLM-Demo-LoRA\u0026quot;,\n save_steps=25,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"HuggingFaceH4/zephyr-7b-beta\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003epush_to_hub\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ehub_private_repo\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ehub_model_id\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"BobaZooba/AntModel-7B-XLLM-Demo-LoRA\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esave_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e25\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCheckpoints will be saved locally and in Huggingface Hub each \u003ccode\u003esave_steps\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eIf you train a model with \u003ccode\u003eLoRA\u003c/code\u003e, then only \u003ccode\u003eLoRA\u003c/code\u003e weights will be saved\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n \u003csummary\u003eReport to W\u0026amp;B\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eBefore that, you must log in to \u003ccode\u003eW\u0026amp;B\u003c/code\u003e or add an \u003ccode\u003eAPI Token\u003c/code\u003e to the environment variables.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"config = Config(\n model_name_or_path=\u0026quot;HuggingFaceH4/zephyr-7b-beta\u0026quot;,\n report_to_wandb=True,\n logging_steps=5,\n wandb_project=\u0026quot;xllm-demo\u0026quot;,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"HuggingFaceH4/zephyr-7b-beta\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ereport_to_wandb\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elogging_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ewandb_project\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"xllm-demo\"\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n \u003csummary\u003eGradient checkpointing\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eThis will help to use \u003ccode\u003eless GPU memory\u003c/code\u003e during training, that is, you will be able to learn more than without this\ntechnique. The disadvantages of this technique is slowing down the forward step, that is, \u003ccode\u003eslowing down training\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou will be training larger models (for example 7B in colab), but at the expense of training speed.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"config = Config(\n model_name_or_path=\u0026quot;HuggingFaceH4/zephyr-7b-beta\u0026quot;,\n use_gradient_checkpointing=True,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"HuggingFaceH4/zephyr-7b-beta\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003euse_gradient_checkpointing\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n \u003csummary\u003eFlash Attention 2\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eThis speeds up training and GPU memory consumption, but it does not work with all models and GPUs. You also need to\ninstall \u003ccode\u003eflash-attn\u003c/code\u003e for this. This can be done using:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003epip install \"xllm[train]\"\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"config = Config(\n model_name_or_path=\u0026quot;meta-llama/Llama-2-7b-hf\u0026quot;,\n use_flash_attention_2=True,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"meta-llama/Llama-2-7b-hf\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003euse_flash_attention_2\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n \u003csummary\u003e\u003cb\u003eRecommended setup\u003c/b\u003e\u003c/summary\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRecommendations\u003c/h4\u003e\u003ca id=\"user-content-recommendations\" class=\"anchor\" aria-label=\"Permalink: Recommendations\" href=\"#recommendations\"\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\u003eIncredibly effective method is LoRA (\u003ccode\u003eapply_lora\u003c/code\u003e). It allows for a tremendous reduction in training costs\nand, moreover, helps very effectively combat catastrophic forgetting.\u003c/li\u003e\n\u003cli\u003eThen, I advise using \u003ccode\u003eload_in_4bit\u003c/code\u003e and \u003ccode\u003eprepare_model_for_kbit_training\u003c/code\u003e together. This also significantly reduces\nmemory consumption.\u003c/li\u003e\n\u003cli\u003eLastly, I would recommend apply \u003ccode\u003euse_gradient_checkpointing\u003c/code\u003e. This method also greatly reduces memory consumption, but\nat the expense of slowing down training.\u003c/li\u003e\n\u003cli\u003eIf your project allows, I suggest enabling \u003ccode\u003epush_to_hub\u003c/code\u003e and \u003ccode\u003ehub_private_repo\u003c/code\u003e, also specifying the model name\nin \u003ccode\u003ehub_model_id\u003c/code\u003e and \u003ccode\u003esave_steps\u003c/code\u003e. Example: \"BobaZooba/SupaDupaLlama-7B-LoRA\". During training, every checkpoint of\nyour model will be saved in the HuggingFace Hub. If you specified \u003ccode\u003eapply_lora\u003c/code\u003e, then only the LoRA weights will be\nsaved, which you can later easily fuse with the main model, for example, using \u003ccode\u003exllm\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf your GPU allows it add \u003ccode\u003euse_flash_attention_2\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eI also recommend using \u003ccode\u003ereport_to_wandb\u003c/code\u003e, also specifying \u003ccode\u003ewandb_project\u003c/code\u003e (the project name in W\u0026amp;B)\nand \u003ccode\u003ewandb_entity\u003c/code\u003e (user or organization name in W\u0026amp;B).\u003c/li\u003e\n\u003cli\u003eNote that for \u003ccode\u003epush_to_hub\u003c/code\u003e, you need to log in to the HuggingFace Hub beforehand or specify the\ntoken (\u003ccode\u003eHUGGING_FACE_HUB_TOKEN\u003c/code\u003e) in the .env file. Similarly, when using \u003ccode\u003ereport_to_wandb\u003c/code\u003e, you will need to log in to\nW\u0026amp;B. You can either specify the token (\u003ccode\u003eWANDB_API_KEY\u003c/code\u003e) in the .env file or you will be prompted to enter the token on\nthe command line.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFeatures\u003c/h4\u003e\u003ca id=\"user-content-features-1\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features-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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eQLoRA\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eGradient checkpointing\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eFlash Attention 2\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eStabilize training\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003ePush checkpoints to HuggingFace Hub\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eW\u0026amp;B report\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"config = Config(\n model_name_or_path=\u0026quot;meta-llama/Llama-2-7b-hf\u0026quot;,\n tokenizer_padding_side=\u0026quot;right\u0026quot;, # good for llama2\n\n warmup_steps=1000,\n max_steps=10000,\n logging_steps=1,\n save_steps=1000,\n per_device_train_batch_size=2,\n gradient_accumulation_steps=2,\n max_length=2048,\n\n stabilize=True,\n use_flash_attention_2=True,\n\n apply_lora=True,\n\n load_in_4bit=True,\n prepare_model_for_kbit_training=True,\n\n use_gradient_checkpointing=True,\n\n push_to_hub=False,\n hub_private_repo=True,\n hub_model_id=\u0026quot;BobaZooba/SupaDupaLlama-7B-LoRA\u0026quot;,\n\n report_to_wandb=False,\n wandb_project=\u0026quot;xllm-demo\u0026quot;,\n wandb_entity=\u0026quot;bobazooba\u0026quot;,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"meta-llama/Llama-2-7b-hf\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etokenizer_padding_side\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"right\"\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# good for llama2\u003c/span\u003e\n\n \u003cspan class=\"pl-s1\"\u003ewarmup_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10000\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elogging_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esave_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eper_device_train_batch_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003egradient_accumulation_steps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_length\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2048\u003c/span\u003e,\n\n \u003cspan class=\"pl-s1\"\u003estabilize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003euse_flash_attention_2\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n\n \u003cspan class=\"pl-s1\"\u003eapply_lora\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n\n \u003cspan class=\"pl-s1\"\u003eload_in_4bit\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eprepare_model_for_kbit_training\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n\n \u003cspan class=\"pl-s1\"\u003euse_gradient_checkpointing\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n\n \u003cspan class=\"pl-s1\"\u003epush_to_hub\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ehub_private_repo\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ehub_model_id\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"BobaZooba/SupaDupaLlama-7B-LoRA\"\u003c/span\u003e,\n\n \u003cspan class=\"pl-s1\"\u003ereport_to_wandb\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ewandb_project\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"xllm-demo\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ewandb_entity\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"bobazooba\"\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n \u003csummary\u003eFuse\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eThis operation is only for models with a LoRA adapter.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can explicitly specify to fuse the model after training.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"config = Config(\n model_name_or_path=\u0026quot;HuggingFaceH4/zephyr-7b-beta\u0026quot;,\n apply_lora=True,\n fuse_after_training=True,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"HuggingFaceH4/zephyr-7b-beta\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eapply_lora\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003efuse_after_training\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eEven when you are using QLoRa\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"config = Config(\n model_name_or_path=\u0026quot;HuggingFaceH4/zephyr-7b-beta\u0026quot;,\n apply_lora=True,\n load_in_4bit=True,\n prepare_model_for_kbit_training=True,\n fuse_after_training=True,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eConfig\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003emodel_name_or_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"HuggingFaceH4/zephyr-7b-beta\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eapply_lora\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eload_in_4bit\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eprepare_model_for_kbit_training\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003efuse_after_training\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOr you can fuse the model yourself after training.