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GitHub - jzhang38/TinyLlama: The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
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no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 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0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 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2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" 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6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 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menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/jzhang38/TinyLlama","user_id":null}}" data-hydro-click-hmac="26f6b76411c420c48bf51647535f40fe74d4a6bdf6e8784f123864a701f36d3f" data-analytics-event="{"category":"Sign up","action":"click to sign up for account","label":"ref_page:/<user-name>/<repo-name>;ref_cta:Sign up;ref_loc:header logged out"}" > Sign up </a> <button type="button" class="sr-only js-header-menu-focus-trap d-block d-lg-none">Reseting focus</button> </div> </div> </div> </div> </header> <div hidden="hidden" data-view-component="true" class="js-stale-session-flash stale-session-flash flash flash-warn flash-full"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 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class=\"heading-element\" dir=\"auto\"\u003eTinyLlama-1.1B\u003c/h1\u003e\u003ca id=\"user-content-tinyllama-11b\" class=\"anchor\" aria-label=\"Permalink: TinyLlama-1.1B\" href=\"#tinyllama-11b\"\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\"\u003eEnglish | \u003ca href=\"/jzhang38/TinyLlama/blob/main/README_zh-CN.md\"\u003e中文\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://huggingface.co/spaces/TinyLlama/tinyllama-chat\" rel=\"nofollow\"\u003eChat Demo\u003c/a\u003e | \u003ca href=\"https://discord.gg/74Wcx4j5Nb\" rel=\"nofollow\"\u003eDiscord\u003c/a\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe TinyLlama project aims to \u003cstrong\u003epretrain\u003c/strong\u003e a \u003cstrong\u003e1.1B Llama model on 3 trillion tokens\u003c/strong\u003e. With some proper optimization, we can achieve this within a span of \"just\" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/jzhang38/TinyLlama/blob/main/.github/TinyLlama_logo.png\"\u003e\u003cimg src=\"/jzhang38/TinyLlama/raw/main/.github/TinyLlama_logo.png\" width=\"300\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNews\u003c/h4\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-label=\"Permalink: News\" href=\"#news\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e2023-12-18： Add two notes \u003ca href=\"https://whimsical-aphid-86d.notion.site/Release-of-TinyLlama-1-5T-Checkpoints-Postponed-01b266998c1c47f78f5ae1520196d194?pvs=4\" rel=\"nofollow\"\u003e1\u003c/a\u003e, \u003ca href=\"https://whimsical-aphid-86d.notion.site/Latest-Updates-from-TinyLlama-Team-7d30c01fff794da28ccc952f327c8d4f?pvs=4\" rel=\"nofollow\"\u003e2\u003c/a\u003e explaining the changes of training curves, project schedules, and bug fixes.\u003c/li\u003e\n\u003cli\u003e2023-10-03: Add examples in speculative decoding with llama.cpp. Do check out the \u003ca href=\"/jzhang38/TinyLlama/blob/main/speculative_decoding/README.md\"\u003especulative_decoding/README.md\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e2023-10-02: 1. 1T-token checkpoint just dropped. 2. We document \u003cstrong\u003eall\u003c/strong\u003e intermediate checkpoints \u003ca href=\"https://huggingface.co/TinyLlama/tinyLlama-intermediate-checkpoints/tree/step-480k-token-1007B\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e2023-09-28: Add a discord server.\u003c/li\u003e\n\u003cli\u003e2023-09-18: 1. We added a \u003ca href=\"https://huggingface.co/spaces/PY007/TinyLlama-Chat\" rel=\"nofollow\"\u003echat demo\u003c/a\u003e so that you can play with TinyLlama-Chat-V0.1 right away.\u003c/li\u003e\n\u003cli\u003e2023-09-16: 1. We released the intermediate checkpoint trained on 503B tokens. 2. We released a chat model finetuned on OpenAssisant and simple \u003ca href=\"/jzhang38/TinyLlama/blob/main/sft\"\u003efinetuning\u003c/a\u003e scripts is added. 3. More eval benchmarks are added and documented in \u003ca href=\"/jzhang38/TinyLlama/blob/main/EVAL.md\"\u003eEVAL.md\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvaluation\u003c/h4\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can find the evaluation results of TinyLlama in \u003ca href=\"/jzhang38/TinyLlama/blob/main/EVAL.md\"\u003eEVAL.md\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReleases Schedule\u003c/h4\u003e\u003ca id=\"user-content-releases-schedule\" class=\"anchor\" aria-label=\"Permalink: Releases Schedule\" href=\"#releases-schedule\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe will be rolling out intermediate checkpoints following the below schedule.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBase models:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDate\u003c/th\u003e\n\u003cth\u003eHF Checkpoint\u003c/th\u003e\n\u003cth\u003eTokens\u003c/th\u003e\n\u003cth\u003eStep\u003c/th\u003e\n\u003cth\u003eCommonsense Avg\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-09-01\u003c/td\u003e\n\u003ctd\u003ePythia-1.0B\u003c/td\u003e\n\u003ctd\u003e300B\u003c/td\u003e\n\u003ctd\u003e143k\u003c/td\u003e\n\u003ctd\u003e48.30\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-09-04\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b\" rel=\"nofollow\"\u003eTinyLlama-1.1B-intermediate-step-50k-105b\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e105B\u003c/td\u003e\n\u003ctd\u003e50k\u003c/td\u003e\n\u003ctd\u003e46.11\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-09-16\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-240k-503b\" rel=\"nofollow\"\u003eTinyLlama-1.1B-intermediate-step-240k-503b\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e503B\u003c/td\u003e\n\u003ctd\u003e240K\u003c/td\u003e\n\u003ctd\u003e48.28\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-10-01\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-480k-1T\" rel=\"nofollow\"\u003eTinyLlama-1.1B-intermediate-step-480k-1T\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e1T\u003c/td\u003e\n\u003ctd\u003e480k\u003c/td\u003e\n\u003ctd\u003e50.