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fine-tuning-with-trl

Fine-tune large language models with reinforcement learning and human feedback.

4|Updated May 14, 2026
Search Tags:#fine-tuning#LLM#reinforcement learning#RLHF#language model
One-click install
npx skills add https://github.com/liumeixin/hermes-skills --skill fine-tuning-with-trl-liumeixin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/liumeixin/hermes-skills/tree/main/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/liumeixin/hermes-skills --skill fine-tuning-with-trl-liumeixin

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 addresses the challenge of fine-tuning language models to better follow instructions and align with human preferences, enabling more accurate and contextually relevant responses.

Core Features & Use Cases

  • Instruction Tuning: Improve model performance by training on human-provided instruction-follower data.
  • Preference Alignment: Train models to align with human preferences using preference data like chosen/rejected pairs.
  • RLHF Pipeline: Offers a complete pipeline for RLHF, including SFT, reward model training, and PPO reinforcement learning.
  • Use Case: Utilize this Skill to fine-tune a language model for specific tasks that require alignment with human preferences, such as creating personalized content or answering customer queries.

Quick Start

Train the language model using the fine-tuning-with-trl skill with the following command:

python train.py

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 large language model with reinforcement learning and human feedback?▼

Fine-tuning a large language model with RLHF involves a pipeline of supervised fine-tuning, reward model training, and PPO reinforcement learning to improve instruction following and preference alignment. This Skill provides the complete pipeline for such training.

What is preference alignment and when do I need it for LLM training?▼

Preference alignment trains language models to align with human preferences using chosen/rejected data pairs. You need it when your model must generate contextually relevant responses for tasks requiring specific instruction following, such as personalized content generation or customer service.

Do I need peft and accelerate to run the RLHF pipeline?▼

Yes, the RLHF pipeline requires Python libraries including peft and accelerate, alongside trl, transformers, datasets, and torch. These dependencies provide the necessary environment for efficient model training and reinforcement learning operations.

What's the best way to start instruction tuning using transformers and trl?▼

The best way to start instruction tuning is by running the training script with Python. This Skill leverages transformers and trl to train the language model on human-provided instruction-follower data, improving model performance for specific tasks.

Can I use this approach for both reward model training and PPO reinforcement learning?▼

Yes, this approach supports both reward model training and PPO reinforcement learning within a complete RLHF pipeline. It enables training models to align with human preferences by utilizing these reinforcement learning techniques sequentially.

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