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README.md
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README.md
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<div align="center">
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<img src="./images/logo.png"/>
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<hr>
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<img src="https://visitor-badge.laobi.icu/badge?page_id=jingyaogong/minimind"/>
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<img src="https://img.shields.io/github/stars/jingyaogong/minimind?style=social"/>
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<img src="https://img.shields.io/github/license/jingyaogong/minimind"/>
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<img src="https://img.shields.io/github/last-commit/jingyaogong/minimind"/>
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<img src="https://img.shields.io/badge/PRs-welcome-blue"/>
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<img src="https://img.shields.io/badge/🤗-MiniMind%20%20Collection-blue">
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<hr>
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</div>
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<div align="center">
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<h3>"The Greatest Path is the Simplest"</h3>
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<a href="https://github.com/jingyaogong/minimind">🔗MiniMind Github Repo🔗</a>
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</div>
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<hr>
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* This open-source project aims to train a tiny language model called **MiniMind** from scratch in just 3 hours, with a model size of only 26.88M.
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* **MiniMind** is extremely lightweight, with the smallest version being approximately $\frac{1}{7000}$ the size of GPT3, making it possible for even an ordinary personal GPU to perform quick inference and even training.
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* **MiniMind** provides the full-stage code for a simplified large model structure, dataset cleaning and preprocessing, supervised pretraining, supervised instruction fine-tuning (SFT), low-rank adaptation (LoRA) fine-tuning, and direct preference alignment with reinforcement learning without rewards (DPO). It also includes code for expanding to sparse models with mixed experts (MoE) and multi-modal vision language models (VLM): <a href="https://github.com/jingyaogong/minimind-v">MiniMind-V</a>.
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* This is not just an implementation of an open-source model but also a tutorial for getting started with large language models (LLM).
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* We hope this project will serve as an introductory example for researchers, helping them quickly get started and inspiring more exploration and innovation in the LLM field.
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> To avoid misinterpretation, "fastest 3 hours" means you need a machine with hardware configuration superior to mine. Detailed specifications will be provided below.
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<div align="center">
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<hr>
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<img src="./images/streamlit.gif"/>
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<a href="https://www.modelscope.cn/studios/gongjy/minimind">ModelScope Online</a> |
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<a href="https://www.bilibili.com/video/BV12dHPeqE72/?share_source=copy_web&vd_source=670c2504f88726f8cf4a21ef6147c0e8">Bilibili Video Link</a>
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<hr>
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</div>
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>MiniMind Project</title>
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<!-- 引入 Bootstrap CSS -->
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<link href="https://lf6-cdn-tos.bytecdntp.com/cdn/expire-1-M/bootstrap/5.1.3/css/bootstrap.min.css" rel="stylesheet">
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<!-- 可选: 引入自定义样式 -->
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<style>
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.logo {
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max-width: 100%;
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height: auto;
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}
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.scroll-container {
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display: flex;
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overflow-x: auto; /* 启用水平滚动 */
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width: 100%;
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padding: 20px 10px 1px 10px; /* 内边距 */
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box-shadow: 0 4px 8px rgba(0, 0, 0, 0.5); /* 阴影效果 */
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border-radius: 20px 20px 5px 5px; /* 圆角 */
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}
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.scroll-container img {
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width: auto; /* 图片最大宽度为100%，以适应容器 */
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height: 500px; /* 维持图片的宽高比 */
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margin: 0 10px; /* 图片之间有间距 */
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object-fit: contain; /* 确保图片不被拉伸且完全显示 */
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}
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/* 自定义滚动条整体部分 */
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.scroll-container::-webkit-scrollbar {
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width: 10px; /* 滚动条宽度 */
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height: 10px; /* 对于水平滚动条有效 */
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}
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/* 自定义滚动条轨道 */
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.scroll-container::-webkit-scrollbar-track {
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background: rgba(0, 0, 0, 0.1); /* 轨道背景颜色 */
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border-radius: 60px; /* 轨道圆角 */
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}
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/* 自定义滚动条滑块 */
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.scroll-container::-webkit-scrollbar-thumb {
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background: rgba(0, 0, 0, 0.1); /* 滑块颜色 */
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border-radius: 10px; /* 滑块圆角 */
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}
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/* 当鼠标悬停在滑块上时的样式 */
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.scroll-container::-webkit-scrollbar-thumb:hover {
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background: rgba(0, 0, 0, 0.3); /* 悬停时的颜色 */
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}
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/* 对于Firefox浏览器（如果需要支持） */