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"experiment.fuse_lora()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003eexperiment\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efuse_lora\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n \u003csummary\u003eDeepSpeed\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eDeepSpeed\u003c/code\u003e is needed for training models on \u003ccode\u003emultiple GPUs\u003c/code\u003e. \u003ccode\u003eDeepSpeed\u003c/code\u003e allows you\nto \u003ccode\u003eefficiently manage the resources of several GPUs during training\u003c/code\u003e. For example, you\ncan \u003ccode\u003edistribute the gradients and the state of the optimizer to several GPUs\u003c/code\u003e, rather than storing a complete set of\ngradients and the state of the optimizer on each GPU. Starting training using \u003ccode\u003eDeepSpeed\u003c/code\u003e can only happen from\nthe \u003ccode\u003ecommand line\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003etrain.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from xllm import Config\nfrom xllm.datasets import GeneralDataset\nfrom xllm.cli import cli_run_train\n\nif __name__ == '__main__':\n train_data = [\u0026quot;Hello!\u0026quot;] * 100\n train_dataset = GeneralDataset.from_list(data=train_data)\n cli_run_train(config_cls=Config, train_dataset=train_dataset)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGeneralDataset\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecli\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecli_run_train\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e__name__\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'__main__'\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s\"\u003e\"Hello!\"\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGeneralDataset\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_list\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e)\n \u003cspan class=\"pl-en\"\u003ecli_run_train\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econfig_cls\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eRun train (in the \u003ccode\u003enum_gpus\u003c/code\u003e parameter, specify as many GPUs as you have)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"deepspeed --num_gpus=8 train.py --deepspeed_stage 2\"\u003e\u003cpre\u003edeepspeed --num_gpus=8 train.py --deepspeed_stage 2\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou also can pass other parameters\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"deepspeed --num_gpus=8 train.py \\\n --deepspeed_stage 2 \\\n --apply_lora True \\\n --stabilize True \\\n --use_gradient_checkpointing True\"\u003e\u003cpre\u003edeepspeed --num_gpus=8 train.py \\\n --deepspeed_stage 2 \\\n --apply_lora True \\\n --stabilize True \\\n --use_gradient_checkpointing True\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNotebooks\u003c/h3\u003e\u003ca id=\"user-content-notebooks\" class=\"anchor\" aria-label=\"Permalink: Notebooks\" href=\"#notebooks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eComment\u003c/th\u003e\n\u003cth\u003eLink\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eX—LLM Prototyping\u003c/td\u003e\n\u003ctd\u003eIn this notebook you will learn the basics of the library\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://colab.research.google.com/drive/1zsNmJFns1PKZy5VE5p5nsQL-mZF7SwHf?usp=sharing\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"xllm_prototyping\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLlama2 \u0026amp; Mistral AI efficient fine-tuning\u003c/td\u003e\n\u003ctd\u003e7B model training in colab using QLoRA, bnb int4, gradient checkpointing and X—LLM\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://colab.research.google.com/drive/1CNNB_HPhQ8g7piosdehqWlgA30xoLauP?usp=sharing\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Llama2MistalAI\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eProduction solution 🚀\u003c/h2\u003e\u003ca id=\"user-content-production-solution-\" class=\"anchor\" aria-label=\"Permalink: Production solution 🚀\" href=\"#production-solution-\"\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\u003ccode\u003eX—LLM\u003c/code\u003e enables not only to prototype models, but also facilitates the development of production-ready solutions through\nbuilt-in capabilities and customization.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsing \u003ccode\u003eX—LLM\u003c/code\u003e to train a model is easy and involves these few steps:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003ePrepare\u003c/code\u003e — Get the data and the model ready by downloading and preparing them. Saves data locally\nto \u003ccode\u003econfig.train_local_path_to_data\u003c/code\u003e and \u003ccode\u003econfig.eval_local_path_to_data\u003c/code\u003e if you are using eval dataset\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eTrain\u003c/code\u003e — Use the data prepared in the previous step to train the model\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eFuse\u003c/code\u003e — If you used LoRA during the training, fuse LoRA\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eQuantize\u003c/code\u003e — Optimize your model's memory usage by quantizing it\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eRemember, these tasks in \u003ccode\u003eX—LLM\u003c/code\u003e start from the command line. So, when you're all set to go, launching your full project\nwill look something like this:\u003c/p\u003e\n\u003cdetails\u003e\n \u003csummary\u003eExample how to run your project\u003c/summary\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eDownloading and preparing data and model\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python3 MY_PROJECT/cli/prepare.py \\\n --dataset_key MY_DATASET \\\n --model_name_or_path mistralai/Mistral-7B-v0.1 \\\n --path_to_env_file ./.env\"\u003e\u003cpre\u003epython3 MY_PROJECT/cli/prepare.py \\\n --dataset_key MY_DATASET \\\n --model_name_or_path mistralai/Mistral-7B-v0.1 \\\n --path_to_env_file ./.env\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003eRun train using DeepSpeed on multiple GPUs\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"deepspeed --num_gpus=8 MY_PROJECT/cli/train.py \\\n --use_gradient_checkpointing True \\\n --deepspeed_stage 2 \\\n --stabilize True \\\n --model_name_or_path mistralai/Mistral-7B-v0.1 \\\n --use_flash_attention_2 False \\\n --load_in_4bit True \\\n --apply_lora True \\\n --raw_lora_target_modules all \\\n --per_device_train_batch_size 8 \\\n --warmup_steps 1000 \\\n --save_total_limit 0 \\\n --push_to_hub True \\\n --hub_model_id MY_HF_HUB_NAME/LORA_MODEL_NAME \\\n --hub_private_repo True \\\n --report_to_wandb True \\\n --path_to_env_file ./.env\"\u003e\u003cpre\u003edeepspeed --num_gpus=8 MY_PROJECT/cli/train.py \\\n --use_gradient_checkpointing True \\\n --deepspeed_stage 2 \\\n --stabilize True \\\n --model_name_or_path mistralai/Mistral-7B-v0.1 \\\n --use_flash_attention_2 False \\\n --load_in_4bit True \\\n --apply_lora True \\\n --raw_lora_target_modules all \\\n --per_device_train_batch_size 8 \\\n --warmup_steps 1000 \\\n --save_total_limit 0 \\\n --push_to_hub True \\\n --hub_model_id MY_HF_HUB_NAME/LORA_MODEL_NAME \\\n --hub_private_repo True \\\n --report_to_wandb True \\\n --path_to_env_file ./.env\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\" dir=\"auto\"\u003e\n\u003cli\u003eFuse LoRA\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python3 MY_PROJECT/cli/fuse.py \\\n --model_name_or_path mistralai/Mistral-7B-v0.1 \\\n --lora_hub_model_id MY_HF_HUB_NAME/LORA_MODEL_NAME \\\n --hub_model_id MY_HF_HUB_NAME/MODEL_NAME \\\n --hub_private_repo True \\\n --force_fp16 True \\\n --fused_model_local_path ./fused_model/ \\\n --path_to_env_file ./.env\"\u003e\u003cpre\u003epython3 MY_PROJECT/cli/fuse.py \\\n --model_name_or_path mistralai/Mistral-7B-v0.1 \\\n --lora_hub_model_id MY_HF_HUB_NAME/LORA_MODEL_NAME \\\n --hub_model_id MY_HF_HUB_NAME/MODEL_NAME \\\n --hub_private_repo True \\\n --force_fp16 True \\\n --fused_model_local_path ./fused_model/ \\\n --path_to_env_file ./.env\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\" dir=\"auto\"\u003e\n\u003cli\u003e[Optional] GPTQ quantization of the trained model with fused LoRA\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python3 MY_PROJECT/cli/quantize.py \\\n --model_name_or_path ./fused_model/ \\\n --apply_lora False \\\n --stabilize False \\\n --quantization_max_samples 128 \\\n --quantized_model_path ./quantized_model/ \\\n --prepare_model_for_kbit_training False \\\n --quantized_hub_model_id MY_HF_HUB_NAME/MODEL_NAME_GPTQ \\\n --quantized_hub_private_repo True \\\n --path_to_env_file ./.env\"\u003e\u003cpre\u003epython3 MY_PROJECT/cli/quantize.py \\\n --model_name_or_path ./fused_model/ \\\n --apply_lora False \\\n --stabilize False \\\n --quantization_max_samples 128 \\\n --quantized_model_path ./quantized_model/ \\\n --prepare_model_for_kbit_training False \\\n --quantized_hub_model_id MY_HF_HUB_NAME/MODEL_NAME_GPTQ \\\n --quantized_hub_private_repo True \\\n --path_to_env_file ./.env\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cp dir=\"auto\"\u003eRight now, the \u003ccode\u003eX—LLM\u003c/code\u003e library lets you use only the \u003ca href=\"https://huggingface.co/datasets/allenai/soda\" rel=\"nofollow\"\u003eSODA dataset\u003c/a\u003e. We've\nset it up this way for demo purposes, but we're planning to add more datasets soon. You'll need to figure out how to\ndownload and handle your dataset. Simply put, you take care of your data, and \u003ccode\u003eX—LLM\u003c/code\u003e handles the rest. We've done it\nthis\nway on purpose, to give you plenty of room to get creative and customize to your heart's content.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can customize your dataset in detail, adding additional fields. All of this will enable you to implement virtually\nany task in the areas of \u003ccode\u003eSupervised Learning\u003c/code\u003e and \u003ccode\u003eOffline Reinforcement Learning\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAt the same time, you always have an easy way to submit data for language modeling.