> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/HandsOnLLM/Hands-On-Large-Language-Models/llms.txt > Use this file to discover all available pages before exploring further. # Welcome to Hands-On Large Language Models > Official documentation for the O'Reilly book featuring practical tutorials and visual guides for understanding and building with LLMs Hands-On Large Language Models Book Cover ## About This Book Welcome to **Hands-On Large Language Models** by [Jay Alammar](https://www.linkedin.com/in/jalammar/) and [Maarten Grootendorst](https://www.linkedin.com/in/mgrootendorst/) — a comprehensive guide to understanding and building with Large Language Models through **nearly 300 custom-made figures** and hands-on code examples. This documentation provides a companion to the book, making it easier to navigate chapters, run code examples, and explore advanced topics. All examples are designed to run on Google Colab with a free T4 GPU (16GB VRAM). You can also run them on any cloud provider or local setup. ## What You'll Learn Understand how language models work, from tokens and embeddings to transformer architecture Learn text classification, clustering, and topic modeling with modern LLMs Master prompt engineering and advanced generation techniques Create custom embedding models and fine-tune BERT and generation models ## Key Features Nearly 300 custom illustrations make complex LLM concepts accessible and easy to understand. Our visual teaching style has been praised by leaders in the field. Every chapter includes working Jupyter notebooks with real code using PyTorch, Transformers, and sentence-transformers. All examples are tested and ready to run. From basic tokens and embeddings to advanced topics like fine-tuning, RAG, multimodal models, and more. Plus bonus visual guides on cutting-edge topics. Learn practical techniques you can apply immediately, including semantic search, Retrieval-Augmented Generation, and model optimization. ## Book Structure The book is organized into 12 chapters covering the full spectrum of LLM development: ### Foundations * **Chapter 1**: Introduction to Language Models * **Chapter 2**: Tokens and Token Embeddings * **Chapter 3**: Looking Inside Transformer LLMs ### Text Understanding & Generation * **Chapter 4**: Text Classification * **Chapter 5**: Text Clustering and Topic Modeling * **Chapter 6**: Prompt Engineering * **Chapter 7**: Advanced Text Generation Techniques ### Advanced Applications * **Chapter 8**: Semantic Search and RAG * **Chapter 9**: Multimodal Large Language Models ### Model Development * **Chapter 10**: Creating Text Embedding Models * **Chapter 11**: Fine-Tuning BERT for Classification * **Chapter 12**: Fine-Tuning Generation Models ## Get Started Install dependencies and set up your development environment Review the required background knowledge and tools Jump into the first chapter and start learning Explore bonus visual guides on cutting-edge topics ## Praise for the Book "*Jay and Maarten have continued their tradition of providing beautifully illustrated and insightful descriptions of complex topics in their new book.*" — **Andrew Ng**, founder of DeepLearning.AI "*This is an exceptional guide to the world of language models and their practical applications in industry.*" — **Nils Reimers**, Director of ML at Cohere "*I can't think of another book that is more important to read right now.*" — **Josh Starmer**, StatQuest ## Community & Resources * **GitHub Repository**: [HandsOnLLM/Hands-On-Large-Language-Models](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) * **Purchase the Book**: Available on [Amazon](https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961), [O'Reilly](https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/), and other retailers * **DeepLearning.AI Course**: [How Transformer LLMs Work](https://www.deeplearning.ai/short-courses/how-transformer-llms-work/) Check out the [Advanced Topics](/advanced/quantization) section for bonus visual guides on quantization, Mamba, Mixture of Experts, reasoning LLMs, and more! This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.