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-11-04\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T\" rel=\"nofollow\"\u003eTinyLlama-1.1B-intermediate-step-715k-1.5T\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e1.5T\u003c/td\u003e\n\u003ctd\u003e715k\u003c/td\u003e\n\u003ctd\u003e51.28\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-11-20\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T\" rel=\"nofollow\"\u003eTinyLlama-1.1B-intermediate-step-955k-2T\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2T\u003c/td\u003e\n\u003ctd\u003e955k\u003c/td\u003e\n\u003ctd\u003e51.64\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-12-11\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T\" rel=\"nofollow\"\u003eTinyLlama-1.1B-intermediate-step-1195k-2.5T\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.5T\u003c/td\u003e\n\u003ctd\u003e1195k\u003c/td\u003e\n\u003ctd\u003e53.86\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-12-28\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\" rel=\"nofollow\"\u003eTinyLlama-1.1B-intermediate-step-1431k-3T\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e3T\u003c/td\u003e\n\u003ctd\u003e1431k\u003c/td\u003e\n\u003ctd\u003e52.99\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eWe are crafting a note offering possible explaination on why there is a significant improvement from 2T to 2.5T checkpoint (It is related to \u003ca href=\"https://github.com/jzhang38/TinyLlama/issues/83\" data-hovercard-type=\"issue\" data-hovercard-url=\"/jzhang38/TinyLlama/issues/83/hovercard\"\u003ebos_id issue\u003c/a\u003e)\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eChat models:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDate\u003c/th\u003e\n\u003cth\u003eHF Checkpoint\u003c/th\u003e\n\u003cth\u003eTokens\u003c/th\u003e\n\u003cth\u003eStep\u003c/th\u003e\n\u003cth\u003eCommonsense Avg\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-09-16\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.1\" rel=\"nofollow\"\u003eTinyLlama-1.1B-Chat-V0.1\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e503B\u003c/td\u003e\n\u003ctd\u003e240K\u003c/td\u003e\n\u003ctd\u003e49.57\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-10-1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3\" rel=\"nofollow\"\u003eTinyLlama-1.1B-Chat-V0.3\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e1T\u003c/td\u003e\n\u003ctd\u003e480K\u003c/td\u003e\n\u003ctd\u003e51.36\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2023-11-04\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4\" rel=\"nofollow\"\u003eTinyLlama-1.1B-Chat-V0.4\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e1.5T\u003c/td\u003e\n\u003ctd\u003e715K\u003c/td\u003e\n\u003ctd\u003e52.30\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eNote that the learning rate of the base model has not cooled down yet so we recommend you to also use the finetuned chat model.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMeanwhile, you can track the live cross entropy loss \u003ca href=\"https://wandb.ai/lance777/lightning_logs/reports/metric-train_loss-23-09-04-23-38-15---Vmlldzo1MzA4MzIw?accessToken=5eu2sndit2mo6eqls8h38sklcgfwt660ek1f2czlgtqjv2c6tida47qm1oty8ik9\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePotential Usecase\u003c/h2\u003e\u003ca id=\"user-content-potential-usecase\" class=\"anchor\" aria-label=\"Permalink: Potential Usecase\" href=\"#potential-usecase\"\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\"\u003eTiny but strong language models are useful for many applications. Here are some potential usecases:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAssisting speculative decoding of larger models. (See this \u003ca href=\"https://twitter.com/karpathy/status/1697318534555336961\" rel=\"nofollow\"\u003etutorial\u003c/a\u003e by Andrej Karpathy)\u003c/li\u003e\n\u003cli\u003eDeployment on edge devices with restricted memory and computational capacities, for functionalities like real-time machine translation without an internet connection (the 4bit-quantized TinyLlama-1.1B's weight only takes up 637 MB).\u003c/li\u003e\n\u003cli\u003eEnabling real-time dialogue generation in video games.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eMoreover, our code can be a \u003cstrong\u003ereference for enthusiasts keen on pretraining language models under 5 billion parameters\u003c/strong\u003e without diving too early into \u003ca href=\"https://github.com/NVIDIA/Megatron-LM\"\u003eMegatron-LM\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining Details\u003c/h2\u003e\u003ca id=\"user-content-training-details\" class=\"anchor\" aria-label=\"Permalink: Training Details\" href=\"#training-details\"\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\"\u003eBelow are some details of our training setup:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSetting\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eParameters\u003c/td\u003e\n\u003ctd\u003e1.1B\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAttention Variant\u003c/td\u003e\n\u003ctd\u003eGrouped Query Attention\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel Size\u003c/td\u003e\n\u003ctd\u003eLayers: 22, Heads: 32, Query Groups: 4, Embedding Size: 2048, Intermediate Size (Swiglu): 5632\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSequence Length\u003c/td\u003e\n\u003ctd\u003e2048\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBatch