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fine-tuning-with-trl

Fine-tune language models with SFT, DPO, and reinforcement learning.

Updated May 9, 2026
Search Tags:#human feedback#fine-tuning#reinforcement learning#language model#preference alignment
One-click install
npx skills add https://github.com/robertbr123/Linket-Agent --skill fine-tuning-with-trl-robertbr123
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/robertbr123/Linket-Agent/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/robertbr123/Linket-Agent --skill fine-tuning-with-trl-robertbr123

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 in fine-tuning language models to align with human preferences and perform specific tasks more effectively.

Core Features & Use Cases

  • Supervised Fine-Tuning (SFT): Train models on instruction-following data to improve their ability to follow commands.
  • Direct Preference Optimization (DPO): Align models with human preferences using chosen/rejected pairs of responses.
  • Reinforcement Learning (RL): Optimize models using reinforcement learning techniques like PPO and GRPO.
  • Use Case: Imagine you have a language model that generates text. Use this Skill to fine-tune it to produce more accurate and relevant responses based on human feedback.

Quick Start

Run the following command to fine-tune the model with supervised fine-tuning:

python -m trl.scripts.sft \
  --model_name_or_path Qwen/Qwen2.5-0.5B \
  --train_dataset instruction_dataset \
  --tokenizer tokenizer \
  --output_dir output_dir

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 TRL?▼

To fine-tune a language model with human feedback using TRL, apply supervised fine-tuning, direct preference optimization, or reinforcement learning techniques like PPO and GRPO to align model responses with human preferences.

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

Direct preference optimization aligns models using chosen and rejected response pairs, while PPO applies reinforcement learning techniques to optimize the model based on reward signals from human feedback.

Can I use PEFT and accelerate for reinforcement learning fine-tuning?▼

Yes, you can use PEFT and accelerate for reinforcement learning fine-tuning. This Skill requires the trl, transformers, datasets, peft, accelerate, and torch libraries to optimize language models effectively.

When do I need supervised fine-tuning versus direct preference optimization?▼

Supervised fine-tuning trains models on instruction-following data to improve command execution, while direct preference optimization aligns models with human preferences using chosen and rejected pairs of responses.

What is the best way to align a language model with specific human preferences?▼

The best way to align a language model with specific human preferences is using direct preference optimization with chosen and rejected response pairs, or reinforcement learning techniques to optimize for desired outputs.

What are the limitations of using TRL for language model fine-tuning?▼

Limitations of using TRL for language model fine-tuning include the requirement of specific dependencies like trl, transformers, datasets, peft, accelerate, and torch, and the need for prepared instruction or preference datasets.

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