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fine-tuning-with-trl

Align language models with human preferences using SFT, DPO, PPO, and GRPO.

2|Updated May 22, 2026
Search Tags:#fine-tuning#instruction tuning#rlhf#language models#reforcement learning
One-click install
npx skills add https://github.com/519lab/thoth-agent --skill fine-tuning-with-trl-519lab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/519lab/thoth-agent/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/519lab/thoth-agent --skill fine-tuning-with-trl-519lab

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps improve language models by aligning them with human preferences and reinforcement learning, addressing issues like poor instruction-following and model quality.

Core Features & Use Cases

  • Post-Training Alignment: Fine-tune models based on human feedback (SFT, DPO, PPO, GRPO).
  • Reward Modeling: Train models to predict human preferences, enhancing their accuracy.
  • Use Case: Enhance a language model to better understand and follow instructions, such as generating more accurate summaries or improving conversation quality.

Quick Start

Fine-tune the Qwen2.5-0.5B-Instruct model for instruction-following using the SFTTrainer.

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 language model with human feedback using RLHF?▼

Fine-tune language models with human feedback using post-training alignment techniques like SFT, DPO, PPO, and GRPO. This method trains models to predict human preferences, improving instruction-following and overall model quality.

What is the best way to start instruction tuning for small models like Qwen?▼

Instruction tuning for models like Qwen2.5-0.5B-Instruct uses the SFTTrainer to enhance instruction-following capabilities. It provides a quick start for aligning language models with specific conversational or summarization tasks.

Do I need PEFT and accelerate to align language models with human preferences?▼

Yes, aligning language models with human preferences requires trl, transformers, datasets, peft, accelerate, and torch. These dependencies provide the necessary environment for reinforcement learning and efficient model training.

What is the difference between DPO and PPO for language model post-training?▼

DPO and PPO are both post-training alignment methods for language models. This Skill supports both along with SFT and GRPO, allowing you to choose the specific reinforcement learning technique that best fits your preference alignment scenario.

Why does my language model struggle with instruction-following after initial training?▼

Poor instruction-following occurs when models lack human preference alignment. Applying post-training reinforcement learning methods like SFT or DPO trains the model to better understand and execute specific instructions.

Can I train a reward model to predict human preferences for my custom dataset?▼

Yes, you can train a reward model to predict human preferences using your custom datasets. This enhances the accuracy of preference alignment when applying reinforcement learning to your language models.

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