fine-tuning-with-trl

Fine-tune language models with TRL for reinforcement learning and alignment.

Updated Oct 23, 2024

Search Tags:#fine-tuning #alignment #reinforcement learning #RLHF #language models

Ready to run

npx skills add https://github.com/lenadlm/docker --skill fine-tuning-with-trl-lenadlm

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive guide to fine-tuning language models using Transformer Reinforcement Learning (TRL), enabling users to align models with human preferences and optimize their performance through reinforcement learning techniques.

Core Features & Use Cases

Reinforcement Learning: Implement SFT, DPO, PPO, and GRPO for fine-tuning LLMs.
Reward Model Training: Train reward models to score completions based on human feedback.
RLHF: Full RLHF pipeline with SFT, reward model training, and PPO for reinforcement learning.
Use Case: Train a language model to generate coherent and accurate responses to prompts, aligned with human preferences and feedback.

Quick Start

To start fine-tuning a model with TRL, first install the required dependencies by running the following command:

pip install trl transformers datasets peft accelerate torch

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 a language model with reinforcement learning using TRL?▼

Fine-tune language models with TRL by applying SFT, DPO, PPO, and GRPO techniques to optimize performance and align completions with human preferences. The Skill provides scripts and references for implementing the full reinforcement learning pipeline.

What is the best way to implement RLHF for a language model?▼

The best way to implement RLHF is using the full TRL pipeline, which includes supervised fine-tuning, reward model training, and PPO reinforcement learning. This process aligns model responses with human feedback and preferences.

Do I need PyTorch and PEFT to run TRL for model alignment?▼

Yes, you need PyTorch and PEFT to run TRL for model alignment. The Skill also requires Transformers, Datasets, and Accelerate to execute the reinforcement learning scripts and optimize language models.

Can I train a reward model to score completions based on human feedback?▼

Yes, you can train a reward model to score completions based on human feedback. TRL provides dedicated reward model training as part of the RLHF pipeline to evaluate and align generated responses with human preferences.

What is the difference between SFT, DPO, and PPO in language model fine-tuning?▼

SFT, DPO, and PPO are distinct fine-tuning methods in TRL. SFT applies supervised learning, DPO uses direct preference optimization, and PPO applies reinforcement learning to train models based on a reward signal from human feedback.

Why use TRL over other tools for language model alignment?▼

Use TRL for language model alignment because it integrates directly with Transformers, PEFT, and Accelerate. It supports specialized reinforcement learning algorithms like PPO and GRPO to effectively optimize models for human preference tasks.