As a developer, I know how frustrating it can be to work with large language models (LLMs) on Windows. That’s why I decided to write a hands-on guide to fine-tuning LLMs with GRPO (Group-Relative PPO) locally on Windows, using Hugging Face’s TRL library.
## The Problem
Most guides and tutorials assume you’re working on Colab or Linux, leaving Windows users in the dark. But what if you want to experiment with reinforcement learning techniques on your own machine?
## The Solution
My guide focuses on creating a practical workflow that doesn’t require Colab or Linux. I’ve included a TRL-based implementation that runs on consumer GPUs, with LoRA and optional 4-bit quantization. Plus, I’ve developed a verifiable reward system that uses numeric, format, and boilerplate checks to create a more reliable training signal.
## Key Features
– **TRL-based implementation**: Runs on consumer GPUs with LoRA and optional 4-bit quantization.
– **Verifiable reward system**: Uses numeric, format, and boilerplate checks to create a more reliable training signal.
– **Automatic data mapping**: Simplifies preprocessing for most Hugging Face datasets.
– **Practical troubleshooting**: Configuration notes and troubleshooting tips for local setups.
## Get Started
If you’re interested in experimenting with reinforcement learning techniques on your own machine, check out my guide: Windows-friendly GRPO fine-tuning with TRL from zero to verifiable rewards.
You can also find the code on GitHub: Reinforcement-learning-with-verifable-rewards-Learnings/projects/trl-ppo-fine-tuning.
I’m open to any feedback, and I’d love to connect with others in the LLM and computer vision space.