Size\u003c/td\u003e\n\u003ctd\u003e2 million tokens (2048 * 1024)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLearning Rate\u003c/td\u003e\n\u003ctd\u003e4e-4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLearning Rate Schedule\u003c/td\u003e\n\u003ctd\u003eCosine with 2000 warmup steps. See \u003ca href=\"https://github.com/jzhang38/TinyLlama/issues/27\" data-hovercard-type=\"issue\" data-hovercard-url=\"/jzhang38/TinyLlama/issues/27/hovercard\"\u003eIssue 27\u003c/a\u003e for a minor bug\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTraining Data\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/datasets/cerebras/slimpajama-627b\" rel=\"nofollow\"\u003eSlimpajama\u003c/a\u003e \u0026amp; \u003ca href=\"https://huggingface.co/datasets/bigcode/starcoderdata\" rel=\"nofollow\"\u003eStarcoderdata\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eData Preprocessing\u003c/td\u003e\n\u003ctd\u003eExcluded GitHub subset of Slimpajama; Sampled all code from Starcoderdata\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCombined Dataset Size\u003c/td\u003e\n\u003ctd\u003eAround 950B tokens\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal Tokens During Training\u003c/td\u003e\n\u003ctd\u003e3 trillion (slightly more than 3 epochs/1430k steps)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNatural Language to Code Ratio\u003c/td\u003e\n\u003ctd\u003e7:3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHardware\u003c/td\u003e\n\u003ctd\u003e16 A100-40G GPUs\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\"\u003eBlazingly Fast\u003c/h2\u003e\u003ca id=\"user-content-blazingly-fast\" class=\"anchor\" aria-label=\"Permalink: Blazingly Fast\" href=\"#blazingly-fast\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOur codebase supports the following features:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003emulti-gpu and multi-node distributed training with FSDP.\u003c/li\u003e\n\u003cli\u003eflash attention 2.\u003c/li\u003e\n\u003cli\u003efused layernorm.\u003c/li\u003e\n\u003cli\u003efused swiglu.\u003c/li\u003e\n\u003cli\u003efused cross entropy loss .\u003c/li\u003e\n\u003cli\u003efused rotary positional embedding.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eCredit: flash attention 2, fused layernorm, fused cross entropy loss, and fused\nrotary positional embedding are from the \u003ca href=\"https://github.com/Dao-AILab/flash-attention/\"\u003eFlashAttention repo\u003c/a\u003e. Fused swiglu is from \u003ca href=\"https://github.com/facebookresearch/xformers\"\u003exformers\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThanks to those optimizations, we achieve a throughput of \u003cstrong\u003e24k\u003c/strong\u003e tokens per second per A100-40G GPU, which translates to \u003cstrong\u003e56% model flops utilization\u003c/strong\u003e without activation checkpointing (We expect the MFU to be even higher on A100-80G). It means you can train a chinchilla-optimal TinyLlama (1.1B param, 22B tokens) in \u003cstrong\u003e32 hours with 8 A100\u003c/strong\u003e. Those optimizations also greatly reduce the memory footprint, allowing us to stuff our 1.1B model into 40GB GPU RAM and train with a per-gpu batch size of 16k tokens. \u003cstrong\u003eYou can also pretrain TinyLlama on 3090/4090 GPUs with a smaller per-gpu batch size\u003c/strong\u003e.\nBelow is a comparison of the training speed of our codebase with that of Pythia and MPT.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eModel\u003c/th\u003e\n\u003cth\u003eA100 GPU hours taken on 300B tokens\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eTinyLlama-1.1B\u003c/td\u003e\n\u003ctd\u003e3456\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/EleutherAI/pythia-1b\" rel=\"nofollow\"\u003ePythia-1.0B\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e4830\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/mosaicml/mpt-1b-redpajama-200b\" rel=\"nofollow\"\u003eMPT-1.3B\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e7920\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003e The Pythia number comes from their \u003ca href=\"https://arxiv.org/abs/2304.01373\" rel=\"nofollow\"\u003epaper\u003c/a\u003e. The MPT number comes from \u003ca href=\"https://huggingface.co/mosaicml/mpt-1b-redpajama-200b\" rel=\"nofollow\"\u003ehere\u003c/a\u003e, in which they say MPT-1.3B \" was trained on 440 A100-40GBs for about half a day\" on 200B tokens. \u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe fact that TinyLlama is a relatively small model with grouped query attention means it is also fast during inference. Below are some throughputs that we measure:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFramework\u003c/th\u003e\n\u003cth\u003eDevice\u003c/th\u003e\n\u003cth\u003eSettings\u003c/th\u003e\n\u003cth\u003eThroughput (tokens/sec)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ggerganov/llama.cpp\"\u003eLlama.cpp\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMac M2 16GB RAM\u003c/td\u003e\n\u003ctd\u003ebatch_size=1; 4-bit inference\u003c/td\u003e\n\u003ctd\u003e71.8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/vllm-project/vllm\"\u003evLLM\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eA40 GPU\u003c/td\u003e\n\u003ctd\u003ebatch_size=100, n=10\u003c/td\u003e\n\u003ctd\u003e7094.5\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\"\u003ePretrain\u003c/h2\u003e\u003ca id=\"user-content-pretrain\" class=\"anchor\" aria-label=\"Permalink: Pretrain\" href=\"#pretrain\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease refer to \u003ca href=\"/jzhang38/TinyLlama/blob/main/PRETRAIN.md\"\u003ePRETRAIN.md\u003c/a\u003e for instructions on how to pretrain TinyLlama.