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fine-tuning-with-trl

Automate RLHF fine-tuning of large language models with TRL workflows.

Updated Apr 30, 2026
Search Tags:#llm#fine-tuning#transformers#dpo#trl#rlhf#reward-model
One-click install
npx skills add https://github.com/photonics-dhl/Hermes --skill fine-tuning-with-trl-photonics-dhl
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/photonics-dhl/Hermes/tree/main/hermes-home/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/photonics-dhl/Hermes --skill fine-tuning-with-trl-photonics-dhl

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Fine-tune large language models using TRL-based reinforcement learning workflows to align models with human preferences, enabling safer and more capable instruction-following.

Core Features & Use Cases

  • SFT for instruction tuning on domain data
  • DPO, PPO, and GRPO pipelines for RLHF with reward modeling
  • HuggingFace Transformers compatibility for seamless integration
  • Domain adaptation and safety-aligned behavior

Quick Start

Provide your base model and an instruction dataset, then run the TRL-based fine-tuning pipeline to train an RLHF-aligned 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 an LLM with RLHF using HuggingFace Transformers?▼

You can fine-tune an LLM with RLHF by running TRL-based reinforcement learning pipelines like DPO, PPO, and GRPO to align HuggingFace Transformers models with human preferences for safer instruction-following.

What's the best way to align a large language model with human preferences?▼

Aligning a large language model with human preferences is best achieved through TRL workflows that apply SFT for instruction tuning and DPO or PPO pipelines to incorporate reward modeling for domain-specific adaptation.

Can I use DPO and PPO pipelines for domain adaptation in TRL?▼

Yes, you can use DPO and PPO pipelines within TRL to perform domain adaptation and safety-aligned behavior tuning, applying reinforcement learning from human feedback to tailor base models to specific instruction datasets.

Do I need PyTorch and PEFT to run SFT instruction tuning with TRL?▼

Yes, you need PyTorch and PEFT along with TRL, transformers, datasets, and accelerate to satisfy dependencies and configurations required to run SFT instruction tuning and RLHF workflows effectively.

What is GRPO and when should I use it for RLHF fine-tuning?▼

GRPO is a reinforcement learning pipeline available in TRL for RLHF fine-tuning. You should use GRPO alongside SFT, DPO, and PPO to align large language models with human preferences and improve instruction-following capabilities.

Does TRL support reward modeling for safety alignment of LLMs?▼

Yes, TRL supports reward modeling for safety alignment of LLMs by providing end-to-end reinforcement learning workflows that automate the alignment of HuggingFace Transformers models with human preferences.

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