\u003c/p\u003e\n\u003cdetails\u003e\n \u003csummary\u003eExample\u003c/summary\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from xllm import Config\nfrom xllm.datasets import GeneralDataset\nfrom xllm.cli import cli_run_train\n\nif __name__ == '__main__':\n train_data = [\u0026quot;Hello!\u0026quot;] * 100\n train_dataset = GeneralDataset.from_list(data=train_data)\n cli_run_train(config_cls=Config, train_dataset=train_dataset)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGeneralDataset\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecli\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecli_run_train\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e__name__\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'__main__'\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s\"\u003e\"Hello!\"\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGeneralDataset\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_list\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e)\n \u003cspan class=\"pl-en\"\u003ecli_run_train\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econfig_cls\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuild your own project\u003c/h3\u003e\u003ca id=\"user-content-build-your-own-project\" class=\"anchor\" aria-label=\"Permalink: Build your own project\" href=\"#build-your-own-project\"\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\"\u003eTo set up your own project using \u003ccode\u003eX—LLM\u003c/code\u003e, you need to do two things:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eImplement your dataset (figure out how to download and handle it)\u003c/li\u003e\n\u003cli\u003eAdd \u003ccode\u003eX—LLM\u003c/code\u003e's command-line tools into your project\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eOnce that's done, your project will be good to go, and you can start running the steps you need (like prepare, train,\nand so on).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo get a handle on building your project with \u003ccode\u003eX—LLM\u003c/code\u003e, check out the materials below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUseful materials\u003c/h2\u003e\u003ca id=\"user-content-useful-materials\" class=\"anchor\" aria-label=\"Permalink: Useful materials\" href=\"#useful-materials\"\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/BobaZooba/xllm/blob/main/GUIDE.md\"\u003eGuide\u003c/a\u003e: here, we go into detail about everything the library can\ndo\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm-demo\"\u003eDemo project\u003c/a\u003e: here's a step-by-step example of how to use \u003ccode\u003eX—LLM\u003c/code\u003e and fit\nit\ninto your own project\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/wgpt\"\u003eWeatherGPT\u003c/a\u003e: this repository features an example of how to utilize the xllm library. Included is a solution for a common type of assessment given to LLM engineers, who typically earn between $120,000 to $140,000 annually\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/shurale\"\u003eShurale\u003c/a\u003e: project with the finetuned 7B Mistal model\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConfig 🔧\u003c/h1\u003e\u003ca id=\"user-content-config-\" class=\"anchor\" aria-label=\"Permalink: Config 🔧\" href=\"#config-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ccode\u003eX—LLM\u003c/code\u003e library uses a single config setup for all steps like preparing, training and the other steps. It's\ndesigned in a way that\nlets you easily understand the available features and what you can adjust. \u003ccode\u003eConfig\u003c/code\u003e has control almost over every\nsingle part of each step. Thanks to the config, you can pick your dataset, set your collator, manage the type of\nquantization during training, decide if you want to use LoRA, if you need to push a checkpoint to the \u003ccode\u003eHuggingFace Hub\u003c/code\u003e,\nand a\nlot more.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eConfig path: \u003ccode\u003esrc.xllm.core.config.Config\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOr\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from xllm import Config\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003exllm\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUseful materials\u003c/h2\u003e\u003ca id=\"user-content-useful-materials-1\" class=\"anchor\" aria-label=\"Permalink: Useful materials\" href=\"#useful-materials-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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm#how-config-controls-xllm\"\u003eImportant config fields for different steps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-do-i-choose-the-methods-for-training\"\u003eHow do I choose the methods for training?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#detailed-config-explanation\"\u003eDetailed description of all config fields\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\"\u003eCustomization options 🛠\u003c/h1\u003e\u003ca id=\"user-content-customization-options-\" class=\"anchor\" aria-label=\"Permalink: Customization options 🛠\" href=\"#customization-options-\"\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 have the flexibility to tweak many aspects of your model's training: data, how data is processed, trainer, config,\nhow the model is loaded, what happens before and after training, and so much more.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe've got ready-to-use components for every part of the \u003ccode\u003exllm\u003c/code\u003e pipeline. You can entirely switch out some components\nlike the dataset, collator, trainer, and experiment.\nFor some components like experiment and config, you have the option to just build on what's already there.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUseful materials\u003c/h3\u003e\u003ca id=\"user-content-useful-materials-2\" class=\"anchor\" aria-label=\"Permalink: Useful materials\" href=\"#useful-materials-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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-implement-dataset\"\u003eHow to implement dataset\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-add-cli-tools-to-your-project\"\u003eHow to add CLI tools to your project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-implement-collator\"\u003eHow to implement collator\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-implement-trainer\"\u003eHow to implement trainer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-implement-experiment\"\u003eHow to implement experiment\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-extend-config\"\u003eHow to extend config\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\"\u003eProjects using X—LLM 🏆\u003c/h1\u003e\u003ca id=\"user-content-projects-using-xllm-\" class=\"anchor\" aria-label=\"Permalink: Projects using X—LLM 🏆\" href=\"#projects-using-xllm-\"\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\"\u003eBuilding something cool with \u003ca href=\"https://github.com/BobaZooba/xllm\"\u003eX—LLM\u003c/a\u003e? Kindly reach out to me\nat \u003ca href=\"mailto:bobazooba@gmail.com\"\u003ebobazooba@gmail.com\u003c/a\u003e. I'd love to hear from you.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHall of Fame\u003c/h2\u003e\u003ca id=\"user-content-hall-of-fame\" class=\"anchor\" aria-label=\"Permalink: Hall of Fame\" href=\"#hall-of-fame\"\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\"\u003eWrite to us so that we can add your project.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/TachyHealth\" rel=\"nofollow\"\u003eTachyHealth/Thealth\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/TachyHealth/Thealth-llama-7b_v1\" rel=\"nofollow\"\u003eTachyHealth/Thealth-llama-7b_v1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/TachyHealth/Thealth-llama-13b\" rel=\"nofollow\"\u003eTachyHealth/Thealth-llama-13b\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/TachyHealth/Thealth-Yi-6b_v1\" rel=\"nofollow\"\u003eTachyHealth/Thealth-Yi-6b_v1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/TachyHealth/Thealth-Yi-6b_v2\" rel=\"nofollow\"\u003eTachyHealth/Thealth-Yi-6b_v2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/TachyHealth/Thealth-zypher-7b_v1\" rel=\"nofollow\"\u003eTachyHealth/Thealth-zypher-7b_v1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/TachyHealth/Thealth-Zypher-7b_v2\" rel=\"nofollow\"\u003eTachyHealth/Thealth-Zypher-7b_v2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/TachyHealth/Thealth-Falcon-7b\" rel=\"nofollow\"\u003eTachyHealth/Thealth-Falcon-7b\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/TachyHealth/Thealth-Mistral-7b\" rel=\"nofollow\"\u003eTachyHealth/Thealth-Mistral-7b\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BobaZooba/shurale\"\u003eShurale\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/BobaZooba/Shurale7b-v1\" rel=\"nofollow\"\u003eShurale7B-v1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/BobaZooba/Shurale7b-v1-GPTQ\" rel=\"nofollow\"\u003eShurale7B-v1-GPTQ\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\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBadge\u003c/h2\u003e\u003ca id=\"user-content-badge\" class=\"anchor\" aria-label=\"Permalink: Badge\" href=\"#badge\"\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\"\u003eConsider adding a badge to your model card.