Hands On Large Language Models

The book and accompanying code provide a practical, visual introduction to large language models covering how they work internally, how to use them via APIs and locally-run models, and how to fine-tune and deploy them for specific applications.

It is designed for practitioners who want working knowledge of LLMs without requiring a deep background in machine learning research.

The code examples progress from foundational concepts tokenization, embeddings, attention, transformer architecture through applied techniques including prompt engineering, retrieval-augmented generation (RAG), semantic search, fine-tuning with LoRA, and model evaluation.

Each chapter is accompanied by Jupyter notebooks that run on Google Colab, making the material accessible without requiring local GPU hardware.

The visual style emphasizes diagrams and intuitive explanations over dense mathematical notation, reflecting Jay Alammar's approach in widely-read blog posts like 'The Illustrated Transformer.'

The book's scope covers the complete LLM application stack: selecting and loading models via the Hugging Face ecosystem, building text classification and generation pipelines, creating embedding-based search systems, and deploying fine-tuned models for production use cases.

It has been widely adopted in university courses, corporate training programs, and self-study curricula as an accessible yet technically substantive introduction to applied LLM engineering. The repository is freely available on GitHub while the full book is available through O'Reilly's platform and retail channels.