trl

What problem does it solve?

This Skill eliminates the complexity of training and fine-tuning large language models (LLMs) on cloud GPUs, removing the need for local infrastructure setup. It also streamlines the critical step of converting trained models into GGUF format for efficient local deployment, saving significant time and reducing technical hurdles.

Core Features & Use Cases

• Cloud GPU Training: Automate fine-tuning of LLMs using TRL methods (SFT, DPO, GRPO, Reward Modeling) on Hugging Face Jobs, with no local GPU required.
• GGUF Conversion: Effortlessly convert your trained models to GGUF format, making them compatible with local inference tools like Ollama, LM Studio, and llama.cpp.
• Monitoring & Persistence: Integrates Trackio for real-time training progress monitoring and ensures all trained models are automatically saved to the Hugging Face Hub.
• Use Case: A machine learning engineer needs to fine-tune a 7B LLM with DPO on a proprietary dataset. Instead of provisioning cloud VMs or managing local GPU drivers, they can use this Skill to submit the training job, monitor its progress, and then convert the resulting model to a compact GGUF file for deployment on edge devices, all through simple commands.

Quick Start

Train a Qwen 0.5B model on the Capybara dataset using SFT on Hugging Face Jobs, and monitor its progress with Trackio. Convert my fine-tuned model 'username/my-finetuned-model' to GGUF format for local deployment.