datawhalechina/tiny-universe resource

Health v2 · maintenance only

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5028 stars · 476 forks observed · 2026-08-28

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

An open-source Chinese-language tutorial series ('Tiny-Universe') that teaches building large language model systems from scratch, covering LLM internals, RAG, Agent, evaluation, diffusion, and GraphRAG with full PyTorch-level implementations. It is structured as Jupyter Notebook content for learners who want to understand LLM principles beyond high-level frameworks.

Use cases

• learn how transformers and LLMs work internally by implementing them from scratch
• build a minimal RAG framework by hand to understand retrieval-augmented generation
• implement a minimal agent system without high-level frameworks
• pretrain a tiny Llama3-style model on limited GPU memory
• understand how to evaluate large language models
• learn diffusion models by coding an image generation model from zero
• study GraphRAG by building a simplified version

When to choose

• you already know deep learning basics and want to understand LLM internals at the PyTorch level
• you prefer hands-on coding over using packaged APIs and frameworks
• you want a full-stack LLM curriculum covering model, RAG, agent, and evaluation
• you learn best by reproducing systems from principles with annotated code

When to avoid

• you need a production-ready RAG, agent, or evaluation framework
• you want a quick-start guide using high-level APIs like LangChain or OpenAI
• you need English-language documentation
• you need a maintained software library with a license for commercial use

Facets

learning-resource · maturity active

machine-learning deep-learning llm-training rag agent-framework data-science large-language-models tutorials artificial-intelligence deep-learning python educational from-scratch hands-on jupyter-notebooks transformer diffusion-models evaluation graphrag chinese retrieval-augmented-generation ai-agents

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