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor Github project:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"[\u0026lt;img src=\u0026quot;https://github.com/BobaZooba/xllm/blob/main/static/images/xllm-badge.png\u0026quot; alt=\u0026quot;Powered by X—LLM\u0026quot; width=\u0026quot;175\u0026quot; height=\u0026quot;32\u0026quot;/\u0026gt;](https://github.com/BobaZooba/xllm)\"\u003e\u003cpre\u003e[\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eimg src\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://github.com/BobaZooba/xllm/blob/main/static/images/xllm-badge.png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e alt\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePowered by X—LLM\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e width\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e175\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e height\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e32\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e](https://github.com/BobaZooba/xllm)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor Hugging Face Hub model card:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"[\u0026lt;img src=\u0026quot;https://cdn-uploads.huggingface.co/production/uploads/6074d5f1134c000d1ae10d42/JudU3rrPP5i87CfwINANO.png\u0026quot; alt=\u0026quot;Powered by X—LLM\u0026quot; width=\u0026quot;175\u0026quot; height=\u0026quot;32\u0026quot;/\u0026gt;](https://github.com/BobaZooba/xllm)\"\u003e\u003cpre\u003e[\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eimg src\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://cdn-uploads.huggingface.co/production/uploads/6074d5f1134c000d1ae10d42/JudU3rrPP5i87CfwINANO.png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e alt\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePowered by X—LLM\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e width\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e175\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e height\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e32\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e](https://github.com/BobaZooba/xllm)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIt will be looks like this:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/BobaZooba/xllm\"\u003e\u003cimg src=\"https://github.com/BobaZooba/xllm/raw/main/static/images/xllm-badge.png\" alt=\"Powered by X—LLM\" width=\"175\" height=\"32\" 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\"\u003eTesting 🧪\u003c/h1\u003e\u003ca id=\"user-content-testing-\" class=\"anchor\" aria-label=\"Permalink: Testing 🧪\" href=\"#testing-\"\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\"\u003eAt the moment, we don't have Continuous Integration tests that utilize a GPU. However, we might develop these kinds of\ntests in the future. It's important to note, though, that this would require investing time into their development, as\nwell as funding for machine maintenance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFuture Work 🔮\u003c/h1\u003e\u003ca id=\"user-content-future-work-\" class=\"anchor\" aria-label=\"Permalink: Future Work 🔮\" href=\"#future-work-\"\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\u003eAdd more tests\u003c/li\u003e\n\u003cli\u003eAdd RunPod deploy function\u003c/li\u003e\n\u003cli\u003eAdd \u003ca href=\"https://arxiv.org/abs/2305.18290\" rel=\"nofollow\"\u003eDPO\u003c/a\u003e components\u003c/li\u003e\n\u003cli\u003eAdd Callbacks to \u003ccode\u003eExperiment\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eGPU CI using RunPod\u003c/li\u003e\n\u003cli\u003eSolve DeepSpeed + push_to_hub checkpoints bug\u003c/li\u003e\n\u003cli\u003eMake DeepSpeed Stage 3 + bitsandbytes works correc\u003c/li\u003e\n\u003cli\u003eAdd multipacking\u003c/li\u003e\n\u003cli\u003eAdd adaptive batch size\u003c/li\u003e\n\u003cli\u003eFix caching in CI\u003c/li\u003e\n\u003cli\u003eAdd sequence bucketing\u003c/li\u003e\n\u003cli\u003eAdd more datasets\u003c/li\u003e\n\u003cli\u003eMaybe add \u003ca href=\"https://github.com/BlackSamorez/tensor_parallel\"\u003etensor_parallel\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"🦖 X—LLM: Cutting Edge \u0026 Easy LLM Finetuning","anchor":"-xllm-cutting-edge--easy-llm-finetuning","htmlText":"🦖 X—LLM: Cutting Edge \u0026amp; Easy LLM Finetuning"},{"level":1,"text":"Why you should use X—LLM 🪄","anchor":"why-you-should-use-xllm-","htmlText":"Why you should use X—LLM 🪄"},{"level":2,"text":"Features","anchor":"features","htmlText":"Features"},{"level":1,"text":"Quickstart 🦖","anchor":"quickstart-","htmlText":"Quickstart 🦖"},{"level":3,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":4,"text":"Training recommended environment","anchor":"training-recommended-environment","htmlText":"Training recommended environment"},{"level":2,"text":"Fast prototyping ⚡","anchor":"fast-prototyping-","htmlText":"Fast prototyping ⚡"},{"level":3,"text":"How Config controls xllm","anchor":"how-config-controls-xllm","htmlText":"How Config controls xllm"},{"level":4,"text":"Simple","anchor":"simple","htmlText":"Simple"},{"level":4,"text":"Advanced","anchor":"advanced","htmlText":"Advanced"},{"level":4,"text":"Simple","anchor":"simple-1","htmlText":"Simple"},{"level":4,"text":"Advanced","anchor":"advanced-1","htmlText":"Advanced"},{"level":4,"text":"Recommendations","anchor":"recommendations","htmlText":"Recommendations"},{"level":4,"text":"Features","anchor":"features-1","htmlText":"Features"},{"level":3,"text":"Notebooks","anchor":"notebooks","htmlText":"Notebooks"},{"level":2,"text":"Production solution 🚀","anchor":"production-solution-","htmlText":"Production solution 🚀"},{"level":3,"text":"Build your own project","anchor":"build-your-own-project","htmlText":"Build your own project"},{"level":2,"text":"Useful materials","anchor":"useful-materials","htmlText":"Useful materials"},{"level":1,"text":"Config 🔧","anchor":"config-","htmlText":"Config 🔧"},{"level":2,"text":"Useful materials","anchor":"useful-materials-1","htmlText":"Useful materials"},{"level":1,"text":"Customization options 🛠","anchor":"customization-options-","htmlText":"Customization options 🛠"},{"level":3,"text":"Useful materials","anchor":"useful-materials-2","htmlText":"Useful materials"},{"level":1,"text":"Projects using X—LLM 🏆","anchor":"projects-using-xllm-","htmlText":"Projects using X—LLM 🏆"},{"level":2,"text":"Hall of Fame","anchor":"hall-of-fame","htmlText":"Hall of Fame"},{"level":2,"text":"Badge","anchor":"badge","htmlText":"Badge"},{"level":1,"text":"Testing 🧪","anchor":"testing-","htmlText":"Testing 🧪"},{"level":1,"text":"Future Work 🔮","anchor":"future-work-","htmlText":"Future Work 🔮"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FBobaZooba%2Fxllm"}},{"displayName":"LICENSE","repoName":"xllm","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%2FBobaZooba%2Fxllm"}}],"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-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":true,"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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X—LLM: Cutting Edge & Easy LLM Finetuning" href="#-xllm-cutting-edge--easy-llm-finetuning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div align="center" dir="auto"> <p dir="auto"><a href="https://github.com/BobaZooba/xllm/actions/workflows/build.yaml"><img src="https://github.com/BobaZooba/xllm/actions/workflows/build.yaml/badge.svg?branch=main" alt="Build" 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style="max-width: 100%;"></a></p> <p dir="auto"><a href="https://discord.gg/5znbxBgwZP" rel="nofollow"><img src="https://camo.githubusercontent.com/ae7281cc58c433672fa3a78740983c6604590eb1b0664f84ff81bd53edc03993/68747470733a2f2f646362616467652e76657263656c2e6170702f6170692f7365727665722f357a6e62784267775a50" alt="" data-canonical-src="https://dcbadge.vercel.app/api/server/5znbxBgwZP" style="max-width: 100%;"></a></p> <p dir="auto">Cutting Edge & Easy LLM Finetuning using the most advanced methods (QLoRA, DeepSpeed, GPTQ, Flash Attention 2, FSDP, etc)</p> <p dir="auto">Developed by <a href="https://t.me/BobaZooba" rel="nofollow">Boris Zubarev</a> | <a href="https://docs.google.com/document/d/1BhFvIHQ1mpm81P-n2A-lhNac-U2wOGc6F2uS9gKvk88/edit?usp=sharing" rel="nofollow">CV</a> | <a href="https://www.linkedin.com/in/boriszubarev/" rel="nofollow">LinkedIn</a> | <a href="mailto:bobazooba@gmail.com">bobazooba@gmail.com</a></p> </div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Why you should use X—LLM 🪄</h1><a id="user-content-why-you-should-use-xllm-" class="anchor" aria-label="Permalink: Why you should use X—LLM 🪄" href="#why-you-should-use-xllm-"><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">Are you using <strong>Large Language Models (LLMs)</strong> for your work and want to train them more efficiently with advanced methods? Wish to focus on the data and improvements rather than repetitive and time-consuming coding for LLM training?</p> <p dir="auto"><strong>X—LLM</strong> is your solution. It's a user-friendly library that streamlines training optimization, so you can <strong>focus on enhancing your models and data</strong>. Equipped with <strong>cutting-edge training techniques</strong>, X—LLM is engineered for efficiency by engineers who understand your needs.</p> <p dir="auto"><strong>X—LLM</strong> is ideal whether you're <strong>gearing up for production</strong> or need a <strong>fast prototyping tool</strong>.</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> <ul dir="auto"> <li>Hassle-free training for Large Language Models</li> <li>Seamless integration of new data and data processing</li> <li>Effortless expansion of the library</li> <li>Speed up your training, while simultaneously reducing model sizes</li> <li>Each checkpoint is saved to the 🤗 HuggingFace Hub</li> <li>Easy-to-use integration with your existing project</li> <li>Customize almost any part of your training with ease</li> <li>Track your training progress using <code>W&B</code></li> <li>Supported many 🤗 Transformers models like <code>Yi-34B</code>, <code>Mistal AI</code>, <code>Llama 2</code>, <code>Zephyr</code>, <code>OpenChat</code>, <code>Falcon</code>, <code>Phi</code>, <code>Qwen</code>, <code>MPT</code> and many more</li> <li>Benefit from cutting-edge advancements in LLM training optimization <ul dir="auto"> <li>QLoRA and fusing</li> <li>Flash Attention 2</li> <li>Gradient checkpointing</li> <li>bitsandbytes</li> <li>GPTQ (including post-training quantization)</li> <li>DeepSpeed</li> <li>FSDP</li> <li>And many more</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Quickstart 🦖</h1><a id="user-content-quickstart-" class="anchor" aria-label="Permalink: Quickstart 🦖" href="#quickstart-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Installation</h3><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"><code>X—LLM</code> is tested on Python 3.8+, PyTorch 2.0.1+ and CUDA 11.8.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install xllm"><pre>pip install xllm</pre></div> <p dir="auto">Version which include <code>deepspeed</code>, <code>flash-attn</code> and <code>auto-gptq</code>:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install "xllm[train]""><pre>pip install <span class="pl-s"><span class="pl-pds">"</span>xllm[train]<span class="pl-pds">"</span></span></pre></div> <p dir="auto">Default <code>xllm</code> version recommended for local development, <code>xllm[train]</code> recommended for training.