self-llm
《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程
Chinese tutorials for setting up, deploying, and fine-tuning open-source LLMs locally on Linux without paid APIs.
This project is a Chinese-language tutorial collection for learning how to set up, run, and fine-tune open-source large language models (LLMs) — AI systems trained to understand and generate text — on a Linux system. The goal is to make it accessible to students and researchers who want to work with these models locally rather than relying on paid API services.
The tutorials cover three main stages: configuring the hardware and software environment, deploying models so you can actually chat with them, and fine-tuning — the process of adapting a general-purpose model to a specific task or style using your own data. Fine-tuning techniques covered include LoRA (a lightweight method that adjusts only a small portion of the model's parameters) and full-parameter training.
Over 50 open-source language models are supported, including models like LLaMA, ChatGLM, Qwen, InternLM, and DeepSeek. Each model has its own step-by-step guide. The tutorials are written primarily in Chinese and presented as Jupyter Notebooks — interactive documents that mix explanatory text and runnable code. You would use this project if you want hands-on experience running AI models on your own hardware without needing to pay for cloud AI API access.
Where it fits
- Run a private chatbot on your own hardware without paying for API calls.
- Fine-tune an open-source model on your own data to specialize it for a specific task.
- Learn how LLMs work by deploying and experimenting with them step-by-step.
- Set up a local AI development environment on Linux for research or prototyping.