HuggingFace Model Trainer

What problem does it solve?

This Skill streamlines the end-to-end process of training and fine-tuning large language models using HuggingFace's TRL, Transformers, and PEFT libraries, reducing setup time, complexity, and debugging effort.

Core Features & Use Cases

• Supports Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and PEFT-based methods like LoRA for parameter-efficient training.
• Provides training configuration templates, dataset preparation guidance, and evaluation tips for model alignment, instruction tuning, and domain adaptation.
• Use cases include adapting models to a specific domain, aligning model outputs to human preferences, and deploying fine-tuned models for downstream tasks.

Quick Start

Start with a base model id and a dataset; choose an SFT workflow for instruction tuning or a RLHF-style workflow (DPO/GRPO) for preference alignment, then run a training script and export the final model.