echonoshy/cgft-llm resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2663 stars · 342 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A hands-on tutorial series (in Chinese) for learning large language model technologies, with Jupyter Notebook code, written docs, and video lessons covering fine-tuning, RAG, agents, MCP, vector databases, and LLM deployment. It is a practice-oriented educational repository rather than a production software library.

Use cases

• learn how to fine-tune an LLM like deepseek-r1
• build a RAG knowledge base from scratch
• understand tool calling and implement a browser-use agent
• set up an MCP server and client with fastmcp
• monitor LLM services with langfuse
• practice using a vector database like milvus
• design and build an agent system with memory and context

When to choose

• you want guided, hands-on practice with LLM fine-tuning, RAG, and agents
• you prefer learning through notebooks paired with video walkthroughs
• you want Chinese-language tutorials on modern LLM engineering topics

When to avoid

• you need a production-ready library or framework to ship in an application
• you want English-language documentation
• you need a single cohesive tool rather than a collection of independent lesson projects

Facets

learning-resource · maturity active

llm-training rag agent-framework mcp machine-learning llm-inference large-language-models artificial-intelligence tutorials python cross-platform hands-on-llm jupyter-notebooks video-tutorials fine-tuning chinese-language practice-series ai-agents retrieval-augmented-generation

Member repositories

For agents

markdown · JSON · MCP: product_card(name="echonoshy/cgft-llm")