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFinetune\u003c/h2\u003e\u003ca id=\"user-content-finetune\" class=\"anchor\" aria-label=\"Permalink: Finetune\" href=\"#finetune\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe include a simple full-parameter finetuning \u0026amp; inference script in \u003ca href=\"/jzhang38/TinyLlama/blob/main/sft\"\u003esft\u003c/a\u003e. Our V0.1 chat model is finetuned using this script. The FT dataset we use is \u003ca href=\"https://huggingface.co/datasets/timdettmers/openassistant-guanaco\" rel=\"nofollow\"\u003eopenassistant-guanaco\u003c/a\u003e.\nFor finetuning with less than 4GB RAM, we refer you to the \u003ca href=\"https://github.com/artidoro/qlora\"\u003eQlora\u003c/a\u003e and \u003ca href=\"https://github.com/TimDettmers/bitsandbytes\"\u003ebitsandbytes\u003c/a\u003e repos.\nWe did not undergo extensive hyperparameter tuning nor choose more performant FT datasets. We hope the community can explore on finetuning TinyLlama and come up with better chat models. I will include community-finetuned models in this repo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTODO\u003c/h2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO\" href=\"#todo\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis project is still under active development. We are a really small team. Community feedback and contributions are highly appreciated. Here are some things we plan to work on:\u003c/p\u003e\n\u003cul class=\"contains-task-list\"\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\"\u003e Add scripts for pretraining on other datasets.\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\"\u003e Sequence length extrapolation.\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\"\u003e Test out speculative decoding for Llama-2-7B.\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\"\u003e Test the throughput on RTX 3090/4090.\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\"\u003e Add fine-tuning scripts.\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\"\u003e Properly evaluate the model on downstream tasks.\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\"\u003e A demo running on mobile phones.\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\"\u003e Explore retrieval-augmentation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repository is built upon \u003ca href=\"https://github.com/Lightning-AI/lit-gpt\"\u003elit-gpt\u003c/a\u003e and \u003ca href=\"https://github.com/Dao-AILab/flash-attention\"\u003eflash-attention\u003c/a\u003e. Be sure to explore this fantastic open-source project if it's new to you!\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@online{lit-gpt,\n author = {Lightning AI},\n title = {Lit-GPT},\n url = {https://github.com/Lightning-AI/lit-gpt},\n year = {2023},\n}\n@article{dao2023flashattention2,\n title ={Flash{A}ttention-2: Faster Attention with Better Parallelism and Work Partitioning},\n author ={Dao, Tri},\n year ={2023}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@online{lit-gpt,\n author = {Lightning AI},\n title = {Lit-GPT},\n url = {https://github.com/Lightning-AI/lit-gpt},\n year = {2023},\n}\n@article{dao2023flashattention2,\n title ={Flash{A}ttention-2: Faster Attention with Better Parallelism and Work Partitioning},\n author ={Dao, Tri},\n year ={2023}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis project is currently contributed by \u003ca href=\"https://veiled-texture-20c.notion.site/Peiyuan-Zhang-ab24b48621c9491db767a76df860873a?pvs=4\" rel=\"nofollow\"\u003ePeiyuan Zhang\u003c/a\u003e *, \u003ca href=\"https://github.com/ChaosCodes\"\u003eGuangtao Zeng\u003c/a\u003e *, \u003ca href=\"https://github.com/TianduoWang\"\u003eTianduo Wang\u003c/a\u003e and \u003ca href=\"https://istd.sutd.edu.sg/people/faculty/lu-wei/\" rel=\"nofollow\"\u003eWei Lu\u003c/a\u003e from the StatNLP Research Group of Singapore University of Technology and Design.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you find our work valuable, please cite:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{zhang2024tinyllama,\n title={TinyLlama: An Open-Source Small Language Model}, \n author={Peiyuan Zhang and Guangtao Zeng and Tianduo Wang and Wei Lu},\n year={2024},\n eprint={2401.02385},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{zhang2024tinyllama,\n title={TinyLlama: An Open-Source Small Language Model}, \n author={Peiyuan Zhang and Guangtao Zeng and Tianduo Wang and Wei Lu},\n year={2024},\n eprint={2401.02385},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFrequently Asked Questions\u003c/h2\u003e\u003ca id=\"user-content-frequently-asked-questions\" class=\"anchor\" aria-label=\"Permalink: Frequently Asked Questions\" href=\"#frequently-asked-questions\"\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\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1. Why would pretraining a 1.1B model for so long make sense? Doesn't it contradict the Chinchilla Scaling Law?\u003c/h4\u003e\u003ca id=\"user-content-1-why-would-pretraining-a-11b-model-for-so-long-make-sense-doesnt-it-contradict-the-chinchilla-scaling-law\" class=\"anchor\" aria-label=\"Permalink: 1. Why would pretraining a 1.1B model for so long make sense? Doesn't it contradict the Chinchilla Scaling Law?\" href=\"#1-why-would-pretraining-a-11b-model-for-so-long-make-sense-doesnt-it-contradict-the-chinchilla-scaling-law\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/jzhang38/TinyLlama/blob/main/.github/llama2-training.png\"\u003e\u003cimg src=\"/jzhang38/TinyLlama/raw/main/.github/llama2-training.png\" alt=\"The training loss curve of Llama 2\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAbove is the training loss curve taken from the Llama 2 paper. Here I quote from that paper: \"We observe that after pretraining on 2T Tokens, the models still did not show any sign of saturation\". That is why we believe pretraining a 1.1B model for 3T tokens is a reasonable thing to do. Even if the loss curve does not go down eventually, we can still study the phenomenon of saturation and learn something from it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2. What does \"saturation\" mean?