Hands-On Large Language Models is a code repository accompanying an O'Reilly book that provides practical examples and implementations for learning large language models.
The repository contains executable code examples designed to complement the book's visual and conceptual explanations of how large language models work and how to use them in practice. The examples are structured as Jupyter notebooks that walk through concrete implementations of LLM concepts and techniques, allowing readers to run and experiment with the code directly rather than just reading about theory.
This repository suits developers and practitioners who are working through the book and want to execute the accompanying code examples, or who are learning LLMs through a hands-on approach with visual explanations. The material is designed for accessibility, with the book emphasizing visual education through custom illustrations. The examples are built and tested primarily for Google Colab, which provides free GPU access and represents the most stable platform for running them, though other cloud providers should also work.
The project maintains active engagement with its material through the linked book and associated educational resources, including a companion course. The codebase consists of Jupyter notebooks organized to correspond with the book's chapters and topics, making it straightforward to locate and run examples relevant to specific learning objectives.