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Training recommended environment</h4><a id="user-content-training-recommended-environment" class="anchor" aria-label="Permalink: Training recommended environment" href="#training-recommended-environment"><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">CUDA version: <code>11.8</code> Docker: <code>huggingface/transformers-pytorch-gpu:latest</code></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Fast prototyping ⚡</h2><a id="user-content-fast-prototyping-" class="anchor" aria-label="Permalink: Fast prototyping ⚡" href="#fast-prototyping-"><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="from xllm import Config from xllm.datasets import GeneralDataset from xllm.experiments import Experiment # Init Config which controls the internal logic of xllm # QLoRA example config = Config( model_name_or_path="HuggingFaceH4/zephyr-7b-beta", apply_lora=True, load_in_4bit=True, ) # Prepare the data train_data = ["Hello!"] * 100 train_dataset = GeneralDataset.from_list(data=train_data) # Build Experiment from Config: init tokenizer and model, apply LoRA and so on experiment = Experiment(config=config, train_dataset=train_dataset) experiment.build() # Run Experiment (training) experiment.run() # # [Optional] Fuse LoRA layers # experiment.fuse_lora() # [Optional] Or push LoRA weights to HuggingFace Hub experiment.push_to_hub(repo_id="YOUR_NAME/MODEL_NAME")"><pre><span class="pl-k">from</span> <span class="pl-s1">xllm</span> <span class="pl-k">import</span> <span class="pl-v">Config</span> <span class="pl-k">from</span> <span class="pl-s1">xllm</span>.<span class="pl-s1">datasets</span> <span class="pl-k">import</span> <span class="pl-v">GeneralDataset</span> <span class="pl-k">from</span> <span class="pl-s1">xllm</span>.<span class="pl-s1">experiments</span> <span class="pl-k">import</span> <span class="pl-v">Experiment</span> <span class="pl-c"># Init Config which controls the internal logic of xllm</span> <span class="pl-c"># QLoRA example</span> <span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"HuggingFaceH4/zephyr-7b-beta"</span>, <span class="pl-s1">apply_lora</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">load_in_4bit</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, ) <span class="pl-c"># Prepare the data</span> <span class="pl-s1">train_data</span> <span class="pl-c1">=</span> [<span class="pl-s">"Hello!"</span>] <span class="pl-c1">*</span> <span class="pl-c1">100</span> <span class="pl-s1">train_dataset</span> <span class="pl-c1">=</span> <span class="pl-v">GeneralDataset</span>.<span class="pl-c1">from_list</span>(<span class="pl-s1">data</span><span class="pl-c1">=</span><span class="pl-s1">train_data</span>) <span class="pl-c"># Build Experiment from Config: init tokenizer and model, apply LoRA and so on</span> <span class="pl-s1">experiment</span> <span class="pl-c1">=</span> <span class="pl-en">Experiment</span>(<span class="pl-s1">config</span><span class="pl-c1">=</span><span class="pl-s1">config</span>, <span class="pl-s1">train_dataset</span><span class="pl-c1">=</span><span class="pl-s1">train_dataset</span>) <span class="pl-s1">experiment</span>.<span class="pl-c1">build</span>() <span class="pl-c"># Run Experiment (training)</span> <span class="pl-s1">experiment</span>.<span class="pl-c1">run</span>() <span class="pl-c"># # [Optional] Fuse LoRA layers</span> <span class="pl-c"># experiment.fuse_lora()</span> <span class="pl-c"># [Optional] Or push LoRA weights to HuggingFace Hub</span> <span class="pl-s1">experiment</span>.<span class="pl-c1">push_to_hub</span>(<span class="pl-s1">repo_id</span><span class="pl-c1">=</span><span class="pl-s">"YOUR_NAME/MODEL_NAME"</span>)</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How <code>Config</code> controls <code>xllm</code></h3><a id="user-content-how-config-controls-xllm" class="anchor" aria-label="Permalink: How Config controls xllm" href="#how-config-controls-xllm"><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/BobaZooba/xllm/blob/main/GUIDE.md#config">More about config</a></p> <details> <summary>LoRA</summary> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Simple</h4><a id="user-content-simple" class="anchor" aria-label="Permalink: Simple" href="#simple"><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="config = Config( model_name_or_path="openchat/openchat_3.5", apply_lora=True, )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"openchat/openchat_3.5"</span>, <span class="pl-s1">apply_lora</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, )</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Advanced</h4><a id="user-content-advanced" class="anchor" aria-label="Permalink: Advanced" href="#advanced"><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="config = Config( model_name_or_path="openchat/openchat_3.5", apply_lora=True, lora_rank=8, lora_alpha=32, lora_dropout=0.05, raw_lora_target_modules="all", # Names of modules to apply LoRA. A comma-separated string, for example: "k,q,v" or "all". )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"openchat/openchat_3.5"</span>, <span class="pl-s1">apply_lora</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">lora_rank</span><span class="pl-c1">=</span><span class="pl-c1">8</span>, <span class="pl-s1">lora_alpha</span><span class="pl-c1">=</span><span class="pl-c1">32</span>, <span class="pl-s1">lora_dropout</span><span class="pl-c1">=</span><span class="pl-c1">0.05</span>, <span class="pl-s1">raw_lora_target_modules</span><span class="pl-c1">=</span><span class="pl-s">"all"</span>, <span class="pl-c"># Names of modules to apply LoRA. A comma-separated string, for example: "k,q,v" or "all".</span> )</pre></div> </details> <details> <summary>QLoRA</summary> <p dir="auto">To train the <code>QLoRA</code> model, we need to load the backbone model using <code>bitsandbytes</code> library and int4 (or int8) weights.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Simple</h4><a id="user-content-simple-1" class="anchor" aria-label="Permalink: Simple" href="#simple-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> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="config = Config( model_name_or_path="01-ai/Yi-34B", apply_lora=True, load_in_4bit=True, prepare_model_for_kbit_training=True, )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"01-ai/Yi-34B"</span>, <span class="pl-s1">apply_lora</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">load_in_4bit</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">prepare_model_for_kbit_training</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, )</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Advanced</h4><a id="user-content-advanced-1" class="anchor" aria-label="Permalink: Advanced" href="#advanced-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> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="config = Config( model_name_or_path="01-ai/Yi-34B", apply_lora=True, load_in_4bit=True, prepare_model_for_kbit_training=True, llm_int8_threshold=6.0, llm_int8_has_fp16_weight=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"01-ai/Yi-34B"</span>, <span class="pl-s1">apply_lora</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">load_in_4bit</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">prepare_model_for_kbit_training</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">llm_int8_threshold</span><span class="pl-c1">=</span><span class="pl-c1">6.0</span>, <span class="pl-s1">llm_int8_has_fp16_weight</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">bnb_4bit_use_double_quant</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">bnb_4bit_quant_type</span><span class="pl-c1">=</span><span class="pl-s">"nf4"</span>, )</pre></div> </details> <details> <summary>Push checkpoints to the HuggingFace Hub</summary> <p dir="auto">Before that, you must log in to <code>Huggingface Hub</code> or add an <code>API Token</code> to the environment variables.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="config = Config( model_name_or_path="HuggingFaceH4/zephyr-7b-beta", push_to_hub=True, hub_private_repo=True, hub_model_id="BobaZooba/AntModel-7B-XLLM-Demo-LoRA", save_steps=25, )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"HuggingFaceH4/zephyr-7b-beta"</span>, <span class="pl-s1">push_to_hub</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">hub_private_repo</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">hub_model_id</span><span class="pl-c1">=</span><span class="pl-s">"BobaZooba/AntModel-7B-XLLM-Demo-LoRA"</span>, <span class="pl-s1">save_steps</span><span class="pl-c1">=</span><span class="pl-c1">25</span>, )</pre></div> <ul dir="auto"> <li>Checkpoints will be saved locally and in Huggingface Hub each <code>save_steps</code></li> <li>If you train a model with <code>LoRA</code>, then only <code>LoRA</code> weights will be saved</li> </ul> </details> <details> <summary>Report to W&B</summary> <p dir="auto">Before that, you must log in to <code>W&B</code> or add an <code>API Token</code> to the environment variables.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="config = Config( model_name_or_path="HuggingFaceH4/zephyr-7b-beta", report_to_wandb=True, logging_steps=5, wandb_project="xllm-demo", )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"HuggingFaceH4/zephyr-7b-beta"</span>, <span class="pl-s1">report_to_wandb</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">logging_steps</span><span class="pl-c1">=</span><span class="pl-c1">5</span>, <span class="pl-s1">wandb_project</span><span class="pl-c1">=</span><span class="pl-s">"xllm-demo"</span>, )</pre></div> </details> <details> <summary>Gradient checkpointing</summary> <p dir="auto">This will help to use <code>less GPU memory</code> during training, that is, you will be able to learn more than without this technique. The disadvantages of this technique is slowing down the forward step, that is, <code>slowing down training</code>.</p> <p dir="auto">You will be training larger models (for example 7B in colab), but at the expense of training speed.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="config = Config( model_name_or_path="HuggingFaceH4/zephyr-7b-beta", use_gradient_checkpointing=True, )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"HuggingFaceH4/zephyr-7b-beta"</span>, <span class="pl-s1">use_gradient_checkpointing</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, )</pre></div> </details> <details> <summary>Flash Attention 2</summary> <p dir="auto">This speeds up training and GPU memory consumption, but it does not work with all models and GPUs. You also need to install <code>flash-attn</code> for this. This can be done using:</p> <p dir="auto"><code>pip install "xllm[train]"</code></p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="config = Config( model_name_or_path="meta-llama/Llama-2-7b-hf", use_flash_attention_2=True, )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"meta-llama/Llama-2-7b-hf"</span>, <span class="pl-s1">use_flash_attention_2</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, )</pre></div> </details> <details> <summary><b>Recommended setup</b></summary> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Recommendations</h4><a id="user-content-recommendations" class="anchor" aria-label="Permalink: Recommendations" href="#recommendations"><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>Incredibly effective method is LoRA (<code>apply_lora</code>). It allows for a tremendous reduction in training costs and, moreover, helps very effectively combat catastrophic forgetting.