\u003c/h4\u003e\u003ca id=\"user-content-2-what-does-saturation-mean\" class=\"anchor\" aria-label=\"Permalink: 2. What does \u0026quot;saturation\u0026quot; mean?\" href=\"#2-what-does-saturation-mean\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/jzhang38/TinyLlama/blob/main/.github/Pythia_saturation.png\"\u003e\u003cimg src=\"/jzhang38/TinyLlama/raw/main/.github/Pythia_saturation.png\" alt=\"Figure 10 of the Pythia paper\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe figure from the Pythia paper displays the LAMBADA accuracy plotted against the total training tokens (300B). The term \"saturation\" pertains specifically to the 70M and 160M models. Notably, even the 410M model does not saturate with 300B tokens, as it continues to show an increasing trend, similar to the trend of larger models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStar History\u003c/h2\u003e\u003ca id=\"user-content-star-history\" class=\"anchor\" aria-label=\"Permalink: Star History\" href=\"#star-history\"\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://star-history.com/#jzhang38/TinyLlama\u0026amp;Date\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/840e1f3f88273d495957491277c3787e20944a9972807d08e9f5ead97cc534fd/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d6a7a68616e6733382f54696e794c6c616d6126747970653d44617465\" alt=\"Star History Chart\" data-canonical-src=\"https://api.star-history.com/svg?repos=jzhang38/TinyLlama\u0026amp;type=Date\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"TinyLlama-1.1B","anchor":"tinyllama-11b","htmlText":"TinyLlama-1.1B"},{"level":4,"text":"News","anchor":"news","htmlText":"News"},{"level":4,"text":"Evaluation","anchor":"evaluation","htmlText":"Evaluation"},{"level":4,"text":"Releases Schedule","anchor":"releases-schedule","htmlText":"Releases Schedule"},{"level":2,"text":"Potential Usecase","anchor":"potential-usecase","htmlText":"Potential Usecase"},{"level":2,"text":"Training Details","anchor":"training-details","htmlText":"Training Details"},{"level":2,"text":"Blazingly Fast","anchor":"blazingly-fast","htmlText":"Blazingly Fast"},{"level":2,"text":"Pretrain","anchor":"pretrain","htmlText":"Pretrain"},{"level":2,"text":"Finetune","anchor":"finetune","htmlText":"Finetune"},{"level":2,"text":"TODO","anchor":"todo","htmlText":"TODO"},{"level":2,"text":"Acknowledgements","anchor":"acknowledgements","htmlText":"Acknowledgements"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"},{"level":2,"text":"Frequently Asked Questions","anchor":"frequently-asked-questions","htmlText":"Frequently Asked Questions"},{"level":4,"text":"1. Why would pretraining a 1.1B model for so long make sense? Doesn't it contradict the Chinchilla Scaling Law?","anchor":"1-why-would-pretraining-a-11b-model-for-so-long-make-sense-doesnt-it-contradict-the-chinchilla-scaling-law","htmlText":"1. Why would pretraining a 1.1B model for so long make sense? Doesn't it contradict the Chinchilla Scaling Law?"},{"level":4,"text":"2. What does \"saturation\" mean?","anchor":"2-what-does-saturation-mean","htmlText":"2. What does \"saturation\" mean?"},{"level":2,"text":"Star History","anchor":"star-history","htmlText":"Star History"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fjzhang38%2FTinyLlama"}},{"displayName":"LICENSE","repoName":"TinyLlama","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%2Fjzhang38%2FTinyLlama"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> <div data-target="react-partial.reactRoot"><style 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5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div align="center" dir="auto"> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">TinyLlama-1.1B</h1><a id="user-content-tinyllama-11b" class="anchor" aria-label="Permalink: TinyLlama-1.1B" href="#tinyllama-11b"><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">English | <a href="/jzhang38/TinyLlama/blob/main/README_zh-CN.md">中文</a></p> <p dir="auto"><a href="https://huggingface.co/spaces/TinyLlama/tinyllama-chat" rel="nofollow">Chat Demo</a> | <a href="https://discord.gg/74Wcx4j5Nb" rel="nofollow">Discord</a></p> </div> <p dir="auto">The TinyLlama project aims to <strong>pretrain</strong> a <strong>1.1B Llama model on 3 trillion tokens</strong>. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.</p> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/jzhang38/TinyLlama/blob/main/.github/TinyLlama_logo.png"><img src="/jzhang38/TinyLlama/raw/main/.github/TinyLlama_logo.png" width="300" style="max-width: 100%;"></a> </div> <p dir="auto">We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">News</h4><a id="user-content-news" class="anchor" aria-label="Permalink: News" href="#news"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>2023-12-18： Add two notes <a href="https://whimsical-aphid-86d.notion.site/Release-of-TinyLlama-1-5T-Checkpoints-Postponed-01b266998c1c47f78f5ae1520196d194?pvs=4" rel="nofollow">1</a>, <a href="https://whimsical-aphid-86d.notion.site/Latest-Updates-from-TinyLlama-Team-7d30c01fff794da28ccc952f327c8d4f?pvs=4" rel="nofollow">2</a> explaining the changes of training curves, project schedules, and bug fixes.</li> <li>2023-10-03: Add examples in speculative decoding with llama.cpp. Do check out the <a href="/jzhang38/TinyLlama/blob/main/speculative_decoding/README.md">speculative_decoding/README.md</a>.</li> <li>2023-10-02: 1. 1T-token checkpoint just dropped. 2. We document <strong>all</strong> intermediate checkpoints <a href="https://huggingface.co/TinyLlama/tinyLlama-intermediate-checkpoints/tree/step-480k-token-1007B" rel="nofollow">here</a>.</li> <li>2023-09-28: Add a discord server.