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# Happy LLM <div align='center'>
<img src="./images/head.jpg" alt="alt text" width="90%">
<h1>Happy-LLM</h1>
</div>
<div align="center">
<img src="https://img.shields.io/github/stars/datawhalechina/happy-llm?style=for-the-badge&logo=github" alt="GitHub stars"/>
<img src="https://img.shields.io/github/forks/datawhalechina/happy-llm?style=for-the-badge&logo=github" alt="GitHub forks"/>
<img src="https://img.shields.io/github/license/datawhalechina/happy-llm?style=for-the-badge" alt="License"/>
<img src="https://img.shields.io/badge/language-Chinese-brightgreen?style=for-the-badge" alt="Language"/>
</div>
<div align="center">
<h3>📚 从零开始的大语言模型原理与实践教程</h3>
<p><em>深入理解 LLM 核心原理，动手实现你的第一个大模型</em></p>
</div>
---
## 🎯 项目介绍
> &emsp;&emsp;*很多小伙伴在看完 [self-llm 开源大模型食用指南](https://github.com/datawhalechina/self-llm) 后，感觉意犹未尽，想要深入了解大语言模型的原理和训练过程。于是我们决定推出《Happy-LLM》项目，旨在帮助大家深入理解大语言模型的原理和训练过程。* > &emsp;&emsp;*很多小伙伴在看完 [self-llm 开源大模型食用指南](https://github.com/datawhalechina/self-llm) 后，感觉意犹未尽，想要深入了解大语言模型的原理和训练过程。于是我们决定推出《Happy-LLM》项目，旨在帮助大家深入理解大语言模型的原理和训练过程。*
&emsp;&emsp;本项目是一个开源的 LLM 教程，包含了大语言模型的基本原理、训练过程和应用案例。我们将从基础知识开始，逐步深入到大语言模型的核心原理和训练过程，最后介绍一些实际应用案例。通过本项目，你将能够深入理解大语言模型的原理，并能够动手实现一个简单的大语言模型。 &emsp;&emsp;本项目是一个**系统性的 LLM 学习教程**，包含了大语言模型的基本原理、训练过程和应用案例。我们将从基础知识开始，逐步深入到大语言模型的核心原理和训练过程，最后介绍一些实际应用案例。
&emsp;&emsp;本项目内容包括：第一～二章介绍 NLP 的基本概念和大语言模型的基本原理；第三～四章介绍大模型结构及大模型训练流程；第五～六章会带领大家动手搭建一个大模型并完成模型训练；第七章介绍大模型的应用案例，如：RAG，Agent等等。我们会在每一章中提供详细的代码示例和注释，帮助大家理解每一个细节。 ### ✨ 你将收获什么？
## 内容导航 - 🔍 **深入理解** Transformer 架构和注意力机制
- 📚 **掌握** 预训练语言模型的基本原理
- 🧠 **了解** 现有大模型的基本结构
- 🏗️ **动手实现** 一个完整的 LLaMA2 模型
- ⚙️ **掌握训练** 从预训练到微调的全流程
- 🚀 **实战应用** RAG、Agent 等前沿技术
| 章节 | 关键内容 | ## 📖 内容导航
| ------------------------- | ------------------------------------------------------------ |
| [第一章 NLP 基础概念](./docs/chapter1/第一章%20NLP基础概念.md) | 什么是 NLP、NLP 发展历程、NLP 任务、文本表示的发展历程 |
| [第二章 Transformer 架构](./docs/chapter2/第二章%20Transformer架构.md) | 注意力机制、Encoder-Decoder、搭建一个 Transformer |
| [第三章 预训练语言模型](./docs/chapter3/第三章%20预训练语言模型.md) | Encoder-only PLM、Encoder-Decoder PLM、Decoder-Only PLM |
| [第四章 大语言模型](./docs/chapter4/第四章%20大语言模型.md) | 什么是 LLM、如何训练一个 LLM |
| [第五章 动手搭建大模型](./docs/chapter5/第五章%20动手搭建大模型.md) | 动手实现一个 LLaMA2 大模型、训练 Tokenizer、预训练一个小型LLM |
