Hands-On Large Language Models: A Colab-First Code Companion for Alammar and Grootendorst's O'Reilly Book

A Visual Book's Code Companion

This repository exists to serve one purpose: it holds the code examples for the O'Reilly book Hands-On Large Language Models by Jay Alammar and Maarten Grootendorst, a work the authors call 'The Illustrated LLM Book' because of its almost 300 custom figures. The target reader is someone who learns by combining diagrams with executable code. If you are that reader, the repo is a direct extension of the text. If you are not reading the book, the notebooks lose much of their explanatory power, since they are chapter-aligned and presume the book's narrative. The repository is not a standalone course and never claims to be one. It is a companion, and its structure reflects that dependency.

Twelve Notebooks, One Path Through the LLM Stack

The repository organizes code by chapter, with a notebook per chapter folder. The progression is deliberate. Chapter 1 introduces language models, then Chapter 2 covers tokens and embeddings, and Chapter 3 looks inside transformer LLMs. From there the book moves to applied tasks: text classification, clustering and topic modeling, prompt engineering, advanced text generation, semantic search and retrieval-augmented generation, multimodal LLMs, creating text embedding models, fine-tuning representation models for classification, and fine-tuning generation models. The README table of contents stops mid-way through Chapter 12, listing 'Fine-tuning Generation Models' as the last visible entry, so the full chapter list is not completely shown in the material I have. Each notebook is linked with an 'Open in Colab' badge, which is the primary entry point.

Colab-First Design and the Free T4 GPU

The README states plainly: 'We advise to run all examples through Google Colab for the easiest setup.' That advice is not a suggestion but a design constraint. The authors say all examples were 'mainly built and tested using Google Colab, so it should be the most stable platform.' Colab offers a free T4 GPU with 16GB of VRAM, which is enough for the book's fine-tuning exercises. This Colab-first approach means the notebooks likely include cells that mount Google Drive or install packages in a Colab environment, and the code may assume Colab's pre-installed libraries. For a reader, this lowers the barrier to entry: you do not need a local GPU or a cloud account beyond Google. But it also means that running the same notebooks on a local machine or a different cloud provider may require adjustments that the README does not document. The phrase 'any other cloud provider should work' is optimistic but unverified in the material.

Getting Started: Commands and Config from the README

There is no installation command in the README. The repository is not a pip-installable package. Instead, the 'getting started' path is to click the Colab badge for the chapter you want. That badge points to a URL of the form https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter01/Chapter%201%20-%20Introduction%20to%20Language%20Models.ipynb. The URL pattern reveals the structure: each notebook lives in a chapter folder on the main branch, and Colab opens it directly from GitHub. For local use, you would clone the repository with git clone https://github.com/HandsOnLLM/Hands-On-Large-Language-Models.git, but the README does not give that command. The only explicit configuration is the choice of runtime: select a T4 GPU in Colab for the free tier. There is no requirements.txt or environment.yml mentioned in the README, so dependencies are presumably handled inside each notebook or by Colab's default environment.

Where the Repository Falls Short

The most obvious limitation is that the README is a book advertisement as much as a technical guide. It lists purchase links for Amazon, O'Reilly, Kindle, and others, and it promotes a DeepLearning.AI short course. For an engineer evaluating the repository as a standalone resource, that is noise. More substantively, the repository has no tagged releases. The 'Recent releases' field is empty, and the last push date is 2026-04-24, which suggests active maintenance but no versioned snapshots. That means you cannot pin your learning environment to a specific state; the main branch could change under you. Also, the README's table of contents is truncated in the material I have, so I cannot confirm the exact content of Chapter 12 beyond the folder name. If you are a reader who wants to reproduce the book's results exactly, the absence of release tags is a real risk: a notebook that worked in 2025 might break in 2026 if a dependency updates.

Alternatives: Hugging Face Courses and the Transformers Docs

If you want a free, book-independent way to learn the same material, the Hugging Face course (huggingface.co/learn) covers tokenizers, transformers, fine-tuning, and RAG with notebooks that run in Colab as well. The difference in approach is that Hugging Face's course is self-contained: each lesson explains the concept and then provides the code, so you do not need a separate textbook. The Hands-On LLM repository assumes you have the book open next to the notebook, and its code is organized to match the book's narrative, not to stand alone. Another alternative is the official Transformers documentation, which includes tutorial notebooks for fine-tuning and pipelines, but those are reference material rather than a structured curriculum. If you are an engineer who wants to understand LLMs without buying a book, the Hugging Face course is the more direct route. If you already own the book, this repository is the better match because it follows the book's exact chapter flow.

Licence and Maintenance Cost

The repository is licensed under Apache-2.0, which means you can freely use, modify, and distribute the code, even in commercial projects, as long as you preserve the license notice. That is a permissive license, so there is no legal barrier to adapting the notebooks for your own teaching or internal use. The maintenance cost is low because the repository is not a software library with an API surface; it is a set of static notebooks. There is no build system, no dependency lock file, and no release cadence to track. The real cost is the drift between the notebooks and the evolving ecosystems they depend on, such as Transformers and PyTorch. The last push in April 2026 suggests the authors are updating the code, but without tags, you cannot tell which version of a notebook you are looking at. For a learner, that is acceptable. For a team that wants to reuse the code in production, it is a warning sign.

Who Should Use This Repository

The ideal user is a reader of the O'Reilly book who wants to run each example without fighting environment setup. The Colab badges make that almost trivial, and the Apache-2.0 license means you can even fork the repo and add your own annotations. The person who should not use it is an engineer looking for a production-ready codebase or a self-contained tutorial. The notebooks are educational, not optimized for deployment, and they depend on the book's context. Before you start, verify that the Colab links still resolve to the main branch, since the README may not be updated if a notebook path changes. Also check the book's own website, llm-book.com, for any supplementary materials or errata that the README does not mention. In short, this repository is a well-maintained companion for a specific book, and its value is tied to that book. It is not a general-purpose LLM toolkit, and it does not pretend to be one.

Editorial conclusion

Adopt this repository if you are reading the O'Reilly book and want runnable code that matches each chapter, especially if you plan to use Google Colab's free T4 GPU. Skip it if you need a standalone tutorial or a production codebase, because the notebooks assume book context and are not packaged as a library. Before starting, verify that the Colab links resolve to the latest notebooks on the main branch and that you have a Google account for Colab; also check the book's errata page for any known code updates, since the repo has no tagged releases.

Official sources

1. HandsOnLLM/Hands-On-Large-Language-Models on GitHub
2. Issues
3. License: Apache-2.0
4. Project website
5. README