# 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
Canonical: https://ross.abutalabs.com/products/ai-guide-and-demos-zh_cn
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-14T10:27:29+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 51
- inputs: {"age_days": 722, "days_push": 19, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4465, forks 472 (observed 2026-08-28T04:08:50.863120+00:00)

## What it is
A Chinese-language step-by-step guide and demo code collection for getting started with AI and LLMs, covering API usage, local model deployment, fine-tuning, AI image generation, and MCP, with Kaggle/Colab online versions of all code. It also mirrors the assignments of Hung-Yi Lee's 2024 Generative AI course and includes a small code playground.

## Use cases
- learn how to call LLM APIs with the OpenAI SDK
- get started with large language models as a beginner
- fine-tune an LLM without owning a GPU
- learn Stable Diffusion text-to-image generation
- follow Hung-Yi Lee's 2024 generative AI course assignments in Chinese
- run AI demo scripts in Colab or Kaggle for free
- understand MCP and FastMCP basics
- set up a deep learning environment with Docker or uv

## When to choose
- you are a Chinese-speaking beginner entering AI/LLM development
- you lack a local GPU and need free cloud notebooks
- you want guided, hands-on demos rather than theory-only videos
- you want structured assignments accompanying a university generative AI course

## When to avoid
- you need production-grade code or a maintained library to depend on
- you need English-language materials
- you are already an advanced practitioner seeking cutting-edge research content
- you need comprehensive coverage of a single topic rather than a broad intro

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-inference, llm-training, rag, stable-diffusion, mcp, sdk
- domain: artificial-intelligence, large-language-models, deep-learning, tutorials
- platform: python, cross-platform
- tags: chinese-language, tutorial, colab, kaggle, openai-sdk, fine-tuning, hung-yi-lee-course, code-playground, beginner-friendly, natural-language-processing, web-server

## Member repositories
- Hoper-J/AI-Guide-and-Demos-zh_CN (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:50.863120+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:20:38.966600+00:00, confidence not recorded.
  - readme: https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN (fetched 2026-08-28T04:08:50.863120+00:00, sha e123b81bcb5e)
- Data as of 2026-08-30T08:39:29.467469+00:00.