# Hoper-J/AI-Guide-and-Demos-zh_CN: A Step-by-Step LLM Learning Guide for Chinese-Speaking Developers

> AI-Guide-and-Demos-zh_CN is a Chinese-language repository that walks developers from a first API call through local model deployment and fine-tuning, with Jupyter notebooks runnable on Kaggle or Colab for those without a GPU. It also mirrors the complete homework assignments from Li Hongyi's 2024 Generative AI Introduction course.

**Hoper-J/AI-Guide-and-Demos-zh_CN** — 这是一份入门AI/LLM大模型的逐步指南，包含教程和演示代码，带你从API走进本地大模型部署和微调，代码文件会提供Kaggle或Colab在线版本，即便没有显卡也可以进行学习。项目中还开设了一个小型的代码游乐场🎡，你可以尝试在里面实验一些有意思的AI脚本。同时，包含李宏毅 (HUNG-YI LEE）2024生成式人工智能导论课程的完整中文镜像作业。

- Repository: https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN
- Stars: 4,605 · Forks: 482
- Language: Python
- License: MIT
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/hoper-j-ai-guide-and-demos-zh-cn

## What This Repository Contains and Who It Is For

AI-Guide-and-Demos-zh_CN is a learning resource aimed at Chinese-speaking developers who are starting to work with large language models. The repository's stated goal, as explained in the README, is to help readers who are held back by a fear of the first step. Many beginners watch course videos but stall when they try to get an API key or configure a local environment. The project addresses this by providing working code that runs without any changes on Kaggle or Google Colab.

The README does not position this as a replacement for lecture content. It explicitly recommends watching Li Hongyi's Generative AI Introduction course in parallel. The repository provides the practical side: configured notebooks, step-by-step API guides, and a Dockerfile for those who want a local deep learning environment.

The audience is developers who can read Chinese and who are new to working with LLM APIs. Experienced ML engineers who already run local models and fine-tune pipelines will find the material introductory.

## How the Repository Is Organized

The top-level directories divide content into four areas. Guide/ contains markdown articles with explanations and step-by-step instructions. Demos/ contains the corresponding Jupyter notebooks, one per article in most cases. CodePlayground/ holds standalone AI scripts that the README says can be run with a single command after environment setup. PaperNotes/ contains summaries of foundational research papers.

Each entry in the README table carries one or more tags that indicate GPU requirements and online availability. The API tag means the notebook uses only an LLM API, requires no GPU, and runs on Kaggle or Colab without modification. The LLM tag means the notebook works with local language models and may require GPU memory. The SD tag means the notebook involves Stable Diffusion image generation and has GPU requirements.

For notebooks with Kaggle support, the README gives the path to enable GPU: Settings, then Accelerator, then select GPU. For Colab, it is Runtime, then Change runtime type, then select GPU.

## Topics Covered: DeepSeek API, Fine-Tuning, and the Li Hongyi Course Assignments

The DeepSeek section is one of the most detailed parts of the repository. It covers obtaining a DeepSeek API key from multiple Chinese providers including the official DeepSeek platform, Siliconflow (硅基流动), Alibaba Cloud Bailian (阿里云百炼), Baidu AI Cloud (百度智能云), and ByteDance Volcano Engine (字节火山引擎). The articles then cover the OpenAI SDK interface for DeepSeek, parsing API responses, streaming output, and multi-turn conversation patterns using messages lists.

The introductory section (导论) walks through the same content in order: API setup with Qwen, ZhipuAI, and DeepSeek; building a Gradio application; prompt engineering for math problem solving; LLM fine-tuning; and Stable Diffusion image generation.

The repository also mirrors the complete assignment materials from Li Hongyi's 2024 Generative AI Introduction course, stored under GenAI_PDF/. The README includes a recent guide on FastMCP source code and a Claude Code usage guide, reflecting that the project continues to add coverage of newer tooling.

