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fine-tuning-with-trl

Fine-tune language models with SFT, DPO, PPO, and GRPO.

Updated Jul 13, 2026
Search Tags:#fine-tuning#alignment#reinforcement learning#dpo#trl#rlhf#grpo
One-click install
npx skills add https://github.com/zeronx798/demo-hermes-agent --skill fine-tuning-with-trl-zeronx798
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/zeronx798/demo-hermes-agent/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/zeronx798/demo-hermes-agent --skill fine-tuning-with-trl-zeronx798

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the complexity of post-training and aligning language models, providing a structured environment to perform instruction tuning and preference alignment without reinventing the training pipeline.

Core Features & Use Cases

  • Full RLHF Pipeline: Supports the complete cycle of Supervised Fine-Tuning (SFT), Reward Modeling, and PPO reinforcement learning.
  • Preference Alignment: Implements modern alignment techniques like DPO and GRPO for memory-efficient optimization.
  • Use Case: Use this skill to take a base model like Qwen and align it to follow specific reasoning formats or human preferences using your own custom datasets.

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 large language model using DPO and GRPO workflows?▼

You can align large language models using DPO and GRPO workflows by applying reinforcement learning techniques to perform preference alignment and memory-efficient optimization on custom datasets.

What's the best way to run a full RLHF pipeline for a base model?▼

The best way to run a full RLHF pipeline is by executing the complete cycle of Supervised Fine-Tuning, Reward Modeling, and PPO reinforcement learning to optimize model policies based on human preference data.

Can I use peft and accelerate for memory-efficient fine-tuning with trl?▼

Yes, you can use peft and accelerate alongside transformers and datasets to execute training workflows on GPU-accelerated infrastructure for memory-efficient fine-tuning.

How do I create a reward model and optimize policies with custom reward functions?▼

To create a reward model and optimize policies, you integrate human preference data or custom reward functions into the training workflow to guide reinforcement learning optimization.

Does this reinforcement learning approach support instruction tuning for specific reasoning formats?▼

Yes, this reinforcement learning approach supports instruction tuning, allowing you to take a base model and align it to follow specific reasoning formats or human preferences using your own datasets.

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