Hands-On Large Language Models Practical Code Repository: A Complete Learning Path from Theory to Application

This open-source code repository is the supporting implementation for the book co-authored by Jay Alammar and Maarten Grootendorst, covering comprehensive LLM technical practices from the basics of Transformer architecture to advanced applications (such as fine-tuning, RAG, etc.). It aims to bridge the gap between theory and practice, suitable for learners of different levels to master core skills.

After the boom of ChatGPT, LLM technology has become a core skill for AI practitioners, but there is a gap between theory and practice. Jay Alammar (a well-known author of Transformer visualization blogs) and Maarten Grootendorst (maintainer of NLP open-source projects) co-authored the book and supporting code repository, ensuring theoretical depth and practical value.

The code repository is organized by book chapters, covering core topics such as Transformer architecture, word embedding, text generation, model fine-tuning, prompt engineering, and RAG. It includes inference examples and cutting-edge technologies, suitable for beginners to senior developers.

1. Transformer Architecture: Implements core components such as self-attention, multi-head attention, and positional encoding; 2. Word Embedding: Demonstrates pre-trained vectors and contextual embeddings (e.g., BERT/GPT); 3. Text Generation: GPT-style model continuation and dialogue systems, including strategies like temperature sampling; 4. Fine-tuning: Complete workflow using the Hugging Face library; 5. RAG: Combines external knowledge bases with models to solve the hallucination problem.

Those with a deep learning background can directly choose chapters of interest (e.g., RAG/fine-tuning); beginners are advised to follow the chapters step by step. Each example is accompanied by detailed comments, and notebooks can be run independently to observe results. The modular design enhances flexibility.