</li> <li>2023-09-18: 1. We added a <a href="https://huggingface.co/spaces/PY007/TinyLlama-Chat" rel="nofollow">chat demo</a> so that you can play with TinyLlama-Chat-V0.1 right away.</li> <li>2023-09-16: 1. We released the intermediate checkpoint trained on 503B tokens. 2. We released a chat model finetuned on OpenAssisant and simple <a href="/jzhang38/TinyLlama/blob/main/sft">finetuning</a> scripts is added. 3. More eval benchmarks are added and documented in <a href="/jzhang38/TinyLlama/blob/main/EVAL.md">EVAL.md</a>.</li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Evaluation</h4><a id="user-content-evaluation" class="anchor" aria-label="Permalink: Evaluation" href="#evaluation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can find the evaluation results of TinyLlama in <a href="/jzhang38/TinyLlama/blob/main/EVAL.md">EVAL.md</a>.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Releases Schedule</h4><a id="user-content-releases-schedule" class="anchor" aria-label="Permalink: Releases Schedule" href="#releases-schedule"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We will be rolling out intermediate checkpoints following the below schedule.</p> <p dir="auto">Base models:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Date</th> <th>HF Checkpoint</th> <th>Tokens</th> <th>Step</th> <th>Commonsense Avg</th> </tr> </thead> <tbody> <tr> <td>2023-09-01</td> <td>Pythia-1.0B</td> <td>300B</td> <td>143k</td> <td>48.30</td> </tr> <tr> <td>2023-09-04</td> <td><a href="https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b" rel="nofollow">TinyLlama-1.1B-intermediate-step-50k-105b</a></td> <td>105B</td> <td>50k</td> <td>46.11</td> </tr> <tr> <td>2023-09-16</td> <td><a href="https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-240k-503b" rel="nofollow">TinyLlama-1.1B-intermediate-step-240k-503b</a></td> <td>503B</td> <td>240K</td> <td>48.28</td> </tr> <tr> <td>2023-10-01</td> <td><a href="https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-480k-1T" rel="nofollow">TinyLlama-1.1B-intermediate-step-480k-1T</a></td> <td>1T</td> <td>480k</td> <td>50.22</td> </tr> <tr> <td>2023-11-04</td> <td><a href="https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T" rel="nofollow">TinyLlama-1.1B-intermediate-step-715k-1.5T</a></td> <td>1.5T</td> <td>715k</td> <td>51.28</td> </tr> <tr> <td>2023-11-20</td> <td><a href="https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T" rel="nofollow">TinyLlama-1.1B-intermediate-step-955k-2T</a></td> <td>2T</td> <td>955k</td> <td>51.64</td> </tr> <tr> <td>2023-12-11</td> <td><a href="https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T" rel="nofollow">TinyLlama-1.1B-intermediate-step-1195k-2.5T</a></td> <td>2.5T</td> <td>1195k</td> <td>53.86</td> </tr> <tr> <td>2023-12-28</td> <td><a href="https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T" rel="nofollow">TinyLlama-1.1B-intermediate-step-1431k-3T</a></td> <td>3T</td> <td>1431k</td> <td>52.99</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">We are crafting a note offering possible explaination on why there is a significant improvement from 2T to 2.5T checkpoint (It is related to <a href="https://github.com/jzhang38/TinyLlama/issues/83" data-hovercard-type="issue" data-hovercard-url="/jzhang38/TinyLlama/issues/83/hovercard">bos_id issue</a>)</p> <p dir="auto">Chat models:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Date</th> <th>HF Checkpoint</th> <th>Tokens</th> <th>Step</th> <th>Commonsense Avg</th> </tr> </thead> <tbody> <tr> <td>2023-09-16</td> <td><a href="https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.1" rel="nofollow">TinyLlama-1.1B-Chat-V0.1</a></td> <td>503B</td> <td>240K</td> <td>49.57</td> </tr> <tr> <td>2023-10-1</td> <td><a href="https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3" rel="nofollow">TinyLlama-1.1B-Chat-V0.3</a></td> <td>1T</td> <td>480K</td> <td>51.36</td> </tr> <tr> <td>2023-11-04</td> <td><a href="https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4" rel="nofollow">TinyLlama-1.1B-Chat-V0.4</a></td> <td>1.5T</td> <td>715K</td> <td>52.30</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">Note that the learning rate of the base model has not cooled down yet so we recommend you to also use the finetuned chat model.</p> <p dir="auto">Meanwhile, you can track the live cross entropy loss <a href="https://wandb.ai/lance777/lightning_logs/reports/metric-train_loss-23-09-04-23-38-15---Vmlldzo1MzA4MzIw?accessToken=5eu2sndit2mo6eqls8h38sklcgfwt660ek1f2czlgtqjv2c6tida47qm1oty8ik9" rel="nofollow">here</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Potential Usecase</h2><a id="user-content-potential-usecase" class="anchor" aria-label="Permalink: Potential Usecase" href="#potential-usecase"><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">Tiny but strong language models are useful for many applications. Here are some potential usecases:</p> <ul dir="auto"> <li>Assisting speculative decoding of larger models. (See this <a href="https://twitter.com/karpathy/status/1697318534555336961" rel="nofollow">tutorial</a> by Andrej Karpathy)</li> <li>Deployment on edge devices with restricted memory and computational capacities, for functionalities like real-time machine translation without an internet connection (the 4bit-quantized TinyLlama-1.1B's weight only takes up 637 MB).</li> <li>Enabling real-time dialogue generation in video games.