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Mini-LLM-RL Library

Acknowledgments

A big thank you to the author of the Mini-R1: Reproduce Deepseek R1 „Aha Moment“ – A Reinforcement Learning Tutorial for the excellent article that inspired this project.

Overview

I have transformed the concepts from the tutorial into a robust library, utilizing uv for cleaner and more efficient dependency management. This library is optimized to run on macOS Sonoma (and later versions) using CPU, ensuring compatibility and ease of use for developers working in a macOS environment.

Features

• Library Implementation: Converts the tutorial into a reusable library format.
• Dependency Management: Leverages uv for streamlined and maintainable dependencies.
• macOS Compatibility: Specifically optimized to work on macOS Sonoma and newer versions using CPU.

Installation

1. Clone the Repository

   git clone https://github.com/Guo-astro/mini-llm-rl
   cd mini-llm-rl
   uv sync

Usage

See main.py for an example of how to use the library.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.

License

MIT License