O’Reilly 爆款神书官方源码库：大语言模型实战全景指南 Hands-On Large Language Models 📚🤖

斩获 30K+ Stars 的现象级开源学习仓库，作为 O’Reilly 权威重磅书籍《Hands-On Large Language Models》的官方配套代码库，全面覆盖从 Transformer 内部结构、Token/Embedding 机制到 Prompt 工程、RAG、多模态以及模型微调的全栈实践

👥 【适用人群】

适合 AI/ML 算法工程师、大模型应用开发者、数据科学家，以及希望通过系统化实操代码深入掌握 LLM 底层原理与工程落地的技术极客与学生

🎬 【典型使用场景】

1️⃣ 从零剥离并通透理解 LLM 底层机制：跟随系统化的 Jupyter Notebook 逐步拆解 Tokenizer、文本 Embedding 以及 Transformer 架构的内部运行逻辑，彻底吃透大模型核心运作机理

2️⃣ 快速动手实践 RAG、微调与多模态开发：无需繁琐配置，直接利用自带的源码在 Google Colab 中一键运行，实战体验语义检索、检索增强生成（RAG）、自定义 Embedding 模型构建、多模态 LLM 及大模型微调全流程

🎯 【核心 3 大杀手级功能与技术栈】

1️⃣ 全景式构建大模型知识图谱：章节划分极度清晰，循序渐进涵盖 Prompt 工程、RAG、语义搜索、多模态模型、Embedding 模型训练以及生成模型微调等核心模块

2️⃣ 零门槛开箱即用的 Colab 交互式体验：所有示例均针对 Google Colab 环境进行优化设计，提供完整的依赖脚本与可直接运行的交互式 Notebook，大幅降低环境配置成本

3️⃣ 权威出版物加持的生产级最佳实践：作为 O’Reilly 官方图书代码，逻辑严密、注释详尽，将前沿理论与产业界落地的工程实践进行了完美结合

🛠️ 【技术栈与语言构成】：完全采用交互直观、易于调试的 Jupyter Notebook (100%) 呈现，深度结合 Python 现代 AI/ML 生态库

⭐ Star 数：约 30k

🔗 开源仓库： https://github.com/HandsOnLLM/Hands-On-Large-Language-Models…

#HandsOnLLM #LargeLanguageModels #RAG #FineTuning #OReilly #JupyterNotebook #OpenSource #开源项目 #大模型 #人工智能

HandsOnLLM/Hands-On-Large-Language-Models

Source: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

Hands-On Large Language Models

Welcome! In this repository you will find the code for all examples throughout the book Hands-On Large Language Models written by Jay Alammar and Maarten Grootendorst which we playfully dubbed:

"The Illustrated LLM Book"

Through the visually educational nature of this book and with almost 300 custom made figures, learn the practical tools and concepts you need to use Large Language Models today!

The book is available on:

• Amazon
• Shroff Publishers (India)
• O’Reilly
• Kindle
• Barnes and Noble
• Goodreads

Table of Contents

We advise to run all examples through Google Colab for the easiest setup. Google Colab allows you to use a T4 GPU with 16GB of VRAM for free. All examples were mainly built and tested using Google Colab, so it should be the most stable platform. However, any other cloud provider should work.

Chapter	Notebook
Chapter 1: Introduction to Language Models
Chapter 2: Tokens and Embeddings
Chapter 3: Looking Inside Transformer LLMs
Chapter 4: Text Classification
Chapter 5: Text Clustering and Topic Modeling
Chapter 6: Prompt Engineering
Chapter 7: Advanced Text Generation Techniques and Tools
Chapter 8: Semantic Search and Retrieval-Augmented Generation
Chapter 9: Multimodal Large Language Models
Chapter 10: Creating Text Embedding Models
Chapter 11: Fine-tuning Representation Models for Classification
Chapter 12: Fine-tuning Generation Models

You can check the setup folder for a quick-start guide to install all packages locally and you can check the conda folder for a complete guide on how to setup your environment, including conda and PyTorch installation. Note that the depending on your OS, Python version, and dependencies your results might be slightly differ. However, they should this be similar to the examples in the book.

Reviews

“Jay and Maarten have continued their tradition of providing beautifully illustrated and insightful descriptions of complex topics in their new book. Bolstered with working code, timelines, and references to key papers, their book is a valuable resource for anyone looking to understand the main techniques behind how Large Language Models are built.”

Andrew Ng - founder of DeepLearning.AI

“This is an exceptional guide to the world of language models and their practical applications in industry. Its highly-visual coverage of generative, representational, and retrieval applications of language models empowers readers to quickly understand, use, and refine LLMs. Highly recommended!”

Nils Reimers - Director of Machine Learning at Cohere | creator of sentence-transformers

“I can’t think of another book that is more important to read right now. On every single page, I learned something that is critical to success in this era of language models.”

Josh Starmer - StatQuest

“If you’re looking to get up to speed in everything regarding LLMs, look no further! In this wonderful book, Jay and Maarten will take you from zero to expert in the history and latest advances in large language models. With very intuitive explanations, great real-life examples, clear illustrations, and comprehensive code labs, this book lifts the curtain on the complexities of transformer models, tokenizers, semantic search, RAG, and many other cutting-edge technologies. A must read for anyone interested in the latest AI technology!”

Luis Serrano, PhD - Founder and CEO of Serrano Academy

“Hands-On Large Language Models brings clarity and practical examples to cut through the hype of AI. It provides a wealth of great diagrams and visual aids to supplement the clear explanations. The worked examples and code make concrete what other books leave abstract. The book starts with simple introductory beginnings, and steadily builds in scope. By the final chapters, you will be fine-tuning and building your own large language models with confidence.”

Leland McInnes - Researcher at the Tutte Institute for Mathematics and Computing | creator of UMAP and HDBSCAN

Bonus content!

We attempted to put as much information into the book without it being overwhelming. However, even with a 400-page book there is still much to discover!

We continue to create more guides that compliment the book and go more in-depth into new and exciting topics:

A Visual Guide to Mamba	A Visual Guide to Quantization	The Illustrated Stable Diffusion
A Visual Guide to Mixture of Experts	A Visual Guide to Reasoning LLMs	The Illustrated DeepSeek-R1

Citation

Please consider citing the book if you consider it useful for your research:

@book{hands-on-llms-book,
  author       = {Jay Alammar and Maarten Grootendorst},
  title        = {Hands-On Large Language Models},
  publisher    = {O'Reilly},
  year         = {2024},
  isbn         = {978-1098150969},
  url          = {https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/},
  github       = {https://github.com/HandsOnLLM/Hands-On-Large-Language-Models}
}