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fine-tuning-with-trl

Post-train language models with SFT, DPO, RLOO, and GRPO using HuggingFace libraries.

1|Updated Jul 31, 2026
Search Tags:#llm#fine-tuning#alignment#dpo#trl#rlhf#grpo
One-click install
npx skills add https://github.com/icyzh/hermes-web --skill fine-tuning-with-trl-icyzh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/icyzh/hermes-web/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/icyzh/hermes-web --skill fine-tuning-with-trl-icyzh

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 complexity of aligning large language models with human preferences, providing a structured path for post-training tasks like instruction tuning and reinforcement learning.

Core Features & Use Cases

  • Preference Alignment: Implement DPO, RLOO, and GRPO to align models with human feedback or custom reward functions.
  • Instruction Tuning: Perform supervised fine-tuning (SFT) to improve model instruction-following capabilities.
  • Use Case: Use this skill to take a base model and train it to follow specific reasoning formats or to adhere to safety guidelines using preference datasets.

Quick Start

Use the fine-tuning-with-trl skill to initiate a GRPO training run on the provided dataset using the basic 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 fine-tune a large language model with RLHF and DPO?▼

To fine-tune a large language model with RLHF and DPO, you can use this framework to execute post-training pipelines for preference alignment and instruction tuning. It provides structured workflows integrating HuggingFace libraries.

What is the difference between SFT, DPO, and GRPO for language model alignment?▼

SFT performs supervised instruction tuning, while DPO and GRPO perform preference alignment using human feedback or custom reward functions. This framework supports all three methods to improve model instruction-following and reasoning formats.

Can I use HuggingFace transformers and accelerate for LLM post-training?▼

Yes, you can use HuggingFace transformers and accelerate for LLM post-training. This framework requires integration with these libraries, alongside datasets, peft, and torch, to execute its reinforcement learning training pipelines.

How do I align a base model with custom reward functions using GRPO?▼

You align a base model with custom reward functions using GRPO by initiating a training run through this framework's provided templates. GRPO supports preference optimization to enforce specific reasoning formats or safety guidelines.

Do I need peft and torch to run reinforcement learning workflows?▼

Yes, you need peft and torch to run reinforcement learning workflows. These are required dependencies alongside transformers, datasets, and accelerate to properly manage memory and execute the post-training pipelines.

When should I use supervised fine-tuning versus preference optimization for LLMs?▼

Use supervised fine-tuning to improve basic instruction-following capabilities, and use preference optimization like DPO or GRPO when aligning models with human feedback or custom reward functions to enforce safety guidelines.

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