Hands-On-Large-Language-Models
Official code repo for the O'Reilly Book - 'Hands-On Large Language Models'
GraphCanon updated 4d · GitHub synced 4d
Decision brief
Consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples.
Good fit when
- - You are focusing on practical implementation aspects detailed in a structured format as outlined by O'Reilly's authoritative book.
- - Your team requires access to Jupyter Notebook-based examples that follow the guidelines set forth in the 'Hands-On Large Language Models' text.
Avoid when
- - If you need real-time model evaluation tools rather than educational materials, as this repository primarily provides code for understanding and implementing concepts covered in a book.
- - You are seeking proprietary or more specialized frameworks that go beyond the examples provided in an educational context to meet specific, advanced use-case needs.
- Pricing:
- freemium - The repository is free and open under the Apache-2.0 license.
- Requirements:
- - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial.
Observed Jul 11, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (114d since push)
- As of 4d
- Provenance
- Not a fork · Organization account
- As of 4d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/HandsOnLLM/Hands-On-Large-Language-ModelsHow it fits your stack(16)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
Depends on
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A resource containing materials and examples associated with a book on large language models.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 16, 2026
Categories
Tags
README
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 | |
| 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: Creat |
For agents
This page has a .md twin and JSON over the API.