</li> <li>Then, I advise using <code>load_in_4bit</code> and <code>prepare_model_for_kbit_training</code> together. This also significantly reduces memory consumption.</li> <li>Lastly, I would recommend apply <code>use_gradient_checkpointing</code>. This method also greatly reduces memory consumption, but at the expense of slowing down training.</li> <li>If your project allows, I suggest enabling <code>push_to_hub</code> and <code>hub_private_repo</code>, also specifying the model name in <code>hub_model_id</code> and <code>save_steps</code>. Example: "BobaZooba/SupaDupaLlama-7B-LoRA". During training, every checkpoint of your model will be saved in the HuggingFace Hub. If you specified <code>apply_lora</code>, then only the LoRA weights will be saved, which you can later easily fuse with the main model, for example, using <code>xllm</code>.</li> <li>If your GPU allows it add <code>use_flash_attention_2</code></li> <li>I also recommend using <code>report_to_wandb</code>, also specifying <code>wandb_project</code> (the project name in W&B) and <code>wandb_entity</code> (user or organization name in W&B).</li> <li>Note that for <code>push_to_hub</code>, you need to log in to the HuggingFace Hub beforehand or specify the token (<code>HUGGING_FACE_HUB_TOKEN</code>) in the .env file. Similarly, when using <code>report_to_wandb</code>, you will need to log in to W&B. You can either specify the token (<code>WANDB_API_KEY</code>) in the .env file or you will be prompted to enter the token on the command line.</li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Features</h4><a id="user-content-features-1" class="anchor" aria-label="Permalink: Features" href="#features-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> <ul dir="auto"> <li> <p dir="auto">QLoRA</p> </li> <li> <p dir="auto">Gradient checkpointing</p> </li> <li> <p dir="auto">Flash Attention 2</p> </li> <li> <p dir="auto">Stabilize training</p> </li> <li> <p dir="auto">Push checkpoints to HuggingFace Hub</p> </li> <li> <p dir="auto">W&B report</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="config = Config( model_name_or_path="meta-llama/Llama-2-7b-hf", tokenizer_padding_side="right", # good for llama2 warmup_steps=1000, max_steps=10000, logging_steps=1, save_steps=1000, per_device_train_batch_size=2, gradient_accumulation_steps=2, max_length=2048, stabilize=True, use_flash_attention_2=True, apply_lora=True, load_in_4bit=True, prepare_model_for_kbit_training=True, use_gradient_checkpointing=True, push_to_hub=False, hub_private_repo=True, hub_model_id="BobaZooba/SupaDupaLlama-7B-LoRA", report_to_wandb=False, wandb_project="xllm-demo", wandb_entity="bobazooba", )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"meta-llama/Llama-2-7b-hf"</span>, <span class="pl-s1">tokenizer_padding_side</span><span class="pl-c1">=</span><span class="pl-s">"right"</span>, <span class="pl-c"># good for llama2</span> <span class="pl-s1">warmup_steps</span><span class="pl-c1">=</span><span class="pl-c1">1000</span>, <span class="pl-s1">max_steps</span><span class="pl-c1">=</span><span class="pl-c1">10000</span>, <span class="pl-s1">logging_steps</span><span class="pl-c1">=</span><span class="pl-c1">1</span>, <span class="pl-s1">save_steps</span><span class="pl-c1">=</span><span class="pl-c1">1000</span>, <span class="pl-s1">per_device_train_batch_size</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, <span class="pl-s1">gradient_accumulation_steps</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, <span class="pl-s1">max_length</span><span class="pl-c1">=</span><span class="pl-c1">2048</span>, <span class="pl-s1">stabilize</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">use_flash_attention_2</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">apply_lora</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">load_in_4bit</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">prepare_model_for_kbit_training</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">use_gradient_checkpointing</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">push_to_hub</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">hub_private_repo</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">hub_model_id</span><span class="pl-c1">=</span><span class="pl-s">"BobaZooba/SupaDupaLlama-7B-LoRA"</span>, <span class="pl-s1">report_to_wandb</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">wandb_project</span><span class="pl-c1">=</span><span class="pl-s">"xllm-demo"</span>, <span class="pl-s1">wandb_entity</span><span class="pl-c1">=</span><span class="pl-s">"bobazooba"</span>, )</pre></div> </li> </ul> </details> <details> <summary>Fuse</summary> <p dir="auto">This operation is only for models with a LoRA adapter.</p> <p dir="auto">You can explicitly specify to fuse the model after training.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="config = Config( model_name_or_path="HuggingFaceH4/zephyr-7b-beta", apply_lora=True, fuse_after_training=True, )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"HuggingFaceH4/zephyr-7b-beta"</span>, <span class="pl-s1">apply_lora</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">fuse_after_training</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, )</pre></div> <p dir="auto">Even when you are using QLoRa</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="config = Config( model_name_or_path="HuggingFaceH4/zephyr-7b-beta", apply_lora=True, load_in_4bit=True, prepare_model_for_kbit_training=True, fuse_after_training=True, )"><pre><span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-en">Config</span>( <span class="pl-s1">model_name_or_path</span><span class="pl-c1">=</span><span class="pl-s">"HuggingFaceH4/zephyr-7b-beta"</span>, <span class="pl-s1">apply_lora</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">load_in_4bit</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">prepare_model_for_kbit_training</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">fuse_after_training</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, )</pre></div> <p dir="auto">Or you can fuse the model yourself after training.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="experiment.fuse_lora()"><pre><span class="pl-s1">experiment</span>.<span class="pl-c1">fuse_lora</span>()</pre></div> </details> <details> <summary>DeepSpeed</summary> <p dir="auto"><code>DeepSpeed</code> is needed for training models on <code>multiple GPUs</code>. <code>DeepSpeed</code> allows you to <code>efficiently manage the resources of several GPUs during training</code>. For example, you can <code>distribute the gradients and the state of the optimizer to several GPUs</code>, rather than storing a complete set of gradients and the state of the optimizer on each GPU. Starting training using <code>DeepSpeed</code> can only happen from the <code>command line</code>.</p> <p dir="auto"><code>train.py</code></p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from xllm import Config from xllm.datasets import GeneralDataset from xllm.cli import cli_run_train if __name__ == '__main__': train_data = ["Hello!"] * 100 train_dataset = GeneralDataset.from_list(data=train_data) cli_run_train(config_cls=Config, train_dataset=train_dataset)"><pre><span class="pl-k">from</span> <span class="pl-s1">xllm</span> <span class="pl-k">import</span> <span class="pl-v">Config</span> <span class="pl-k">from</span> <span class="pl-s1">xllm</span>.<span class="pl-s1">datasets</span> <span class="pl-k">import</span> <span class="pl-v">GeneralDataset</span> <span class="pl-k">from</span> <span class="pl-s1">xllm</span>.<span class="pl-s1">cli</span> <span class="pl-k">import</span> <span class="pl-s1">cli_run_train</span> <span class="pl-k">if</span> <span class="pl-s1">__name__</span> <span class="pl-c1">==</span> <span class="pl-s">'__main__'</span>: <span class="pl-s1">train_data</span> <span class="pl-c1">=</span> [<span class="pl-s">"Hello!"</span>] <span class="pl-c1">*</span> <span class="pl-c1">100</span> <span class="pl-s1">train_dataset</span> <span class="pl-c1">=</span> <span class="pl-v">GeneralDataset</span>.<span class="pl-c1">from_list</span>(<span class="pl-s1">data</span><span class="pl-c1">=</span><span class="pl-s1">train_data</span>) <span class="pl-en">cli_run_train</span>(<span class="pl-s1">config_cls</span><span class="pl-c1">=</span><span class="pl-v">Config</span>, <span class="pl-s1">train_dataset</span><span class="pl-c1">=</span><span class="pl-s1">train_dataset</span>)</pre></div> <p dir="auto">Run train (in the <code>num_gpus</code> parameter, specify as many GPUs as you have)</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="deepspeed --num_gpus=8 train.py --deepspeed_stage 2"><pre>deepspeed --num_gpus=8 train.py --deepspeed_stage 2</pre></div> <p dir="auto">You also can pass other parameters</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="deepspeed --num_gpus=8 train.py \ --deepspeed_stage 2 \ --apply_lora True \ --stabilize True \ --use_gradient_checkpointing True"><pre>deepspeed --num_gpus=8 train.py \ --deepspeed_stage 2 \ --apply_lora True \ --stabilize True \ --use_gradient_checkpointing True</pre></div> </details> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Notebooks</h3><a id="user-content-notebooks" class="anchor" aria-label="Permalink: Notebooks" href="#notebooks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>Name</th> <th>Comment</th> <th>Link</th> </tr> </thead> <tbody> <tr> <td>X—LLM Prototyping</td> <td>In this notebook you will learn the basics of the library</td> <td><a href="https://colab.research.google.com/drive/1zsNmJFns1PKZy5VE5p5nsQL-mZF7SwHf?usp=sharing" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="xllm_prototyping" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> <tr> <td>Llama2 & Mistral AI efficient fine-tuning</td> <td>7B model training in colab using QLoRA, bnb int4, gradient checkpointing and X—LLM</td> <td><a href="https://colab.research.google.com/drive/1CNNB_HPhQ8g7piosdehqWlgA30xoLauP?usp=sharing" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Llama2MistalAI" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Production solution 🚀</h2><a id="user-content-production-solution-" class="anchor" aria-label="Permalink: Production solution 🚀" href="#production-solution-"><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"><code>X—LLM</code> enables not only to prototype models, but also facilitates the development of production-ready solutions through built-in capabilities and customization.