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@-moz-document url-prefix() {
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.scroll-container {
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scrollbar-width: thin; /* "auto"、"thin" 或 "none" */
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scrollbar-color: rgba(0, 0, 0, 0.3) rgba(0, 0, 0, 0.1); /* thumb 和 track 颜色 */
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}
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}
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</style>
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</head>
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<body>
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<div class="container" style="max-width: 1000px;">
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<div align="center">
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<img src="./images/logo.png" alt="Logo" class="logo mb-4"/>
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<!--<br>-->
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<img src="https://visitor-badge.laobi.icu/badge?page_id=jingyaogong/minimind" alt="Visitor Badge" class="me-2">
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<img src="https://img.shields.io/github/stars/jingyaogong/minimind?style=social" alt="GitHub Stars"
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class="me-2">
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<img src="https://img.shields.io/github/license/jingyaogong/minimind" alt="License" class="me-2">
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<img src="https://img.shields.io/github/last-commit/jingyaogong/minimind" alt="Last Commit" class="me-2">
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<img src="https://img.shields.io/badge/PRs-welcome-blue" alt="Pull Requests Welcome" class="me-2">
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<img src="https://img.shields.io/badge/🤗-MiniMind%20%20Collection-blue" alt="MiniMind Collection" class="me-2">
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</div>
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<div align="center">
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<br/>
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<p style="font-size: 4vh;font-weight: bold;">"The Greatest Path is the Simplest"</p>
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<a href="https://github.com/jingyaogong/minimind">🔗 MiniMind Github Repo 🔗</a>
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</div>
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<hr>
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<ul>
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<li>This open-source project aims to train a tiny language model called <strong>MiniMind</strong> from scratch
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in just 3 hours, with a model size of only 26.88M.
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</li>
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<li><strong>MiniMind</strong> is extremely lightweight, with the smallest version being approximately <span
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style="white-space: nowrap;">1/7000</span> the size of GPT3, making it possible for even an ordinary
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personal GPU to perform quick inference and even training.
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</li>
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<li><strong>MiniMind</strong> provides the full-stage code for a simplified large model structure, dataset
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cleaning and preprocessing, supervised pretraining, supervised instruction fine-tuning (SFT), low-rank
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adaptation (LoRA) fine-tuning, and direct preference alignment with reinforcement learning without rewards
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(DPO). It also includes code for expanding to sparse models with mixed experts (MoE) and multi-modal vision
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language models (VLM): <a href="https://github.com/jingyaogong/minimind-v">MiniMind-V</a>.
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</li>
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<li>This is not just an implementation of an open-source model but also a tutorial for getting started with
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large language models (LLM).
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</li>
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<li>We hope this project will serve as an introductory example for researchers, helping them quickly get started
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and inspiring more exploration and innovation in the LLM field.
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</li>
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<li style="color: #aaa;">To avoid misinterpretation, "fastest 3 hours" means you need a machine with hardware
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configuration superior
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to mine. Detailed specifications will be provided below.
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</li>
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</ul>
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<div align="center">
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<div class="scroll-container">
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<img src="./images/streamlit.gif" alt="Streamlit Demo" class="img-fluid mb-4">
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<img src="./images/LLM-structure.png" alt="LLM Structure" class="img-fluid mb-4">
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<img src="./images/LLM-structure-moe.png" alt="LLM Structure MOE" class="img-fluid mb-4">
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</div>
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<br/>
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<a href="https://www.modelscope.cn/studios/gongjy/minimind">ModelScope Online</a> |
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<a href="https://www.bilibili.com/video/BV12dHPeqE72/?share_source=copy_web&vd_source=670c2504f88726f8cf4a21ef6147c0e8">Bilibili
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Video Link</a>
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<br/>
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<br/>
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</div>
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</div>
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<!-- 引入 Bootstrap JS 和 Popper.js（用于一些组件） -->
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<script src="https://lf6-cdn-tos.bytecdntp.com/cdn/expire-1-M/popper.js/2.11.2/cjs/popper.min.js"></script>
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<script src="https://lf6-cdn-tos.bytecdntp.com/cdn/expire-1-M/bootstrap/5.1.3/js/bootstrap.min.js"></script>
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</body>
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</html>
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