datawhalechina/happy-llm
📚 从零开始的大语言模型原理与实践教程
This project is a comprehensive learning guide for building large language models (LLMs) from scratch. It takes you from understanding core NLP concepts to designing, training, and fine-tuning your own LLM, like LLaMA2. It's intended for students, researchers, and AI enthusiasts who want to grasp the inner workings of LLMs and develop practical skills.
27,292 stars. Actively maintained with 1 commit in the last 30 days.
Use this if you are a student, researcher, or LLM enthusiast with some programming and deep learning background who wants to deeply understand LLM principles and implement them.
Not ideal if you are looking for a plug-and-play solution or an application-focused tool without delving into the underlying LLM architecture and training processes.
Stars
27,292
Forks
2,515
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 05, 2026
Commits (30d)
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/datawhalechina/happy-llm"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Recent Releases
Related tools
WangRongsheng/awesome-LLM-resources
🧑🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the...
SylphAI-Inc/AdalFlow
AdalFlow: The library to build & auto-optimize LLM applications.
LazyAGI/LazyLLM
Easiest and laziest way for building multi-agent LLMs applications.
luhengshiwo/LLMForEverybody
每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈
katanaml/sparrow
Structured data extraction and instruction calling with ML, LLM and Vision LLM