HandsOnLLM/Hands-On-Large-Language-Models
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
The project lists 11 contributors on GitHub.
Use Cases & Benefits
- Provides Jupyter notebooks with code examples from the Hands-On Large Language Models O'Reilly book to teach practical LLM concepts and techniques.
- Offers visually rich, step-by-step educational content with nearly 300 custom figures to simplify complex LLM topics and accelerate learning.
- Use for learning foundational concepts of language models and token embeddings through interactive notebooks.
- Use for exploring advanced LLM applications like prompt engineering, semantic search, and fine-tuning generation models.
- Use for hands-on experimentation with multimodal LLMs and creating custom text embedding models in a reproducible environment.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
Watch-outs
- No commits in the last six months (last push Apr 2026).
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
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!
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.
"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
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
6.8K+324
Open issues
50+12
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| HandsOnLLM/Hands-On-Large-Language-Models | 30.1K | — | 12 | — |
| Category median | 115.8K | +4.0K | 75 | — |
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