</li> </ul> <p dir="auto">Moreover, our code can be a <strong>reference for enthusiasts keen on pretraining language models under 5 billion parameters</strong> without diving too early into <a href="https://github.com/NVIDIA/Megatron-LM">Megatron-LM</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Training Details</h2><a id="user-content-training-details" class="anchor" aria-label="Permalink: Training Details" href="#training-details"><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">Below are some details of our training setup:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Setting</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>Parameters</td> <td>1.1B</td> </tr> <tr> <td>Attention Variant</td> <td>Grouped Query Attention</td> </tr> <tr> <td>Model Size</td> <td>Layers: 22, Heads: 32, Query Groups: 4, Embedding Size: 2048, Intermediate Size (Swiglu): 5632</td> </tr> <tr> <td>Sequence Length</td> <td>2048</td> </tr> <tr> <td>Batch Size</td> <td>2 million tokens (2048 * 1024)</td> </tr> <tr> <td>Learning Rate</td> <td>4e-4</td> </tr> <tr> <td>Learning Rate Schedule</td> <td>Cosine with 2000 warmup steps. See <a href="https://github.com/jzhang38/TinyLlama/issues/27" data-hovercard-type="issue" data-hovercard-url="/jzhang38/TinyLlama/issues/27/hovercard">Issue 27</a> for a minor bug</td> </tr> <tr> <td>Training Data</td> <td><a href="https://huggingface.co/datasets/cerebras/slimpajama-627b" rel="nofollow">Slimpajama</a> & <a href="https://huggingface.co/datasets/bigcode/starcoderdata" rel="nofollow">Starcoderdata</a></td> </tr> <tr> <td>Data Preprocessing</td> <td>Excluded GitHub subset of Slimpajama; Sampled all code from Starcoderdata</td> </tr> <tr> <td>Combined Dataset Size</td> <td>Around 950B tokens</td> </tr> <tr> <td>Total Tokens During Training</td> <td>3 trillion (slightly more than 3 epochs/1430k steps)</td> </tr> <tr> <td>Natural Language to Code Ratio</td> <td>7:3</td> </tr> <tr> <td>Hardware</td> <td>16 A100-40G GPUs</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Blazingly Fast</h2><a id="user-content-blazingly-fast" class="anchor" aria-label="Permalink: Blazingly Fast" href="#blazingly-fast"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Our codebase supports the following features:</p> <ul dir="auto"> <li>multi-gpu and multi-node distributed training with FSDP.</li> <li>flash attention 2.</li> <li>fused layernorm.</li> <li>fused swiglu.</li> <li>fused cross entropy loss .</li> <li>fused rotary positional embedding.</li> </ul> <p dir="auto">Credit: flash attention 2, fused layernorm, fused cross entropy loss, and fused rotary positional embedding are from the <a href="https://github.com/Dao-AILab/flash-attention/">FlashAttention repo</a>. Fused swiglu is from <a href="https://github.com/facebookresearch/xformers">xformers</a>.</p> <p dir="auto">Thanks to those optimizations, we achieve a throughput of <strong>24k</strong> tokens per second per A100-40G GPU, which translates to <strong>56% model flops utilization</strong> without activation checkpointing (We expect the MFU to be even higher on A100-80G). It means you can train a chinchilla-optimal TinyLlama (1.1B param, 22B tokens) in <strong>32 hours with 8 A100</strong>. Those optimizations also greatly reduce the memory footprint, allowing us to stuff our 1.1B model into 40GB GPU RAM and train with a per-gpu batch size of 16k tokens. <strong>You can also pretrain TinyLlama on 3090/4090 GPUs with a smaller per-gpu batch size</strong>. Below is a comparison of the training speed of our codebase with that of Pythia and MPT.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Model</th> <th>A100 GPU hours taken on 300B tokens</th> </tr> </thead> <tbody> <tr> <td>TinyLlama-1.1B</td> <td>3456</td> </tr> <tr> <td><a href="https://huggingface.co/EleutherAI/pythia-1b" rel="nofollow">Pythia-1.0B</a></td> <td>4830</td> </tr> <tr> <td><a href="https://huggingface.co/mosaicml/mpt-1b-redpajama-200b" rel="nofollow">MPT-1.3B</a></td> <td>7920</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto"> The Pythia number comes from their <a href="https://arxiv.org/abs/2304.01373" rel="nofollow">paper</a>. The MPT number comes from <a href="https://huggingface.co/mosaicml/mpt-1b-redpajama-200b" rel="nofollow">here</a>, in which they say MPT-1.3B " was trained on 440 A100-40GBs for about half a day" on 200B tokens. </p> <p dir="auto">The fact that TinyLlama is a relatively small model with grouped query attention means it is also fast during inference. Below are some throughputs that we measure:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Framework</th> <th>Device</th> <th>Settings</th> <th>Throughput (tokens/sec)</th> </tr> </thead> <tbody> <tr> <td><a href="https://github.com/ggerganov/llama.cpp">Llama.cpp</a></td> <td>Mac M2 16GB RAM</td> <td>batch_size=1; 4-bit inference</td> <td>71.8</td> </tr> <tr> <td><a href="https://github.com/vllm-project/vllm">vLLM</a></td> <td>A40 GPU</td> <td>batch_size=100, n=10</td> <td>7094.5</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Pretrain</h2><a id="user-content-pretrain" class="anchor" aria-label="Permalink: Pretrain" href="#pretrain"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please refer to <a href="/jzhang38/TinyLlama/blob/main/PRETRAIN.md">PRETRAIN.md</a> for instructions on how to pretrain TinyLlama.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Finetune</h2><a id="user-content-finetune" class="anchor" aria-label="Permalink: Finetune" href="#finetune"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We include a simple full-parameter finetuning & inference script in <a href="/jzhang38/TinyLlama/blob/main/sft">sft</a>. Our V0.1 chat model is finetuned using this script. The FT dataset we use is <a href="https://huggingface.co/datasets/timdettmers/openassistant-guanaco" rel="nofollow">openassistant-guanaco</a>. For finetuning with less than 4GB RAM, we refer you to the <a href="https://github.com/artidoro/qlora">Qlora</a> and <a href="https://github.com/TimDettmers/bitsandbytes">bitsandbytes</a> repos. We did not undergo extensive hyperparameter tuning nor choose more performant FT datasets. We hope the community can explore on finetuning TinyLlama and come up with better chat models. I will include community-finetuned models in this repo.