hugging-face-model-trainer

What problem does it solve?

This Skill simplifies the process of training and fine-tuning large language models (LLMs) by leveraging Hugging Face's managed cloud infrastructure, eliminating the need for local GPU setup and complex environment management.

Core Features & Use Cases

• Cloud-Based Training: Train models on Hugging Face Jobs infrastructure without requiring local GPUs.
• Multiple Training Methods: Supports SFT, DPO, GRPO, and reward modeling using the TRL library.
• GGUF Conversion: Converts trained models to GGUF format for local deployment with tools like Ollama and LM Studio.
• Use Case: You want to fine-tune a Qwen2.5 model on a custom dataset using DPO. This Skill allows you to submit the training job directly to Hugging Face Jobs, monitor its progress, and automatically save the resulting model to the Hugging Face Hub.

Quick Start

Use the hugging face model trainer skill to fine-tune the Qwen2.5-0.5B model using SFT on the trl-lib/Capybara dataset for 3 epochs.