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fine-tuning-with-trl

Fine-tune HuggingFace Transformers models with TRL reinforcement learning pipelines.

Updated Sep 1, 2021
Search Tags:#fine-tuning#transformers#ppo#trl#rlhf#preference-alignment
One-click install
npx skills add https://github.com/unclehowell/unclehowell --skill fine-tuning-with-trl-unclehowell
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/unclehowell/unclehowell/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/unclehowell/unclehowell --skill fine-tuning-with-trl-unclehowell

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill enables developers to fine‑tune large language models using reinforcement learning techniques, turning generic pretrained LLMs into instruction‑following or preference‑aligned models without building complex pipelines from scratch.

Core Features & Use Cases

  • Supervised Fine‑Tuning (SFT) for instruction tuning with HuggingFace Transformers.
  • Direct Preference Optimization (DPO) for aligning models with chosen/rejected preference data.
  • PPO and GRPO pipelines for full RLHF, including reward‑model training and policy optimization.
  • End‑to‑end RLHF workflow covering SFT → Reward Model → PPO, suitable for chatbot or assistant development.

Quick Start

Use the TRL skill to fine‑tune a model on instruction data and align it with user preferences.

Frequently Asked Questions about fine-tuning-with-trl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is RLHF and how does it align LLMs with human preferences?▼

DPO aligns models directly using chosen and rejected preference data without requiring a separate reward model. This skill implements DPO pipelines via the TRL library to fine-tune large language models efficiently.

How do I fine-tune a HuggingFace Transformer for instruction following?▼

You can fine-tune a HuggingFace Transformer for instruction following using Supervised Fine-Tuning (SFT). This skill provides SFT pipelines via the TRL library to adapt pretrained models into instruction-tuned assistants.

Do I need PEFT and accelerate to run TRL pipelines?▼

The end-to-end RLHF workflow involves three stages: Supervised Fine-Tuning, Reward Model training, and PPO policy optimization. This skill uses TRL to guide models through this complete pipeline for chatbot or assistant development.

Can I use DPO instead of PPO for preference alignment?▼

Yes, you can use DPO instead of PPO for preference alignment. This skill supports Direct Preference Optimization pipelines within the TRL library to align models using chosen and rejected response pairs.

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