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Hoper-J/AI-Guide-and-Demos-zh_CN

A Chinese-language step-by-step guide and demo code for learning LLMs, from calling APIs to local deployment and fine-tuning, with Kaggle/Colab versions.

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What is it?

This repository is a Chinese-language learning path for large language models and generative AI. It starts with calling LLM APIs through the OpenAI-compatible SDK and then moves on to running quantized models locally, building a simple Gradio app, fine-tuning models, RAG with LangChain, and Stable Diffusion LoRA training. Each tutorial is paired with notebook code that has matching Kaggle or Colab links, so readers without a local GPU can run the examples online.

Beyond the tutorials, the project includes a small CodePlayground with runnable scripts for AI video summarization, chat, and image generation, plus a DeepSeek API usage section. It also hosts a Chinese mirror of the homework from Hung-yi Lee's 2024 Generative AI course, with the assignment code adapted to run locally.

The project uses uv for environment setup and provides a Docker image for a deep learning environment on Linux.

Key features

  • 01Step-by-step tutorials from LLM API calls to local deployment and fine-tuning
  • 02Notebook code with Kaggle and Colab links for running without a local GPU
  • 03Covers LoRA, DPO, RAG with LangChain, quantization (GPTQ, AWQ, GGUF), and Stable Diffusion LoRA
  • 04Runnable scripts in CodePlayground for video summarization, chat, and image generation
  • 05Chinese mirror of homework from Hung-yi Lee's 2024 generative AI course
  • 06Docker images with preinstalled deep learning tools for Linux

Use cases

  • A developer new to LLMs who wants hands-on API examples without a GPU
  • A student following Hung-yi Lee's 2024 generative AI course who needs runnable homework code
  • Someone learning to run quantized 7B models on a personal computer
  • Teams or learners exploring LoRA fine-tuning and RAG with Chinese-language walkthroughs

Quick start

  1. 1

    Clone the repository

    git clone https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN.git
  2. 2

    Open the guide and follow the environment setup steps (uv is used for environment configuration)

Things to know

  • Most content is written in Chinese, so English-speaking developers may find it less useful.
  • Several guides require a GPU with enough VRAM for local model training or inference.
  • Some Colab links were suspended and are being migrated, so some online notebooks may be unavailable.

Topics

#llm#tutorial#fine-tuning#rag#lora#openai-sdk#jupyter-notebook#stable-diffusion

FAQ

What is AI-Guide-and-Demos-zh_CN?
A Chinese-language step-by-step guide and demo code for learning LLMs, from calling APIs to local deployment and fine-tuning, with Kaggle/Colab versions. This repository is a Chinese-language learning path for large language models and generative AI. It starts with calling LLM APIs through the OpenAI-compatible SDK and then moves on to running quantized models locally, building a simple Gradio app, fine-tuning models, RAG with LangChain, and Stable Diffusion LoRA training. Each tutorial is paired with notebook code that has matching Kaggle or Colab links, so readers without a local GPU can run the examples online.
Is AI-Guide-and-Demos-zh_CN free to use? What license does it have?
AI-Guide-and-Demos-zh_CN is open source under the MIT license. Check the license terms before using it in commercial products.
Is AI-Guide-and-Demos-zh_CN still maintained?
Actively maintained. The last commit to AI-Guide-and-Demos-zh_CN was on Oct 12, 2026.
What can I use AI-Guide-and-Demos-zh_CN for?
A developer new to LLMs who wants hands-on API examples without a GPU A student following Hung-yi Lee's 2024 generative AI course who needs runnable homework code Someone learning to run quantized 7B models on a personal computer Teams or learners exploring LoRA fine-tuning and RAG with Chinese-language walkthroughs
How do I install AI-Guide-and-Demos-zh_CN?
Clone the repository: git clone https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN.git Open the guide and follow the environment setup steps (uv is used for environment configuration)
Where can I get AI-Guide-and-Demos-zh_CN?
The source code and installation instructions are on GitHub: https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN

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