This project provides an accessible, beginner-friendly tutorial on model compression techniques like pruning, quantization, and knowledge distillation, aimed at researchers, developers, and students interested in deploying AI models efficiently. It addresses the high resource demands of large models, offering practical code examples to demystify these optimization methods.

How It Works

The tutorial breaks down complex model compression concepts into easy-to-understand theoretical content, complemented by practical code implementations. It draws inspiration from MIT's TinyML curriculum, structuring the learning path from foundational CNN concepts to advanced techniques and project-based applications. This approach aims to lower the barrier to entry for learning and applying model compression.

Quick Start & Requirements

• Practice Environment: Python 3.10. Detailed installation instructions are in INSTALL.md.
• Local Docs: Requires Node.js v16 and docsify-cli. Install with npm i docsify-cli -g, then serve with docsify serve ./docs.
• Online Reading: Available at https://datawhalechina.github.io/awesome-compression.

Highlighted Details

• Covers core compression methods: Pruning, Quantization, Neural Architecture Search, Knowledge Distillation.
• Includes practical code examples for hands-on learning.
• Structured curriculum from introduction to project practice.
• Aims to be a comprehensive Chinese-language introductory resource.

Maintenance & Community

The project is a Datawhale initiative, with contributions from university researchers and industry engineers. Community engagement is encouraged via GitHub Issues and Discussions.

Licensing & Compatibility

Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). This license restricts commercial use and requires derivative works to be shared under the same terms.

Limitations & Caveats

The project focuses on introductory concepts and practical application for beginners. Advanced users or those requiring commercial deployment might need to explore more specialized or permissively licensed resources.