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fine-tuning-with-trl

Fine-tune language models with Transformer Reinforcement Learning for human-aligned outputs.

Updated May 11, 2026
Search Tags:#fine-tuning#RLHF#transformers#language model#reward modeling
One-click install
npx skills add https://github.com/heximao/agents --skill fine-tuning-with-trl-heximao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/heximao/agents/tree/main/skill-archive/docker-hermes/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/heximao/agents --skill fine-tuning-with-trl-heximao

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 provides a comprehensive framework for fine-tuning language models to align with human preferences, enabling the creation of models that generate more accurate and contextually relevant responses.

Core Features & Use Cases

  • TRL Workflows: Offers multiple post-training methods (SFT, DPO, PPO, GRPO) for aligning language models.
  • Reward Modeling: Allows training reward models to predict human preferences.
  • RLHF Pipeline: Provides a full RLHF pipeline from base model to human-aligned model.
  • Use Cases: Ideal for tasks requiring high-quality, human-aligned text generation, such as content creation, customer service, and automated decision-making.

Quick Start

Train a language model to follow instructions using the TRL framework. First, install the required dependencies:

pip install trl transformers datasets peft accelerate

Then, train the model with the following code:

from trl import SFTTrainer

trainer = SFTTrainer(
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset,  # Prompt-completion pairs
)
trainer.train()

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 for human-aligned outputs?▼

Fine-tuning a language model for human-aligned outputs requires the Transformer Reinforcement Learning (TRL) framework to apply post-training methods like SFT, DPO, PPO, or GRPO to your base model.

What is the best way to build a reward model to predict human preferences?▼

Building a reward model to predict human preferences is achievable through the TRL framework, which provides dedicated reward modeling components to construct a full Reinforcement Learning from Human Feedback (RLHF) pipeline.

Do I need peft and accelerate to train models with TRL?▼

Yes, you need peft and accelerate along with trl, transformers, datasets, and torch to properly execute the SFTTrainer and run the post-training workflows for your language models.

Can I use SFTTrainer for instruction following on a base model?▼

Yes, you can use SFTTrainer for instruction following by configuring it with a base model like Qwen/Qwen2.5-0.5B and a dataset of prompt-completion pairs to train the model to follow instructions.

What is the difference between SFT and DPO when aligning language models?▼

When aligning language models, SFT (Supervised Fine-Tuning) directly trains on prompt-completion pairs, while DPO (Direct Preference Optimization) uses preference data to align models without explicitly training a separate reward model.

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