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fine-tuning-with-trl

Fine-tune HuggingFace Transformers language models with RLHF-style SFT, DPO, PPO/GRPO, and reward-model workflows.

2|2|Updated Apr 16, 2026
Search Tags:#fine-tuning#ppo#dpo#trl#rlhf#sft#grpo
One-click install
npx skills add https://github.com/huidge/hermes-skills --skill fine-tuning-with-trl-huidge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/huidge/hermes-skills/tree/main/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/huidge/hermes-skills --skill fine-tuning-with-trl-huidge

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Fine-tune large language models using reinforcement learning from human feedback (RLHF) with TRL, enabling alignment to human preferences and task-specific objectives.

Core Features & Use Cases

  • SFT for instruction tuning to build strong base capabilities.
  • DPO for direct preference alignment without a separate reward model.
  • PPO/GRPO for reward optimization and efficient policy updates.
  • Reward-model training to support full RLHF pipelines.
  • HuggingFace Transformers compatibility with end-to-end templates and references.
  • Production-ready GRPO templates and workflow references for real-world deployments.

Quick Start

Run the GRPO-based training template to kick off RLHF-style fine-tuning with 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 a language model with RLHF using HuggingFace Transformers?▼

Run DPO for direct preference alignment without training a separate reward model, or use PPO/GRPO templates for full reward optimization workflows to align model outputs with human preferences.

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

SFT provides instruction tuning to build strong base capabilities, while DPO performs direct preference alignment to human feedback without the computational overhead of training a separate reward model.

Do I need PEFT and accelerate to run TRL training workflows?▼

Yes, the TRL training workflows enforce dependencies on PEFT and accelerate, alongside trl, transformers, datasets, and torch, to efficiently manage memory and distribute production-ready GRPO training.

Can I train a reward model from scratch for a custom RLHF pipeline?▼

Yes, you can train a custom reward model to support full RLHF pipelines, which then integrates with the PPO or GRPO templates to optimize the language model policy against the learned reward signals.

What's the best way to start GRPO training for a base model?▼

The best way to start GRPO training is to run the provided production-ready GRPO template, which kicks off RLHF-style fine-tuning directly on a base model using the configured TRL and Transformers environment.

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