datawhalechina/happy-llm resource

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

33272 stars · 3155 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Happy-LLM is a free, open-source Chinese-language tutorial by Datawhale that teaches large language model principles from scratch, covering Transformer architecture, pretraining, and fine-tuning. Learners implement a full LLaMA2 model and practice RAG, Agent, and agentic-RL techniques through hands-on Jupyter Notebook exercises.

Use cases

• learn how large language models work from scratch
• implement a LLaMA2 model by hand
• understand the Transformer architecture and attention mechanism
• practice pretraining and fine-tuning an LLM including LoRA and QLoRA
• learn how to build RAG and agent applications
• study agentic reinforcement learning like GRPO
• find a structured free LLM course in Chinese

When to choose

• you want a systematic, hands-on curriculum for understanding and building LLMs from first principles
• you prefer learning by implementing models in code rather than just reading theory
• you want free, well-maintained educational material covering pretraining through RAG and agents

When to avoid

• you need a production-ready LLM training framework or library rather than a tutorial
• you need English-only learning materials
• you want a quick reference guide for using existing LLM APIs instead of building models yourself

Facets

learning-resource · maturity active

llm-training machine-learning rag agent-framework prompt-engineering large-language-models deep-learning tutorials education python cross-platform llm-from-scratch transformer llama2 fine-tuning lora chinese datawhale jupyter-notebook pretraining agentic-rl natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="datawhalechina/happy-llm")