</p> <p dir="auto">Using <code>X—LLM</code> to train a model is easy and involves these few steps:</p> <ol dir="auto"> <li><code>Prepare</code> — Get the data and the model ready by downloading and preparing them. Saves data locally to <code>config.train_local_path_to_data</code> and <code>config.eval_local_path_to_data</code> if you are using eval dataset</li> <li><code>Train</code> — Use the data prepared in the previous step to train the model</li> <li><code>Fuse</code> — If you used LoRA during the training, fuse LoRA</li> <li><code>Quantize</code> — Optimize your model's memory usage by quantizing it</li> </ol> <p dir="auto">Remember, these tasks in <code>X—LLM</code> start from the command line. So, when you're all set to go, launching your full project will look something like this:</p> <details> <summary>Example how to run your project</summary> <ol dir="auto"> <li>Downloading and preparing data and model</li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python3 MY_PROJECT/cli/prepare.py \ --dataset_key MY_DATASET \ --model_name_or_path mistralai/Mistral-7B-v0.1 \ --path_to_env_file ./.env"><pre>python3 MY_PROJECT/cli/prepare.py \ --dataset_key MY_DATASET \ --model_name_or_path mistralai/Mistral-7B-v0.1 \ --path_to_env_file ./.env</pre></div> <ol start="2" dir="auto"> <li>Run train using DeepSpeed on multiple GPUs</li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="deepspeed --num_gpus=8 MY_PROJECT/cli/train.py \ --use_gradient_checkpointing True \ --deepspeed_stage 2 \ --stabilize True \ --model_name_or_path mistralai/Mistral-7B-v0.1 \ --use_flash_attention_2 False \ --load_in_4bit True \ --apply_lora True \ --raw_lora_target_modules all \ --per_device_train_batch_size 8 \ --warmup_steps 1000 \ --save_total_limit 0 \ --push_to_hub True \ --hub_model_id MY_HF_HUB_NAME/LORA_MODEL_NAME \ --hub_private_repo True \ --report_to_wandb True \ --path_to_env_file ./.env"><pre>deepspeed --num_gpus=8 MY_PROJECT/cli/train.py \ --use_gradient_checkpointing True \ --deepspeed_stage 2 \ --stabilize True \ --model_name_or_path mistralai/Mistral-7B-v0.1 \ --use_flash_attention_2 False \ --load_in_4bit True \ --apply_lora True \ --raw_lora_target_modules all \ --per_device_train_batch_size 8 \ --warmup_steps 1000 \ --save_total_limit 0 \ --push_to_hub True \ --hub_model_id MY_HF_HUB_NAME/LORA_MODEL_NAME \ --hub_private_repo True \ --report_to_wandb True \ --path_to_env_file ./.env</pre></div> <ol start="3" dir="auto"> <li>Fuse LoRA</li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python3 MY_PROJECT/cli/fuse.py \ --model_name_or_path mistralai/Mistral-7B-v0.1 \ --lora_hub_model_id MY_HF_HUB_NAME/LORA_MODEL_NAME \ --hub_model_id MY_HF_HUB_NAME/MODEL_NAME \ --hub_private_repo True \ --force_fp16 True \ --fused_model_local_path ./fused_model/ \ --path_to_env_file ./.env"><pre>python3 MY_PROJECT/cli/fuse.py \ --model_name_or_path mistralai/Mistral-7B-v0.1 \ --lora_hub_model_id MY_HF_HUB_NAME/LORA_MODEL_NAME \ --hub_model_id MY_HF_HUB_NAME/MODEL_NAME \ --hub_private_repo True \ --force_fp16 True \ --fused_model_local_path ./fused_model/ \ --path_to_env_file ./.env</pre></div> <ol start="4" dir="auto"> <li>[Optional] GPTQ quantization of the trained model with fused LoRA</li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python3 MY_PROJECT/cli/quantize.py \ --model_name_or_path ./fused_model/ \ --apply_lora False \ --stabilize False \ --quantization_max_samples 128 \ --quantized_model_path ./quantized_model/ \ --prepare_model_for_kbit_training False \ --quantized_hub_model_id MY_HF_HUB_NAME/MODEL_NAME_GPTQ \ --quantized_hub_private_repo True \ --path_to_env_file ./.env"><pre>python3 MY_PROJECT/cli/quantize.py \ --model_name_or_path ./fused_model/ \ --apply_lora False \ --stabilize False \ --quantization_max_samples 128 \ --quantized_model_path ./quantized_model/ \ --prepare_model_for_kbit_training False \ --quantized_hub_model_id MY_HF_HUB_NAME/MODEL_NAME_GPTQ \ --quantized_hub_private_repo True \ --path_to_env_file ./.env</pre></div> </details> <p dir="auto">Right now, the <code>X—LLM</code> library lets you use only the <a href="https://huggingface.co/datasets/allenai/soda" rel="nofollow">SODA dataset</a>. We've set it up this way for demo purposes, but we're planning to add more datasets soon. You'll need to figure out how to download and handle your dataset. Simply put, you take care of your data, and <code>X—LLM</code> handles the rest. We've done it this way on purpose, to give you plenty of room to get creative and customize to your heart's content.</p> <p dir="auto">You can customize your dataset in detail, adding additional fields. All of this will enable you to implement virtually any task in the areas of <code>Supervised Learning</code> and <code>Offline Reinforcement Learning</code>.</p> <p dir="auto">At the same time, you always have an easy way to submit data for language modeling.</p> <details> <summary>Example</summary> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from xllm import Config from xllm.datasets import GeneralDataset from xllm.cli import cli_run_train if __name__ == '__main__': train_data = ["Hello!"] * 100 train_dataset = GeneralDataset.from_list(data=train_data) cli_run_train(config_cls=Config, train_dataset=train_dataset)"><pre><span class="pl-k">from</span> <span class="pl-s1">xllm</span> <span class="pl-k">import</span> <span class="pl-v">Config</span> <span class="pl-k">from</span> <span class="pl-s1">xllm</span>.<span class="pl-s1">datasets</span> <span class="pl-k">import</span> <span class="pl-v">GeneralDataset</span> <span class="pl-k">from</span> <span class="pl-s1">xllm</span>.<span class="pl-s1">cli</span> <span class="pl-k">import</span> <span class="pl-s1">cli_run_train</span> <span class="pl-k">if</span> <span class="pl-s1">__name__</span> <span class="pl-c1">==</span> <span class="pl-s">'__main__'</span>: <span class="pl-s1">train_data</span> <span class="pl-c1">=</span> [<span class="pl-s">"Hello!"</span>] <span class="pl-c1">*</span> <span class="pl-c1">100</span> <span class="pl-s1">train_dataset</span> <span class="pl-c1">=</span> <span class="pl-v">GeneralDataset</span>.<span class="pl-c1">from_list</span>(<span class="pl-s1">data</span><span class="pl-c1">=</span><span class="pl-s1">train_data</span>) <span class="pl-en">cli_run_train</span>(<span class="pl-s1">config_cls</span><span class="pl-c1">=</span><span class="pl-v">Config</span>, <span class="pl-s1">train_dataset</span><span class="pl-c1">=</span><span class="pl-s1">train_dataset</span>)</pre></div> </details> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Build your own project</h3><a id="user-content-build-your-own-project" class="anchor" aria-label="Permalink: Build your own project" href="#build-your-own-project"><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">To set up your own project using <code>X—LLM</code>, you need to do two things:</p> <ol dir="auto"> <li>Implement your dataset (figure out how to download and handle it)</li> <li>Add <code>X—LLM</code>'s command-line tools into your project</li> </ol> <p dir="auto">Once that's done, your project will be good to go, and you can start running the steps you need (like prepare, train, and so on).</p> <p dir="auto">To get a handle on building your project with <code>X—LLM</code>, check out the materials below.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Useful materials</h2><a id="user-content-useful-materials" class="anchor" aria-label="Permalink: Useful materials" href="#useful-materials"><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/BobaZooba/xllm/blob/main/GUIDE.md">Guide</a>: here, we go into detail about everything the library can do</li> <li><a href="https://github.com/BobaZooba/xllm-demo">Demo project</a>: here's a step-by-step example of how to use <code>X—LLM</code> and fit it into your own project</li> <li><a href="https://github.com/BobaZooba/wgpt">WeatherGPT</a>: this repository features an example of how to utilize the xllm library. Included is a solution for a common type of assessment given to LLM engineers, who typically earn between $120,000 to $140,000 annually</li> <li><a href="https://github.com/BobaZooba/shurale">Shurale</a>: project with the finetuned 7B Mistal model</li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Config 🔧</h1><a id="user-content-config-" class="anchor" aria-label="Permalink: Config 🔧" href="#config-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The <code>X—LLM</code> library uses a single config setup for all steps like preparing, training and the other steps. It's designed in a way that lets you easily understand the available features and what you can adjust. <code>Config</code> has control almost over every single part of each step. Thanks to the config, you can pick your dataset, set your collator, manage the type of quantization during training, decide if you want to use LoRA, if you need to push a checkpoint to the <code>HuggingFace Hub</code>, and a lot more.</p> <p dir="auto">Config path: <code>src.xllm.core.config.Config</code></p> <p dir="auto">Or</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from xllm import Config"><pre><span class="pl-k">from</span> <span class="pl-s1">xllm</span> <span class="pl-k">import</span> <span class="pl-v">Config</span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Useful materials</h2><a id="user-content-useful-materials-1" class="anchor" aria-label="Permalink: Useful materials" href="#useful-materials-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> <ul dir="auto"> <li><a href="https://github.com/BobaZooba/xllm#how-config-controls-xllm">Important config fields for different steps</a></li> <li><a href="https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-do-i-choose-the-methods-for-training">How do I choose the methods for training?</a></li> <li><a href="https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#detailed-config-explanation">Detailed description of all config fields</a></li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Customization options 🛠</h1><a id="user-content-customization-options-" class="anchor" aria-label="Permalink: Customization options 🛠" href="#customization-options-"><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 have the flexibility to tweak many aspects of your model's training: data, how data is processed, trainer, config, how the model is loaded, what happens before and after training, and so much more.