| [第六章 大模型训练实践流程](./docs/chapter6/第六章%20大模型训练流程实践.md) | 模型预训练、模型有监督微调、高效微调 |
| [第七章 大模型应用](./docs/chapter7/第七章%20大模型应用.md) | LLM 的评测、RAG、Agent |
## 致谢 | 章节 | 关键内容 | 状态 |
| --- | --- | --- |
| [第一章 NLP 基础概念](./docs/chapter1/第一章%20NLP基础概念.md) | 什么是 NLP、发展历程、任务分类、文本表示演进 | ✅ |
| [第二章 Transformer 架构](./docs/chapter2/第二章%20Transformer架构.md) | 注意力机制、Encoder-Decoder、手把手搭建 Transformer | ✅ |
| [第三章 预训练语言模型](./docs/chapter3/第三章%20预训练语言模型.md) | Encoder-only、Encoder-Decoder、Decoder-Only 模型对比 | ✅ |
| [第四章 大语言模型](./docs/chapter4/第四章%20大语言模型.md) | LLM 定义、训练策略、涌现能力分析 | ✅ |
| [第五章 动手搭建大模型](./docs/chapter5/第五章%20动手搭建大模型.md) | 实现 LLaMA2、训练 Tokenizer、预训练小型 LLM | ✅ |
| [第六章 大模型训练实践](./docs/chapter6/第六章%20大模型训练流程实践.md) | 预训练、有监督微调、LoRA/QLoRA 高效微调 | ✅ |
| [第七章 大模型应用](./docs/chapter7/第七章%20大模型应用.md) | 模型评测、RAG 检索增强、Agent 智能体 | ✅ |
## 🤝 如何贡献
我们欢迎任何形式的贡献！
- 🐛 **报告 Bug** - 发现问题请提交 Issue
- 💡 **功能建议** - 有好想法就告诉我们
- 📝 **内容完善** - 帮助改进教程内容
- 🔧 **代码优化** - 提交 Pull Request
## 🙏 致谢
### 核心贡献者 ### 核心贡献者
- [宋志学-项目负责人](https://github.com/KMnO4-zx) (Datawhale成员-中国矿业大学(北京)) - [宋志学-项目负责人](https://github.com/KMnO4-zx) (Datawhale成员-中国矿业大学(北京))
- [邹雨衡-项目负责人](https://github.com/logan-zou) (Datawhale成员-对外经济贸易大学) - [邹雨衡-项目负责人](https://github.com/logan-zou) (Datawhale成员-对外经济贸易大学)
### 其他 ### 特别感谢
- 感谢 [@Sm1les](https://github.com/Sm1les) 对本项目的帮助与支持
- 特别感谢[@Sm1les](https://github.com/Sm1les)对本项目的帮助与支持 - 感谢所有为本项目做出贡献的开发者们 ❤️
- 如果有任何想法可以联系我们 DataWhale 也欢迎大家多多提出 issue
- 特别感谢以下为教程做出贡献的同学！
<div align=center style="margin-top: 30px;"> <div align=center style="margin-top: 30px;">
<a href="https://github.com/datawhalechina/happy-llm/graphs/contributors"> <a href="https://github.com/datawhalechina/happy-llm/graphs/contributors">
@@ -37,10 +70,19 @@
</a> </a>
</div> </div>
## 关于我们 ## 关于 Datawhale
&emsp;&emsp;扫描下方二维码关注公众号：Datawhale，获取更多开源项目和最新资讯！
<div align='center'> <div align='center'>
<img src="./images/datawhale.png" alt="alt text" width="30%"> <img src="./images/datawhale.png" alt="Datawhale" width="30%">
<p>扫描二维码关注 Datawhale 公众号，获取更多优质开源内容</p>
</div> </div>
---
<div align="center">
<p>⭐ 如果这个项目对你有帮助，请给我们一个 Star！</p>
</div>
## 📜 开源协议
本作品采用[知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议](http://creativecommons.org/licenses/by-nc-sa/4.0/)进行许可。

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