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trl-fine-tuning

Implement RLHF fine-tuning workflows for LLMs using HuggingFace Transformers and TRL.

247|22|Updated Dec 11, 2024
Search Tags:#huggingface#dpo#trl#rlhf#preference alignment#llm fine-tuning#reward modeling
One-click install
npx skills add https://github.com/graniet/kheish --skill trl-fine-tuning-graniet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trl-fine-tuning
Source: https://github.com/graniet/kheish/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/graniet/kheish --skill trl-fine-tuning-graniet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the complexity of building custom reinforcement learning fine-tuning pipelines for large language models, letting you align models with human preferences, optimize for reward signals, and improve instruction following without writing hundreds of lines of boilerplate training code.

Core Features & Use Cases

  • Full RLHF pipeline support: Guides you through end-to-end workflows from supervised fine-tuning (SFT) to reward model training and PPO/GRPO reinforcement learning for production-ready aligned models.
  • Simplified preference alignment: Enables direct preference optimization (DPO) using chosen/rejected response pairs, eliminating the need for a separate reward model for fast, effective alignment.
  • Memory-efficient training options: Includes GRPO and LoRA configuration guidance to fine-tune 7B+ parameter models on consumer GPUs with limited VRAM.
  • Use Case: For example, if your customer support chatbot gives generic or unhelpful responses, use this Skill to fine-tune it on your internal support ticket data and align it with your team's preferred response style using DPO.

Quick Start

Use the trl-fine-tuning skill to fine-tune your base LLM on your instruction-following dataset with SFT and align it with your human preference data using DPO.

Frequently Asked Questions about trl-fine-tuning

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

FAQPage Schema
How do I fine-tune an LLM with HuggingFace TRL for RLHF?▼

You can fine-tune LLMs with HuggingFace TRL by running supervised fine-tuning, training a reward model, and applying PPO or GRPO reinforcement learning to align model outputs with human preferences through configurable training pipelines.

What is the difference between DPO and PPO for preference alignment?▼

DPO aligns models directly using chosen and rejected response pairs without training a separate reward model, while PPO requires an explicit reward model to optimize the LLM through reinforcement learning for preference alignment.

Can I use LoRA to fine-tune a 7B parameter model on a consumer GPU?▼

Yes, you can use LoRA and GRPO configuration to fine-tune 7B+ parameter models on consumer GPUs with limited VRAM, applying memory-efficient training to reduce the hardware requirements for large language model alignment.

When should I use DPO loss variants for fine-tuning?▼

DPO loss variants support robust preference alignment across different data quality scenarios, allowing you to adapt the direct preference optimization process when your chosen and rejected response pairs contain noisy or inconsistent human preference data.

How do I align a customer support chatbot with my team's preferred response style?▼

You can align a chatbot by fine-tuning the base LLM on internal support ticket data using supervised fine-tuning, then applying direct preference optimization with DPO to match your team's chosen response style.

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