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fine-tuning-with-trl

Fine-tune language models with DPO, RLOO, and GRPO training pipelines.

Updated Jun 25, 2026
Search Tags:#llm#fine-tuning#alignment#reinforcement learning#trl#rlhf#grpo
One-click install
npx skills add https://github.com/Rheasilvia/hermes-desktop --skill fine-tuning-with-trl-rheasilvia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/Rheasilvia/hermes-desktop/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/Rheasilvia/hermes-desktop --skill fine-tuning-with-trl-rheasilvia

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the challenge of aligning base language models with human preferences and specific instruction-following behaviors, which is essential for creating reliable, production-ready AI agents.

Core Features & Use Cases

  • Preference Alignment: Implement DPO, RLOO, and GRPO to steer model outputs toward desired outcomes without needing complex PPO setups.
  • Structured Reasoning: Use GRPO to enforce specific output formats like XML-based chain-of-thought reasoning.
  • Use Case: A developer can use this skill to fine-tune a base model to solve math problems by rewarding correct answers and proper XML formatting, significantly improving the model's reasoning accuracy.

Quick Start

Use the fine-tuning-with-trl skill to initiate a GRPO training run on the provided dataset using the basic grpo training template.

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 align a base LLM with human preferences using reinforcement learning?▼

To align a base LLM with human preferences, you can use reinforcement learning techniques like DPO, RLOO, and GRPO to steer model outputs toward desired outcomes without needing complex PPO setups. This skill implements these preference alignment workflows to create reliable AI agents.

What is the best way to fine-tune a model for structured reasoning and XML chain-of-thought outputs?▼

The best way to enforce structured reasoning like XML chain-of-thought outputs is by using GRPO during fine-tuning. GRPO rewards correct answers and proper formatting, significantly improving the model's reasoning accuracy and enforcing specific output formats.

Can I use trl and peft libraries for instruction tuning in a CUDA environment?▼

Yes, you can use trl and peft libraries for instruction tuning in a CUDA-enabled environment. This skill requires a CUDA setup along with transformers, trl, and peft to execute post-training pipelines for instruction tuning and reward modeling.

Do I need complex PPO setups to perform reward modeling and preference alignment?▼

You do not need complex PPO setups to perform reward modeling and preference alignment. This skill optimizes language models using alternative techniques like DPO, RLOO, and GRPO, which steer model outputs toward desired outcomes more simply.

How do I start a GRPO training run for fine-tuning a language model?▼

To start a GRPO training run for fine-tuning a language model, use the basic grpo training template provided by this skill on your target dataset. This initiates the reinforcement learning pipeline to optimize the model based on your defined rewards.

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