Download
Happy-LLM is an open-source educational project created by the Datawhale AI community that provides a structured and comprehensive tutorial for understanding and building large language models from scratch. The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer architecture, pre-training paradigms, and model scaling strategies while also providing hands-on coding examples so readers can implement and experiment with their own models. The tutorial emphasizes practical understanding by walking users through building and training small language models, including tokenizer construction, pre-training workflows, and fine-tuning methods.
Features
- Step-by-step tutorial for understanding large language models
- Hands-on implementation of Transformer and LLM architectures
- Guided training workflows including pre-training and fine-tuning
- Practical examples using modern deep learning frameworks
- Coverage of advanced topics such as RAG and AI agents
- Open educational resources with code, documentation, and exercises
Project Samples
