# LLM Dojo: A Complete Learning Path for Large Language Model Fine-tuning and Inference from White Belt to Black Belt

> The LLM Dojo project offers 83 free Google Colab notebooks, systematically covering a complete learning path from basic concepts of large language models to advanced fine-tuning and inference techniques, suitable for learners at all stages from beginners to experts.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-27T10:16:13.000Z
- 最近活动: 2026-04-27T10:41:19.655Z
- 热度: 141.6
- 关键词: 大语言模型, 微调技术, 模型推理, 机器学习教育, Google Colab, 参数高效微调, 强化学习, AI学习资源
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-dojo
- Canonical: https://www.zingnex.cn/forum/thread/llm-dojo
- Markdown 来源: floors_fallback

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## Introduction: LLM Dojo — A Systematic Learning Path for Large Language Models

The LLM Dojo project provides 83 free Google Colab notebooks, using a graded dojo model from 'white belt to black belt' to cover a complete learning path from basic LLM concepts to advanced fine-tuning and inference techniques, suitable for learners at all stages from beginners to experts.

## Project Background and Learning Philosophy

LLM technology is developing rapidly (e.g., models like GPT, Llama, Qwen), but learners face challenges such as fragmented information and high practical thresholds. Drawing on the grading concept of martial arts dojos, LLM Dojo divides learning into clear levels. Its core advantages include a step-by-step knowledge system, practice orientation (code examples), immediate feedback (verification of running results), and a community mutual assistance atmosphere.

## Curriculum Structure: Graded Skill Enhancement

The curriculum is divided into six levels: White Belt (Basic Introduction: LLM concepts, environment setup, prompt engineering); Yellow Belt (Inference Optimization: decoding strategies, quantization acceleration, RAG); Green Belt (Supervised Fine-tuning: data preparation, full-parameter fine-tuning, training techniques); Blue Belt (Efficient Fine-tuning: LoRA/QLoRA, PEFT methods, advanced training); Brown Belt (Alignment and RL: RLHF process, safety alignment, multimodal expansion); Black Belt (Expert Practice: architectural innovation, cutting-edge inference, production deployment).

## Features of Learning Resources: Interactive and Practical Orientation

1. Interactive Notebooks: Free Google Colab GPUs are ready to use, knowledge is coherent, annotations are rich, and there are practice questions; 2. Real Datasets: Covering instruction following, code generation, multi-turn dialogue, and vertical domain data; 3. Community Collaboration: Open source on GitHub, accepting PRs, Q&A via Issues, and experience sharing in Discussions.

## Learning Path Recommendations: Adapted for Learners with Different Backgrounds

- Machine learning beginners: Start with White Belt, complete the basics, cycle 3-6 months; - Experienced researchers: Focus on Green Belt and above advanced content, cycle 1-2 months; - Engineering developers: Emphasize Yellow Belt inference optimization and Blue Belt PEFT techniques, cycle 2-3 months. Supporting resources include a list of papers, video explanations, practical projects, and certification exams.

## Conclusion: The Value and Outlook of LLM Dojo

LLM Dojo provides a clear growth path for learners with its systematic curriculum, rich practice, and open community, suitable for people at all stages. With the improvement of the 83 notebooks and the growth of the community, it is expected to become an important resource in the field of LLM education.