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fine-tuning-with-trl

Fine-tune LLMs with TRL using SFT, DPO, PPO, and GRPO.

Updated Aug 27, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#trl#rlhf#preference alignment
One-click install
npx skills add https://github.com/kwasi-cpu/hermes-agent --skill fine-tuning-with-trl
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/kwasi-cpu/hermes-agent/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/kwasi-cpu/hermes-agent --skill fine-tuning-with-trl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl, transformers, datasets, peft, accelerate, torch, and includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of aligning Large Language Models (LLMs) with human preferences and instructions, making them more helpful, harmless, and honest.

Core Features & Use Cases

  • Supervised Fine-Tuning (SFT): Instruction tuning for better response generation.
  • Preference Alignment (DPO): Directly optimize models using preference data without a separate reward model.
  • Reinforcement Learning (PPO/GRPO): Further refine models using reinforcement learning techniques for advanced alignment.
  • Reward Model Training: Train models to score the quality of LLM generations.
  • Use Case: You have a base LLM and want to make it better at following user instructions and adhering to specific safety guidelines. This Skill provides the tools to fine-tune the model using various techniques, including human feedback.

Quick Start

Use the fine-tuning-with-trl skill to perform supervised fine-tuning on a base model using the provided dataset.

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 large language model with human preferences using reinforcement learning?▼

You can fine-tune large language models with human preferences by applying reinforcement learning techniques like PPO, DPO, and GRPO from the TRL library to align base models with specific instructions and safety guidelines.

What is the difference between DPO and PPO for preference alignment in LLMs?▼

DPO directly optimizes models using preference data without requiring a separate reward model, whereas PPO uses reinforcement learning to further refine models by integrating a trained reward model to score generation quality.

Can I use HuggingFace Transformers and Datasets for supervised fine-tuning?▼

Yes, supervised fine-tuning integrates directly with HuggingFace Transformers and Datasets to perform instruction tuning, enabling better response generation from base large language models using your provided datasets.

Do I need separate reward model training before applying RLHF to my base model?▼

Reward model training is supported to score the quality of LLM generations for PPO, but you can bypass it by using Direct Preference Optimization to align models directly from preference data without a separate reward model.

What dependencies are required to run TRL for LLM alignment?▼

To run TRL for LLM alignment, you need the trl, transformers, datasets, peft, accelerate, and torch dependencies installed in your environment to support supervised fine-tuning, preference optimization, and reinforcement learning workflows.

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