HandsOnLLM/Hands-On-Large-Language-ModelsPublic

Official code repo for the O'Reilly Book - "Hands-On Large Language Models"

AI summary: The official companion repository for the O'Reilly book 'Hands-On Large Language Models', featuring practical code examples.

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Jupyter NotebookApache-2.0Created Jun 28, 2024Last push 3mo ago+97 stars this week+143 this month

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  • Widely adopted

    27,996 stars

  • Permissive license

    Apache-2.0

What Hands-On-Large-Language-Models does

This repository serves as the definitive, executable companion to the O'Reilly book 'Hands-On Large Language Models', translating theoretical concepts into runnable code. It provides structured Jupyter notebooks and scripts that guide learners through the entire lifecycle of working with LLMs, from basic inference to advanced fine-tuning and deployment. The technical content focuses on modern, open-source tooling, demonstrating how to implement RAG architectures, handle embeddings, and manage model weights locally. What makes this distinctive is its pedagogical design; the code is meticulously commented and structured to align with the book's chapters, ensuring that complex AI engineering concepts are accessible and reproducible for self-guided learners. It bridges the gap between high-level theory and applied engineering.

This repository is designed for software engineers, data scientists, and students who are actively reading the associated O'Reilly book. Prerequisites include a working knowledge of Python and basic machine learning concepts.

  • Chapter-Aligned Notebooks: provides executable Jupyter notebooks that directly correspond to the concepts taught in each chapter of the book.
  • End-to-End RAG Implementation: demonstrates how to build robust Retrieval-Augmented Generation pipelines from scratch.
  • Local Fine-Tuning Examples: includes scripts for fine-tuning smaller open-source models on consumer hardware using PEFT and LoRA.
  • Modern Tooling Integration: showcases practical implementations using current industry-standard libraries like Hugging Face Transformers and LangChain.
  • Reproducible Environments: supplies comprehensive requirements files to ensure all examples run consistently across different setups.

Where teams use it

Self-Guided Learning

Software engineers use the repository to transition into AI engineering by executing the code alongside reading the textbook.

Reference Implementation

Practitioners copy and adapt the thoroughly tested RAG and fine-tuning scripts to bootstrap their own enterprise AI projects.

Academic Instruction

Computer science professors utilize the structured notebooks as foundational material for university courses on applied machine learning.

Skill Assessment Preparation

Job seekers work through the examples to build a portfolio of practical LLM implementations before technical interviews.

Getting started: git clone https://github.com/HandsOnLLM/Hands-On-Large-Language-Models.git && pip install -r requirements.txt

README

main branch

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:

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

Tip

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


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}
}
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SunMonTueWedThuFriSat036912151821Sun 0:00 — 0 commitsSun 1:00 — 0 commitsSun 2:00 — 1 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 1 commitsSun 8:00 — 0 commitsSun 9:00 — 0 commitsSun 10:00 — 0 commitsSun 11:00 — 1 commitsSun 12:00 — 0 commitsSun 13:00 — 0 commitsSun 14:00 — 0 commitsSun 15:00 — 0 commitsSun 16:00 — 0 commitsSun 17:00 — 0 commitsSun 18:00 — 0 commitsSun 19:00 — 0 commitsSun 20:00 — 0 commitsSun 21:00 — 0 commitsSun 22:00 — 2 commitsSun 23:00 — 0 commitsMon 0:00 — 0 commitsMon 1:00 — 0 commitsMon 2:00 — 0 commitsMon 3:00 — 0 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 1 commitsMon 8:00 — 1 commitsMon 9:00 — 6 commitsMon 10:00 — 2 commitsMon 11:00 — 0 commitsMon 12:00 — 1 commitsMon 13:00 — 0 commitsMon 14:00 — 0 commitsMon 15:00 — 0 commitsMon 16:00 — 0 commitsMon 17:00 — 0 commitsMon 18:00 — 0 commitsMon 19:00 — 0 commitsMon 20:00 — 0 commitsMon 21:00 — 0 commitsMon 22:00 — 0 commitsMon 23:00 — 0 commitsTue 0:00 — 0 commitsTue 1:00 — 0 commitsTue 2:00 — 0 commitsTue 3:00 — 0 commitsTue 4:00 — 0 commitsTue 5:00 — 0 commitsTue 6:00 — 0 commitsTue 7:00 — 0 commitsTue 8:00 — 0 commitsTue 9:00 — 1 commitsTue 10:00 — 2 commitsTue 11:00 — 1 commitsTue 12:00 — 0 commitsTue 13:00 — 0 commitsTue 14:00 — 0 commitsTue 15:00 — 0 commitsTue 16:00 — 0 commitsTue 17:00 — 1 commitsTue 18:00 — 0 commitsTue 19:00 — 0 commitsTue 20:00 — 0 commitsTue 21:00 — 0 commitsTue 22:00 — 0 commitsTue 23:00 — 0 commitsWed 0:00 — 0 commitsWed 1:00 — 0 commitsWed 2:00 — 0 commitsWed 3:00 — 0 commitsWed 4:00 — 0 commitsWed 5:00 — 0 commitsWed 6:00 — 0 commitsWed 7:00 — 0 commitsWed 8:00 — 1 commitsWed 9:00 — 0 commitsWed 10:00 — 0 commitsWed 11:00 — 0 commitsWed 12:00 — 1 commitsWed 13:00 — 1 commitsWed 14:00 — 0 commitsWed 15:00 — 0 commitsWed 16:00 — 0 commitsWed 17:00 — 2 commitsWed 18:00 — 1 commitsWed 19:00 — 0 commitsWed 20:00 — 0 commitsWed 21:00 — 0 commitsWed 22:00 — 0 commitsWed 23:00 — 0 commitsThu 0:00 — 0 commitsThu 1:00 — 0 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 0 commitsThu 6:00 — 0 commitsThu 7:00 — 2 commitsThu 8:00 — 1 commitsThu 9:00 — 0 commitsThu 10:00 — 0 commitsThu 11:00 — 0 commitsThu 12:00 — 0 commitsThu 13:00 — 0 commitsThu 14:00 — 0 commitsThu 15:00 — 0 commitsThu 16:00 — 1 commitsThu 17:00 — 0 commitsThu 18:00 — 0 commitsThu 19:00 — 0 commitsThu 20:00 — 0 commitsThu 21:00 — 0 commitsThu 22:00 — 0 commitsThu 23:00 — 0 commitsFri 0:00 — 0 commitsFri 1:00 — 0 commitsFri 2:00 — 0 commitsFri 3:00 — 0 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 0 commitsFri 7:00 — 0 commitsFri 8:00 — 2 commitsFri 9:00 — 1 commitsFri 10:00 — 1 commitsFri 11:00 — 0 commitsFri 12:00 — 2 commitsFri 13:00 — 0 commitsFri 14:00 — 4 commitsFri 15:00 — 3 commitsFri 16:00 — 0 commitsFri 17:00 — 0 commitsFri 18:00 — 0 commitsFri 19:00 — 0 commitsFri 20:00 — 0 commitsFri 21:00 — 0 commitsFri 22:00 — 0 commitsFri 23:00 — 0 commitsSat 0:00 — 0 commitsSat 1:00 — 0 commitsSat 2:00 — 0 commitsSat 3:00 — 0 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 0 commitsSat 10:00 — 0 commitsSat 11:00 — 0 commitsSat 12:00 — 0 commitsSat 13:00 — 0 commitsSat 14:00 — 0 commitsSat 15:00 — 1 commitsSat 16:00 — 0 commitsSat 17:00 — 0 commitsSat 18:00 — 0 commitsSat 19:00 — 0 commitsSat 20:00 — 0 commitsSat 21:00 — 0 commitsSat 22:00 — 0 commitsSat 23:00 — 0 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Feb 20, 2026daily#18+122
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