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handsonllm/hands-on-large-language-models

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TLDR

Learn how large language models work through interactive Python code examples and 300+ illustrations. A practical guide to understanding transformers, embeddings, prompt engineering, and fine-tuning.

Mindmap

mindmap
  root((Hands-On LLMs))
    What it teaches
      How models process text
      Transformer architecture
      Prompt engineering
      Fine-tuning models
    Key topics
      Tokenization and embeddings
      Text classification
      Document clustering
      Semantic search
    Learning format
      Runnable code examples
      Google Colab notebooks
      Custom illustrations
      Chapter-by-chapter progression
    Audience
      Developers
      Data scientists
      AI learners
      No PhD required

Things people build with this

USE CASE 1

Learn how transformers and embeddings work by running code examples in your browser with free GPU access.

USE CASE 2

Understand prompt engineering and retrieval-augmented generation to build smarter AI applications.

USE CASE 3

Fine-tune language models on your own data without needing expensive hardware or deep ML expertise.

USE CASE 4

Build semantic search systems and multimodal AI features by following step-by-step tutorials.

Tech stack

PythonJupyter NotebookGoogle ColabPyTorchTransformers

Getting it running

Difficulty · easy Time to first run · 5min
Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

In plain English

This is the official code companion for "Hands-On Large Language Models," a published O'Reilly book by Jay Alammar and Maarten Grootendorst, two well-known AI educators. The book and this repository teach you how large language models (the technology behind ChatGPT, Claude, and similar AI systems) actually work, with nearly 300 custom illustrations and runnable Python code examples for every chapter. The book is aimed at developers, data scientists, and technically curious people who want to go beyond just using AI tools and actually understand how they're built and customized. You don't need a PhD, but you should be comfortable with Python code. Endorsed by Andrew Ng, founder of DeepLearning.AI, it's considered one of the most accessible deep-dives into LLM technology available. The topics covered follow a logical progression: how language models process text (tokenization and embeddings, how words get turned into numbers), what's happening inside the transformer architecture (the core design that powers modern AI), classifying text, clustering documents, prompt engineering (writing better instructions for AI), building search systems that understand meaning rather than just keywords, retrieval-augmented generation (connecting AI to your own documents), multimodal models that understand both text and images, and how to fine-tune existing models on your own data. All code examples run in Google Colab, a free, browser-based coding environment that requires no local setup and provides free GPU access. The full book is available for purchase on Amazon, O'Reilly, and other retailers. The code is free on GitHub.

Copy-paste prompts

Prompt 1
Walk me through the tokenization example in the Hands-On LLMs repo. How do words get converted to numbers that a model can understand?
Prompt 2
Show me how to use the retrieval-augmented generation code from Hands-On LLMs to connect a language model to my own documents.
Prompt 3
Explain the transformer architecture section from Hands-On LLMs. What's happening in the attention mechanism?
Prompt 4
How do I fine-tune a model using the code examples in the Hands-On LLMs repository on my own dataset?
Prompt 5
Walk through the semantic search example from Hands-On LLMs. How is it different from keyword-based search?
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