fine-tuning-with-trl

Align large language models via reinforcement learning and preference-based fine-tuning workflows.

Updated May 9, 2026

Search Tags:#ppo #dpo #trl #rlhf #sft #grpo #llm fine-tuning

Ready to run

npx skills add https://github.com/pmcdowall/hermes-skills --skill fine-tuning-with-trl-pmcdowall

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/pmcdowall/hermes-skills/tree/main/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/pmcdowall/hermes-skills --skill fine-tuning-with-trl-pmcdowall

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps practitioners fine-tune and align large language models by providing structured workflows for supervised fine-tuning, preference optimization, and reinforcement learning from feedback.

Core Features & Use Cases

Post-Training Workflows: Supports SFT, DPO, PPO, GRPO, and reward model training with HuggingFace TRL.
Preference Alignment: Helps train models using chosen/rejected datasets, reward signals, and online reinforcement learning techniques.
Use Case: Build an instruction-following assistant by combining supervised tuning, preference alignment, and evaluation workflows while optimizing for limited GPU resources.

Quick Start

Use the fine-tuning-with-trl skill to train my language model with DPO using my preference dataset.

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 large language models using preference datasets?▼

To align large language models using preference datasets, you can apply DPO or PPO workflows to train models with chosen and rejected pairs. This enables preference optimization for instruction tuning.

What's the best way to start RLHF fine-tuning for an instruction-following assistant?▼

Start RLHF fine-tuning by combining supervised fine-tuning with reward model development and PPO optimization. This creates an instruction-following assistant aligned through reinforcement learning from feedback.

Can I use GRPO training for LLM post-training on limited GPU resources?▼

Yes, GRPO training is supported for LLM post-training on limited GPU resources. By utilizing PEFT and accelerate, you can configure scalable fine-tuning pipelines for reinforcement learning optimization.

Does HuggingFace TRL support supervised fine-tuning and reward model development?▼

HuggingFace TRL supports supervised fine-tuning and reward model development natively. It provides structured workflows for SFT, DPO, PPO, and GRPO to execute scalable reinforcement learning pipelines.

What frameworks do I need to configure scalable fine-tuning pipelines?▼

Configuring scalable fine-tuning pipelines requires TRL, transformers, datasets, PEFT, accelerate, and PyTorch. These dependencies enable reinforcement learning and preference-based alignment for large language models.

Why use DPO alignment instead of PPO optimization for LLM training?▼

Use DPO alignment instead of PPO optimization for LLM training to simplify preference optimization using chosen and rejected datasets. DPO bypasses explicit reward model development required by PPO.