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fine-tuning-with-trl

Fine-tunes LLMs with SFT, DPO, PPO, and GRPO using HuggingFace Transformers and PEFT.

3|Updated Feb 21, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#trl#rlhf#preference alignment
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill fine-tuning-with-trl-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/06-post-training/trl-fine-tuning
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill fine-tuning-with-trl-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables the fine-tuning of Large Language Models (LLMs) to align with human preferences and instructions, improving their helpfulness, honesty, and harmlessness.

Core Features & Use Cases

  • Supervised Fine-Tuning (SFT): Instruction tuning for better response generation.
  • Direct Preference Optimization (DPO): Align models with preferences without a separate reward model.
  • Reinforcement Learning (PPO/GRPO): Optimize models using reward signals for advanced alignment.
  • Reward Model Training: Create models that score the quality of LLM outputs.
  • Use Case: You have a base LLM that generates factually correct but sometimes unhelpful or biased responses. Use this Skill to fine-tune it using preference data to make it more aligned with desired conversational behavior.

Quick Start

Use the fine-tuning-with-trl skill to perform supervised fine-tuning on the Qwen/Qwen2.5-0.5B 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 align LLMs with human preferences using reinforcement learning?▼

Direct Preference Optimization (DPO) aligns LLMs with human preferences directly without training a separate reward model, whereas PPO and GRPO optimize models using reward signals for advanced alignment, and SFT focuses on instruction tuning.

Can I use PEFT for efficient fine-tuning of large language models with TRL?▼

Yes, you can use PEFT for efficient fine-tuning of large language models with TRL. The skill fully supports HuggingFace Transformers and PEFT to facilitate efficient model adaptation during SFT and DPO processes.

What is the best way to start instruction tuning a base model to fix unhelpful responses?▼

The best way to start instruction tuning is using Supervised Fine-Tuning (SFT) on a base model like Qwen2.5-0.5B with a dataset, adjusting its behavior to generate more helpful and aligned conversational responses.

How do I train a reward model to score LLM outputs?▼

You can train a reward model to score the quality of LLM outputs by using this skill's reinforcement learning capabilities, which explicitly facilitates reward model training for advanced preference alignment workflows.

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