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fine-tuning-with-trl

Fine-tune LLMs with TRL for SFT, DPO, and PPO/GRPO workflows.

Updated Apr 10, 2026
Search Tags:#llm#fine-tuning#transformers#ppo#dpo#trl#rlhf
One-click install
npx skills add https://github.com/overviewlabs/WHOX --skill fine-tuning-with-trl-overviewlabs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/overviewlabs/WHOX/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/overviewlabs/WHOX --skill fine-tuning-with-trl-overviewlabs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl, transformers, datasets, peft, accelerate, torch.

What problem does it solve?

Fine-tune LLMs to follow instructions and align them with human preferences using TRL methods (SFT, DPO, PPO/GRPO, and reward modeling) within the HuggingFace Transformers ecosystem.

Core Features & Use Cases

  • SFT-based instruction tuning using prompt-completion or chat templates with TRL.
  • DPO, PPO, and GRPO workflows for preference alignment and RLHF pipelines.
  • Reward-model training to support end-to-end RLHF.

Use cases include building instruction-following assistants, domain adaptation, and customizing models with human feedback.

Quick Start

Install TRL and Transformers, prepare your dataset, and run the appropriate trainer (SFT, DPO, PPO/GRPO) to start fine-tuning a base model.

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 an LLM with human preferences using HuggingFace Transformers?▼

Fine-tuning an LLM with TRL involves preparing a dataset and running the SFT, DPO, PPO/GRPO, or reward-model trainer to align a base model with human preferences. It requires installing trl, transformers, datasets, peft, accelerate, and torch.

What is the difference between SFT and DPO for instruction tuning?▼

SFT provides instruction tuning using prompt-completion or chat templates, while DPO, PPO, and GRPO execute preference alignment workflows. Reward-model training is also available to support end-to-end RLHF pipelines using TRL.

What dependencies do I need to run TRL for RLHF pipelines?▼

Running TRL for RLHF pipelines requires installing trl, transformers, datasets, peft, accelerate, and torch. These dependencies provide the environment needed to execute SFT, DPO, PPO/GRPO, and reward-model training workflows.

Can I use TRL for reward-model training in an end-to-end RLHF workflow?▼

TRL supports reward-model training to build end-to-end RLHF pipelines. You can train a reward model and use PPO or GRPO workflows to align LLMs with human preferences using HuggingFace Transformers.

When should I use GRPO instead of PPO for preference alignment?▼

TRL provides both GRPO and PPO workflows for preference alignment within RLHF pipelines. These methods allow you to align LLMs with human preferences, complementing SFT for instruction tuning and DPO for direct preference optimization.

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