Hands-On LLM Practical Project Codebase: From Theory to Hands-On Practice with Large Language Models

This GitHub code repository is based on the book 'Hands-On Large Language Models' and provides a complete learning path from theory to hands-on practice with large language models. The repository is divided into three core modules: understanding LLM fundamentals, using pre-trained models, and training & fine-tuning. It is suitable for developers, researchers, etc., to systematically learn or quickly reference LLM technologies, addressing the challenge of translating theory into practical code.

• Original author/maintainer: mpopov576
• Source platform: GitHub
• Repository name: hands_on_llm_projects
• Link: https://github.com/mpopov576/hands_on_llm_projects
• Time: Created on May 28, 2026; updated on June 13, 2026

The project aims to address the challenge of developers translating LLM theory into runnable code, providing a practical path for readers of 'Hands-On Large Language Models'.

Module 1: Understanding LLM Fundamentals

Covers underlying mechanisms such as Tokens, Embeddings, recommendation system applications, Transformer architecture, etc.

Module 2: Using Pre-trained Models

Includes application scenarios like text classification, clustering/topic modeling, prompt engineering, advanced text generation, semantic search & RAG, multimodal LLMs, etc.

Module 3: Training & Fine-tuning

Covers content such as creating text embedding models, fine-tuning classification models, fine-tuning generative models (instruction/dialogue fine-tuning), etc.

• Interactive Learning: All examples are provided in Jupyter Notebook format, allowing line-by-line execution and modification.
• Progressive Difficulty: From basic concepts to complex RAG systems, suitable for learners of different levels.
• Integration of Theory and Practice: Based on the book's theoretical framework, understand 'how to do' and 'why'.
• Code Reusability: Modular structure facilitates extracting functional fragments for application in one's own projects.

1. Enterprise knowledge base Q&A system (RAG technology)
2. Content moderation and classification (text classification/clustering)
3. Personalized recommendation engine (embedding technology + recommendation algorithms)
4. Fine-tuning of models for vertical domains (adaptation to professional fields like healthcare, law, etc.)

Target Audience: Developers systematically learning LLMs, researchers transitioning from theory to practice, engineers quickly getting started with LLM applications, and book readers.

Recommendations: Learn in the order of the three modules; expand experiments after understanding examples (change datasets, adjust parameters); directly jump to the corresponding module as a technical reference.