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fine-tuning-with-trl

Fine-tune HuggingFace language models with TRL using SFT, DPO, PPO, or GRPO.

Updated Feb 21, 2026
Search Tags:#llm#fine-tuning#huggingface#dpo#trl#rlhf#grpo
One-click install
npx skills add https://github.com/Gitnapp/Skills --skill fine-tuning-with-trl-gitnapp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/Gitnapp/Skills/tree/main/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/Gitnapp/Skills --skill fine-tuning-with-trl-gitnapp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps practitioners fine-tune and align large language models by providing structured workflows for supervised tuning, preference optimization, and reinforcement learning methods without building the entire training pipeline from scratch.

Core Features & Use Cases

  • RLHF Training Workflows: Guides SFT, reward model training, PPO, and GRPO pipelines for human preference alignment.
  • Preference Optimization: Covers DPO and related variants for training models from chosen and rejected response pairs.
  • Use Case: Apply this Skill when adapting HuggingFace language models for instruction following, domain alignment, reasoning optimization, or custom reward-driven behaviors.

Quick Start

Use the fine-tuning-with-trl skill to train an LLM with TRL using SFT, DPO, PPO, or GRPO methods for a custom alignment task.

Frequently Asked Questions about fine-tuning-with-trl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a HuggingFace language model with DPO?▼

To fine-tune a HuggingFace language model with DPO, you need a preference dataset of chosen and rejected response pairs. This Skill provides structured workflows to apply Direct Preference Optimization for training models without building the pipeline from scratch.

What's the difference between SFT and GRPO for LLM alignment?▼

SFT (Supervised Fine-Tuning) adapts models using instructional input-output pairs, whereas GRPO is a reinforcement learning method that optimizes responses via reward models. This Skill provides structured workflows for both SFT and GRPO pipelines.

Can I use TRL for PPO training on custom reward models?▼

Yes, you can use TRL for PPO training with custom reward models. This Skill guides the complete RLHF workflow, including reward model training and Proximal Policy Optimization pipelines for human preference alignment.

Do I need a preference dataset for RLHF training workflows?▼

Yes, preference datasets containing chosen and rejected response pairs are required for RLHF training workflows. This Skill requires preference datasets to configure reward optimization for SFT, DPO, PPO, and GRPO methods.

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