## Setting Up the Environment

The project uses uv for environment management. The README states this is a deliberate change and acknowledges it is not a friendly transition for readers accustomed to pip or conda, but treats it as worthwhile given that uv has become widely adopted.

The pyproject.toml specifies the exact Python requirement:

```toml
[project]
name = "ai-guide-and-demos-zh-cn"
version = "1.0.0"
requires-python = "==3.12.*"
```

The dependency list in pyproject.toml is extensive, covering transformers, peft, trl, diffusers, datasets, accelerate, torch, torchvision, torchaudio, gradio, langchain, fastapi, llama-cpp-python, and many others. For readers who prefer not to install locally, the Kaggle and Colab links in the README table provide pre-configured environments that avoid local setup entirely.

The README includes a Docker quick-deploy section for readers who want a preconfigured deep learning environment without manually installing GPU drivers. The base Docker image is described as ready, though specific image names and commands are in the README sections not reproduced in the available portion.

## What the Repository Does Not Cover and Where It Falls Short

The repository is a tutorial collection, not a comprehensive reference. It does not document production concerns such as rate limiting, error handling in high-volume API scenarios, model serving infrastructure, or cost optimization. These topics appear in the official documentation of each API provider but are out of scope here.

The README notes that some Colab links are temporarily unavailable. The README explains the reason: the account hosting those notebooks lost external access and cannot be recovered because a recovery email was not registered. The author plans to migrate those notebooks to a new account, but as of the last push they remain inaccessible.

The coverage is also selective. The repository focuses on APIs available to developers in China or through Chinese cloud providers. Readers outside that context who want to use different providers may find the API-specific setup sections less directly applicable, even though the underlying OpenAI SDK patterns are transferable.

## Alternative Learning Path and Maintenance Status

The fast.ai Practical Deep Learning course is a comparable self-study resource targeting practitioners. Its approach differs in emphasis: fast.ai starts from working code and practical results, then works toward theory, and it is taught in English with a different toolchain (FastAI library rather than the Hugging Face ecosystem). For a Chinese-speaking developer who wants a structured course with video lectures alongside code, fast.ai would require reading in English and adapting the examples, while AI-Guide-and-Demos-zh_CN is written for Chinese readers and integrates Chinese-accessible API providers from the start.

The last push was on 2026-09-25, and the README was updated on that date with new content including a guide on Claude and GPT subscription options. The project has no GitHub releases.

## Conclusion

AI-Guide-and-Demos-zh_CN is a good starting point for a Chinese-speaking developer who wants to move from watching AI videos to running working code, without immediately needing a local GPU. It is not a course with structured exercises or grading. It is not a reference for production LLM deployment. The repository requires Python 3.12 exactly as specified in pyproject.toml. Readers who want to run notebooks locally should use uv, which the README states is now the standard environment manager for the project. The last push was on 2026-09-25.

## FAQ

### Can I learn from AI-Guide-and-Demos-zh_CN without a GPU?

Yes. The README uses an API tag to mark notebooks that run entirely on LLM APIs and do not require a local GPU. These notebooks are available on Kaggle and Colab. Sections marked LLM or SD do require GPU access, but even those can be run on the free GPU quota that Kaggle and Colab provide.

### Does this repository cover LLM fine-tuning?

Yes. The introductory section includes an article and notebook on LLM fine-tuning. The pyproject.toml includes peft and trl among its dependencies, which are the standard libraries used for parameter-efficient fine-tuning in the Hugging Face ecosystem.

### What Python version does AI-Guide-and-Demos-zh_CN require?

The pyproject.toml specifies requires-python as ==3.12.*, meaning exactly Python 3.12. Other versions are not declared as compatible.

## Sources

- [Hoper-J/AI-Guide-and-Demos-zh_CN on GitHub](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN)
- [Issues](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/issues)
- [License: MIT](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/LICENSE)
- [README](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/hoper-j-ai-guide-and-demos-zh-cn