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Decision brief
Happy-LLM is a comprehensive guide and resource set designed for users who are aiming to build large-scale models from the ground up using Jupyter Notebooks.
Good fit when
- - When you need detailed, step-by-step guidance on creating large language models with practical examples in Jupyter Notebook.
- - If you're interested in a hands-on approach that starts with foundational concepts and gradually introduces more complex building blocks of LLMs.
Avoid when
- - If your goal is to use pre-existing models without understanding their inner workings; Happy-LLM focuses on teaching the construction process from scratch.
- - For those looking for real-time coding environments or platforms with more interactive user interfaces beyond Jupyter Notebooks, which may offer less of a guided learning experience in return.
- Pricing:
- unknown - Pricing or licensing costs are not specified, and the exact terms of use should be verified directly from the source.
- Requirements:
- - Requires familiarity with Jupyter Notebooks for maximum utility in leveraging resources.; - Intended audience includes beginner to intermediate level model developers who seek a comprehensive learning experience on LLMs.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Install
git clone https://github.com/datawhalechina/happy-llmHow it fits your stack(12)
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Overview
A guide or set of resources aimed at helping users understand and build large-scale models from scratch.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 16, 2026
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README
Happy-LLM
中文 | English
🎯 项目介绍
很多小伙伴在看完 Datawhale开源项目： self-llm 开源大模型食用指南 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（Datawhale）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。
本项目是一个系统性的 LLM 学习教程，将从 NLP 的基本研究方法出发，根据 LLM 的思路及原理逐层深入，依次为读者剖析 LLM 的架构基础和训练过程。同时，我们会结合目前 LLM 领域最主流的代码框架，演练如何亲手搭建、训练一个 LLM，期以实现授之以鱼，更授之以渔。希望大家能从这本书开始走入 LLM 的浩瀚世界，探索 LLM 的无尽可能。
✨ 你将收获什么？
- 📚 Datawhale 开源免费 完全免费的学习本项目所有内容
- 🔍 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
📖 内容导航
| 章节 | 关键内容 | 状态 |
|---|---|---|
| 学习与环境准备 | 分章依赖、硬件建议与实践入口 | ✅ |
| 前言 | 本项目的缘起、背景及读者建议 | ✅ |
| 第一章 NLP 基础概念 | 什么是 NLP、发展历程、任务分类、文本表示演进 | ✅ |
| 第二章 Transformer 架构 | 注意力机制、Encoder-Decoder、手把手搭建 Transformer | ✅ |
| 第三章 预训练语言模型 | Encoder-only、Encoder-Decoder、Decoder-Only 模型对比 | ✅ |
| 第四章 大语言模型 | LLM 定义、训练策略、涌现能力分析 | ✅ |
| 第五章 动手搭建大模型 | 实现 LLaMA2、训练 Tokenizer、预训练小型 LLM | ✅ |
| 第六章 大模型训练实践 | 预训练、有监督微调、LoRA/QLoRA 高效微调 | ✅ |
| 第七章 大模型应用 | 模型评测、RAG 检索增强、Agent 智能体 | ✅ |
| 第八章 Agentic-RL | GRPO、OPD、Search-R1、ReTool（Coding Agent-RL） | ✅ |
| Extra Chapter LLM Blog | 优秀的大模型 学习笔记/Blog ，欢迎大家来 PR ！ | 🚧 |
第六章正文已覆盖 Pretrain、SFT 与 PEFT 等核心训练流程，建议结合 第六章实践说明 和 学习与环境准备 一起阅读。
第八章聚焦 GRPO、OPD、Search-R1 与 ReTool。更多 Agentic RL 算法、训练代码与实验实践，可以前往作者持续维护的另一个仓库 agentic-rl-lab；该仓库更新频率更高，会持续跟进新的算法与环境。
Extra Chapter LLM Blog
-
大模型都这么厉害了，微调0.6B的小模型有什么意义？ @不要葱姜蒜 2025-7-11
-
Transformer 整体模块设计解读 @ditingdapeng 2025-7-14
-
文本数据处理详解 @蔡鋆捷 2025-7-14
-
Qwen3-"VL"——超小中文多模态模型的“拼接微调”之路 @ShaohonChen 2025-7-30
-
S1: Thinking Budget with vLLM @不要葱姜蒜 2025-8-03
-
CDDRS: 使用细粒度语义信息指导增强的RAG检索方法 @[Hongru0306](https://github.com/Hongru0
For agents
This page has a .md twin and JSON over the API.