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">TODO</h2><a id="user-content-todo" class="anchor" aria-label="Permalink: TODO" href="#todo"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This project is still under active development. We are a really small team. Community feedback and contributions are highly appreciated. Here are some things we plan to work on:</p> <ul class="contains-task-list"> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> Add scripts for pretraining on other datasets.</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> Sequence length extrapolation.</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> Test out speculative decoding for Llama-2-7B.</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> Test the throughput on RTX 3090/4090.</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> Add fine-tuning scripts.</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> Properly evaluate the model on downstream tasks.</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> A demo running on mobile phones.</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> Explore retrieval-augmentation.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgements</h2><a id="user-content-acknowledgements" class="anchor" aria-label="Permalink: Acknowledgements" href="#acknowledgements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repository is built upon <a href="https://github.com/Lightning-AI/lit-gpt">lit-gpt</a> and <a href="https://github.com/Dao-AILab/flash-attention">flash-attention</a>. Be sure to explore this fantastic open-source project if it's new to you!</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@online{lit-gpt, author = {Lightning AI}, title = {Lit-GPT}, url = {https://github.com/Lightning-AI/lit-gpt}, year = {2023}, } @article{dao2023flashattention2, title ={Flash{A}ttention-2: Faster Attention with Better Parallelism and Work Partitioning}, author ={Dao, Tri}, year ={2023} }"><pre class="notranslate"><code>@online{lit-gpt, author = {Lightning AI}, title = {Lit-GPT}, url = {https://github.com/Lightning-AI/lit-gpt}, year = {2023}, } @article{dao2023flashattention2, title ={Flash{A}ttention-2: Faster Attention with Better Parallelism and Work Partitioning}, author ={Dao, Tri}, year ={2023} } </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This project is currently contributed by <a href="https://veiled-texture-20c.notion.site/Peiyuan-Zhang-ab24b48621c9491db767a76df860873a?pvs=4" rel="nofollow">Peiyuan Zhang</a> *, <a href="https://github.com/ChaosCodes">Guangtao Zeng</a> *, <a href="https://github.com/TianduoWang">Tianduo Wang</a> and <a href="https://istd.sutd.edu.sg/people/faculty/lu-wei/" rel="nofollow">Wei Lu</a> from the StatNLP Research Group of Singapore University of Technology and Design.</p> <p dir="auto">If you find our work valuable, please cite:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{zhang2024tinyllama, title={TinyLlama: An Open-Source Small Language Model}, author={Peiyuan Zhang and Guangtao Zeng and Tianduo Wang and Wei Lu}, year={2024}, eprint={2401.02385}, archivePrefix={arXiv}, primaryClass={cs.CL} }"><pre class="notranslate"><code>@misc{zhang2024tinyllama, title={TinyLlama: An Open-Source Small Language Model}, author={Peiyuan Zhang and Guangtao Zeng and Tianduo Wang and Wei Lu}, year={2024}, eprint={2401.02385}, archivePrefix={arXiv}, primaryClass={cs.CL} } </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Frequently Asked Questions</h2><a id="user-content-frequently-asked-questions" class="anchor" aria-label="Permalink: Frequently Asked Questions" href="#frequently-asked-questions"><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"><h4 tabindex="-1" class="heading-element" dir="auto">1. Why would pretraining a 1.1B model for so long make sense? Doesn't it contradict the Chinchilla Scaling Law?</h4><a id="user-content-1-why-would-pretraining-a-11b-model-for-so-long-make-sense-doesnt-it-contradict-the-chinchilla-scaling-law" class="anchor" aria-label="Permalink: 1. Why would pretraining a 1.1B model for so long make sense? Doesn't it contradict the Chinchilla Scaling Law?" href="#1-why-would-pretraining-a-11b-model-for-so-long-make-sense-doesnt-it-contradict-the-chinchilla-scaling-law"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/jzhang38/TinyLlama/blob/main/.github/llama2-training.png"><img src="/jzhang38/TinyLlama/raw/main/.github/llama2-training.png" alt="The training loss curve of Llama 2" width="500" style="max-width: 100%;"></a></p> <p dir="auto">Above is the training loss curve taken from the Llama 2 paper. Here I quote from that paper: "We observe that after pretraining on 2T Tokens, the models still did not show any sign of saturation". That is why we believe pretraining a 1.1B model for 3T tokens is a reasonable thing to do. Even if the loss curve does not go down eventually, we can still study the phenomenon of saturation and learn something from it.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">2. What does "saturation" mean?</h4><a id="user-content-2-what-does-saturation-mean" class="anchor" aria-label="Permalink: 2. What does "saturation" mean?" href="#2-what-does-saturation-mean"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/jzhang38/TinyLlama/blob/main/.github/Pythia_saturation.png"><img src="/jzhang38/TinyLlama/raw/main/.github/Pythia_saturation.png" alt="Figure 10 of the Pythia paper" width="500" style="max-width: 100%;"></a></p> <p dir="auto">The figure from the Pythia paper displays the LAMBADA accuracy plotted against the total training tokens (300B). The term "saturation" pertains specifically to the 70M and 160M models. Notably, even the 410M model does not saturate with 300B tokens, as it continues to show an increasing trend, similar to the trend of larger models.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Star History</h2><a id="user-content-star-history" class="anchor" aria-label="Permalink: Star History" href="#star-history"><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 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