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fine-tuning-with-trl

Align language models with human preferences using TRL workflows.

Updated Jun 9, 2026
Search Tags:#fine-tuning#reinforcement learning#TRL#language models#reward modeling
One-click install
npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill fine-tuning-with-trl-zhouboyu-xreal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/zhouboyu-xreal/Hermes-Memory/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill fine-tuning-with-trl-zhouboyu-xreal

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 aligning language models with human preferences, enabling fine-tuning and reinforcement learning for improved model performance.

Core Features & Use Cases

  • Post-Training Methods: Offers a suite of post-training methods for aligning language models with human preferences.
  • TRL Workflows: Provides workflows for Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), and Group Relative Policy Optimization (GRPO).
  • Reward Modeling: Enables the training of reward models for evaluating and improving model outputs.
  • Use Case: For a business looking to improve its AI chatbot's responses, this Skill can be used to fine-tune the chatbot using human feedback and preference data.

Quick Start

Use the fine-tuning-with-trl skill to train a model to follow instructions using the Qwen/Qwen2.5-0.5B model and a custom instruction dataset.

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 human preferences?▼

Fine-tune a language model with human preferences using Transformer Reinforcement Learning (TRL) by applying post-training methods like DPO or PPO to align model outputs with human feedback data.

What's the best way to align a chatbot using reinforcement learning?▼

Align a chatbot using reinforcement learning by training a reward model on human preference data, then applying Proximal Policy Optimization (PPO) to adjust the language model's responses based on the learned rewards.

Can I use PEFT and accelerate for reward modeling?▼

Yes, you can use PEFT and accelerate for reward modeling. This Skill requires both libraries alongside trl and transformers to efficiently train reward models for evaluating and improving language model outputs.

Does this support Direct Preference Optimization and GRPO workflows?▼

Yes, this supports Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) workflows, alongside Supervised Fine-Tuning (SFT) and PPO for comprehensive language model alignment.

What libraries do I need to run TRL workflows for language models?▼

You need trl, transformers, datasets, peft, accelerate, and torch to run TRL workflows for fine-tuning and aligning language models with human preferences.

How do I train a model to follow instructions using a custom dataset?▼

Train a model to follow instructions by applying Supervised Fine-Tuning (SFT) through TRL workflows, using a custom instruction dataset with a base model like Qwen2.5-0.5B to learn instruction-following behavior.

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