What it does

This is a Chinese-language tutorial repository aimed at beginners who want to run open-source LLMs locally on Linux. It provides step-by-step guides for environment configuration, model deployment (command-line, web demo, LangChain integration), and fine-tuning methods including full-parameter distributed training, LoRA, and p-tuning. The project explicitly targets “普通学生” — ordinary students without API access or big budgets.

The interesting bit

The breadth is the point: 50+ models covered, each with its own complete tutorial, plus an AMD GPU section and Ascend NPU section. The “Example series” gets weird in a good way — there’s a LoRA-funed model that speaks like 甄嬛 from Empresses in the Palace, and another for “人情世故” social etiquette scenarios. The project bills itself as “LLM与普罗大众的阶梯” — a ladder between LLMs and the masses.

Key highlights

• 50+ supported models: Qwen3, DeepSeek-R1, Llama4, GLM-4, Gemma3, MiniCPM, and many others
• Structured learning path: environment → deployment → fine-tuning
• AMD GPU and Ascend NPU support with dedicated setup guides
• Example projects: character-roleplay fine-tuning, math problem-solving (AMChat), personal AI digital twin
• Active community: issues and PRs welcome, maintainer onboarding available
• Companion projects for deeper theory (Happy-LLM, Tiny-Universe, so-large-llm)

Caveats

• Linux-only; Windows/Mac users are out of scope
• Chinese-language only (English README exists but appears secondary)
• Jupyter Notebook format means you’ll be reading notebooks, not a structured docs site
• The README is upfront that this is “主要就是教程” — mainly tutorials, not a framework or tool

Verdict

Worth bookmarking if you’re a Chinese-speaking student or researcher trying to get your first local LLM running without drowning in fragmented English docs. Skip it if you need Windows support, want a unified API abstraction, or already know your way around vLLM and Axolotl.