</p> <p dir="auto">We've got ready-to-use components for every part of the <code>xllm</code> pipeline. You can entirely switch out some components like the dataset, collator, trainer, and experiment. For some components like experiment and config, you have the option to just build on what's already there.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Useful materials</h3><a id="user-content-useful-materials-2" class="anchor" aria-label="Permalink: Useful materials" href="#useful-materials-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> <ul dir="auto"> <li><a href="https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-implement-dataset">How to implement dataset</a></li> <li><a href="https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-add-cli-tools-to-your-project">How to add CLI tools to your project</a></li> <li><a href="https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-implement-collator">How to implement collator</a></li> <li><a href="https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-implement-trainer">How to implement trainer</a></li> <li><a href="https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-implement-experiment">How to implement experiment</a></li> <li><a href="https://github.com/BobaZooba/xllm/blob/main/GUIDE.md#how-to-extend-config">How to extend config</a></li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Projects using X—LLM 🏆</h1><a id="user-content-projects-using-xllm-" class="anchor" aria-label="Permalink: Projects using X—LLM 🏆" href="#projects-using-xllm-"><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">Building something cool with <a href="https://github.com/BobaZooba/xllm">X—LLM</a>? Kindly reach out to me at <a href="mailto:bobazooba@gmail.com">bobazooba@gmail.com</a>. I'd love to hear from you.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Hall of Fame</h2><a id="user-content-hall-of-fame" class="anchor" aria-label="Permalink: Hall of Fame" href="#hall-of-fame"><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">Write to us so that we can add your project.</p> <ul dir="auto"> <li><a href="https://huggingface.co/TachyHealth" rel="nofollow">TachyHealth/Thealth</a> <ul dir="auto"> <li><a href="https://huggingface.co/TachyHealth/Thealth-llama-7b_v1" rel="nofollow">TachyHealth/Thealth-llama-7b_v1</a></li> <li><a href="https://huggingface.co/TachyHealth/Thealth-llama-13b" rel="nofollow">TachyHealth/Thealth-llama-13b</a></li> <li><a href="https://huggingface.co/TachyHealth/Thealth-Yi-6b_v1" rel="nofollow">TachyHealth/Thealth-Yi-6b_v1</a></li> <li><a href="https://huggingface.co/TachyHealth/Thealth-Yi-6b_v2" rel="nofollow">TachyHealth/Thealth-Yi-6b_v2</a></li> <li><a href="https://huggingface.co/TachyHealth/Thealth-zypher-7b_v1" rel="nofollow">TachyHealth/Thealth-zypher-7b_v1</a></li> <li><a href="https://huggingface.co/TachyHealth/Thealth-Zypher-7b_v2" rel="nofollow">TachyHealth/Thealth-Zypher-7b_v2</a></li> <li><a href="https://huggingface.co/TachyHealth/Thealth-Falcon-7b" rel="nofollow">TachyHealth/Thealth-Falcon-7b</a></li> <li><a href="https://huggingface.co/TachyHealth/Thealth-Mistral-7b" rel="nofollow">TachyHealth/Thealth-Mistral-7b</a></li> </ul> </li> <li><a href="https://github.com/BobaZooba/shurale">Shurale</a> <ul dir="auto"> <li><a href="https://huggingface.co/BobaZooba/Shurale7b-v1" rel="nofollow">Shurale7B-v1</a></li> <li><a href="https://huggingface.co/BobaZooba/Shurale7b-v1-GPTQ" rel="nofollow">Shurale7B-v1-GPTQ</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Badge</h2><a id="user-content-badge" class="anchor" aria-label="Permalink: Badge" href="#badge"><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">Consider adding a badge to your model card.</p> <p dir="auto">For Github project:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="[<img src="https://github.com/BobaZooba/xllm/blob/main/static/images/xllm-badge.png" alt="Powered by X—LLM" width="175" height="32"/>](https://github.com/BobaZooba/xllm)"><pre>[<span class="pl-k"><</span>img src<span class="pl-k">=</span><span class="pl-s"><span class="pl-pds">"</span>https://github.com/BobaZooba/xllm/blob/main/static/images/xllm-badge.png<span class="pl-pds">"</span></span> alt<span class="pl-k">=</span><span class="pl-s"><span class="pl-pds">"</span>Powered by X—LLM<span class="pl-pds">"</span></span> width<span class="pl-k">=</span><span class="pl-s"><span class="pl-pds">"</span>175<span class="pl-pds">"</span></span> height<span class="pl-k">=</span><span class="pl-s"><span class="pl-pds">"</span>32<span class="pl-pds">"</span></span>/<span class="pl-k">></span>](https://github.com/BobaZooba/xllm)</pre></div> <p dir="auto">For Hugging Face Hub model card:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="[<img src="https://cdn-uploads.huggingface.co/production/uploads/6074d5f1134c000d1ae10d42/JudU3rrPP5i87CfwINANO.png" alt="Powered by X—LLM" width="175" height="32"/>](https://github.com/BobaZooba/xllm)"><pre>[<span class="pl-k"><</span>img src<span class="pl-k">=</span><span class="pl-s"><span class="pl-pds">"</span>https://cdn-uploads.huggingface.co/production/uploads/6074d5f1134c000d1ae10d42/JudU3rrPP5i87CfwINANO.png<span class="pl-pds">"</span></span> alt<span class="pl-k">=</span><span class="pl-s"><span class="pl-pds">"</span>Powered by X—LLM<span class="pl-pds">"</span></span> width<span class="pl-k">=</span><span class="pl-s"><span class="pl-pds">"</span>175<span class="pl-pds">"</span></span> height<span class="pl-k">=</span><span class="pl-s"><span class="pl-pds">"</span>32<span class="pl-pds">"</span></span>/<span class="pl-k">></span>](https://github.com/BobaZooba/xllm)</pre></div> <p dir="auto">It will be looks like this:</p> <p dir="auto"><a href="https://github.com/BobaZooba/xllm"><img src="https://github.com/BobaZooba/xllm/raw/main/static/images/xllm-badge.png" alt="Powered by X—LLM" width="175" height="32" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Testing 🧪</h1><a id="user-content-testing-" class="anchor" aria-label="Permalink: Testing 🧪" href="#testing-"><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">At the moment, we don't have Continuous Integration tests that utilize a GPU. However, we might develop these kinds of tests in the future. It's important to note, though, that this would require investing time into their development, as well as funding for machine maintenance.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Future Work 🔮</h1><a id="user-content-future-work-" class="anchor" aria-label="Permalink: Future Work 🔮" href="#future-work-"><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>Add more tests</li> <li>Add RunPod deploy function</li> <li>Add <a href="https://arxiv.org/abs/2305.18290" rel="nofollow">DPO</a> components</li> <li>Add Callbacks to <code>Experiment</code></li> <li>GPU CI using RunPod</li> <li>Solve DeepSpeed + push_to_hub checkpoints bug</li> <li>Make DeepSpeed Stage 3 + bitsandbytes works correc</li> <li>Add multipacking</li> <li>Add adaptive batch size</li> <li>Fix caching in CI</li> <li>Add sequence bucketing</li> <li>Add more datasets</li> <li>Maybe add <a href="https://github.com/BlackSamorez/tensor_parallel">tensor_parallel</a></li> </ul> </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="MT+TyzZPsvNc6x94/4v7KWL1EjaRYrwLI2OBFE1NLRskcVcO7XDzUqWz+PRYn5tP+H9GGREhxxKBpnyLJ29fJw==" /> </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"> 🦖 X—LLM: Cutting Edge & Easy LLM Finetuning </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <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> <span class="flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a title="https://t.me/talequestbot" role="link" target="_blank" rel="noopener noreferrer nofollow" class="text-bold" href="https://t.me/talequestbot">t.me/talequestbot</a> </span> </div> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/deep-neural-networks" title="Topic: deep-neural-networks" data-view-component="true" class="topic-tag topic-tag-link"> deep-neural-networks </a> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/torch" title="Topic: torch" data-view-component="true" class="topic-tag topic-tag-link"> torch </a> <a href="/topics/pytorch" title="Topic: pytorch" data-view-component="true" class="topic-tag topic-tag-link"> pytorch </a> <a href="/topics/openai" title="Topic: openai" data-view-component="true" class="topic-tag topic-tag-link"> openai </a> <a href="/topics/llama" title="Topic: llama" data-view-component="true" class="topic-tag topic-tag-link"> llama </a> <a href="/topics/gpt" title="Topic: gpt" data-view-component="true" class="topic-tag topic-tag-link"> gpt </a> <a href="/topics/alpaca" title="Topic: alpaca" data-view-component="true" class="topic-tag topic-tag-link"> alpaca </a> <a href="/topics/zephyr" title="Topic: zephyr" data-view-component="true" class="topic-tag topic-tag-link"> zephyr </a> <a href="/topics/mistral" title="Topic: mistral" data-view-component="true" class="topic-tag topic-tag-link"> mistral </a> <a href="/topics/vicuna" title="Topic: vicuna" data-view-component="true" class="topic-tag topic-tag-link"> vicuna </a> <a href="/topics/gpt-4" title="Topic: gpt-4" data-view-component="true" class="topic-tag topic-tag-link"> gpt-4 </a> <a href="/topics/large-language-models" title="Topic: large-language-models" data-view-component="true" class="topic-tag topic-tag-link"> large-language-models </a> <a href="/topics/llm" title="Topic: llm" data-view-component="true" class="topic-tag topic-tag-link"> llm </a> <a href="/topics/chatgpt" title="Topic: chatgpt" data-view-component="true" class="topic-tag topic-tag-link"> chatgpt </a> <a href="/topics/cerebras" title="Topic: cerebras" data-view-component="true" class="topic-tag topic-tag-link"> cerebras </a> <a href="/topics/gptq" title="Topic: gptq" data-view-component="true" class="topic-tag topic-tag-link"> gptq </a> <a href="/topics/bitsandbytes" title="Topic: bitsandbytes" data-view-component="true" class="topic-tag topic-tag-link"> bitsandbytes </a> <a href="/topics/llama2" title="Topic: llama2" data-view-component="true" class="topic-tag topic-tag-link"> llama2 </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:readme"}" href="#readme-ov-file"> <svg 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class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>401</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/bobazooba/xllm/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 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data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>