Hands-On Large Language Models — Repository Overview
This GitHub repository is the official code companion to the O'Reilly book "Hands-On Large Language Models" by Jay Alammar and Maarten Grootendorst. It bundles the notebooks, examples, visual aids, and supplemental material that follow the book's chapters. The project is designed to help learners move from conceptual understanding to practical implementation of modern large language models (LLMs).
What you'll find here
- A complete set of Jupyter/Colab notebooks corresponding to each chapter (Introduction, Tokens & Embeddings, Looking Inside Transformers, Text Classification, Clustering & Topic Modeling, Prompt Engineering, Advanced Generation Techniques, Semantic Search & RAG, Multimodal LLMs, Creating Embedding Models, Fine-tuning representation/generation models, etc.).
- Many custom illustrations and diagrams used in the book to explain complex concepts visually.
- Quick-start setup instructions, conda environment examples, and tips to run the examples reliably (Google Colab recommended for free GPU access).
- Bonus guides and linked visual explainers (quantization, Mixture of Experts, reasoning LLMs, Stable Diffusion guides, etc.).
Key features
- Practical, runnable examples: notebooks are set up to be executed in Colab so readers can reproduce results and experiments easily.
- Educational visualizations: the repository complements the highly illustrated nature of the book, aiding intuition about how LLMs work internally.
- Coverage across the LLM lifecycle: from tokenization and embeddings to advanced topics like retrieval-augmented generation (RAG), multimodal models, and fine-tuning.
- Citation and metadata: the repo includes a citation entry for academic use and references to the book and publisher.
Who is it for
- Engineers and practitioners who want hands-on examples to learn LLM techniques quickly.
- Students and researchers who prefer visual explanations alongside runnable code.
- ML practitioners exploring topics such as prompt engineering, semantic search, embeddings, and fine-tuning.
How to use
- Open the notebooks in Google Colab via the provided badges for each chapter for the easiest setup (Colab offers free T4 GPU access).
- Follow the repository's setup/conda instructions if running locally; ensure required packages (PyTorch, Hugging Face transformers, sentence-transformers, etc.) are installed.
- Run notebooks chapter-by-chapter to build understanding progressively—from basics to advanced generation and deployment-ready practices.
Additional notes
- The repository is actively intended as an educational companion rather than a production library; examples are pedagogical and focus on clarity and reproducibility.
- The project links to extra materials and authors' visual guides for deeper dives into specific subtopics.
Citation
If you use the repository in research, the authors provide a bibtex-style citation to reference the book and the GitHub repository.
