HandsOnLLM/Hands-On-Large-Language-Models resource

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28623 stars · 6570 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

The official companion code repository for the O'Reilly book 'Hands-On Large Language Models' by Jay Alammar and Maarten Grootendorst, containing Jupyter notebook examples for all chapters. It is a visually-driven educational resource with nearly 300 custom figures covering LLM concepts, tokenizers, semantic search, and RAG.

Use cases

• learn how large language models work with illustrated explanations
• find hands-on jupyter notebook examples for LLM techniques
• understand tokenizers, embeddings, and semantic search
• learn to build RAG applications step by step
• study transformer architecture with visual diagrams
• get a practical introduction to using open-source LLMs

When to choose

• you want a structured, book-style learning path for LLMs
• you prefer visual, illustrated explanations of complex concepts
• you want runnable notebook code accompanying each topic
• you are a beginner-to-intermediate practitioner entering the LLM field

When to avoid

• you need production-ready LLM application code or a library
• you want a comprehensive reference for training LLMs from scratch at scale
• you are looking for a tool or framework rather than educational material

Facets

learning-resource · maturity active

machine-learning llm-inference rag nlp data-science large-language-models artificial-intelligence machine-learning tutorials python cross-platform book jupyter-notebooks oreilly educational illustrated-guide transformers semantic-search embeddings natural-language-processing

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markdown · JSON · MCP: product_card(name="HandsOnLLM/Hands-On-Large-Language-Models")