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fine-tuning-with-trl

Fine-tune language models with SFT, DPO, PPO, and GRPO methods.

Updated Jul 13, 2026
Search Tags:#llm#fine-tuning#alignment#dpo#trl#rlhf#grpo
One-click install
npx skills add https://github.com/zangjeicy/Hermes --skill fine-tuning-with-trl-zangjeicy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/zangjeicy/Hermes/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/zangjeicy/Hermes --skill fine-tuning-with-trl-zangjeicy

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the complexity of aligning language models with human preferences, providing a structured path for post-training tasks like instruction tuning and reinforcement learning.

Core Features & Use Cases

  • Post-Training Pipelines: Execute full RLHF workflows including SFT, Reward Modeling, and PPO.
  • Preference Alignment: Implement DPO and GRPO to align models with human feedback or custom reward functions.
  • Use Case: Use this skill to fine-tune a base model on instruction-following data or to optimize a model's reasoning capabilities using memory-efficient GRPO.

Quick Start

Use the fine-tuning-with-trl skill to initiate a supervised fine-tuning run on your dataset using the SFTTrainer class.

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 using TRL?▼

You can fine-tune a language model with human preferences using TRL by executing full RLHF workflows, including supervised fine-tuning, reward modeling, and PPO, to align model outputs with custom reward functions.

What's the best way to implement DPO and GRPO for LLM alignment?▼

To implement DPO and GRPO for LLM alignment, use this framework to apply preference optimization techniques, aligning language models with human feedback or optimizing reasoning capabilities using memory-efficient pipelines.

Can I use SFTTrainer for instruction tuning on a custom dataset?▼

Yes, you can use the SFTTrainer class to initiate supervised fine-tuning runs on your custom instruction-following datasets, enabling post-training alignment for base language models.

Does this framework support memory-efficient training pipelines for large language models?▼

Yes, this framework supports memory-efficient training pipelines for large language models, facilitating the creation of custom reward functions and allowing GRPO to optimize model reasoning under memory constraints.

When should I use GRPO instead of PPO for reinforcement learning?▼

Use GRPO instead of PPO when you need to optimize a model's reasoning capabilities using memory-efficient pipelines, whereas PPO is typically utilized for full RLHF workflows with reward modeling.

Do I need peft and accelerate to run post-training pipelines?▼

Yes, you need peft and accelerate alongside transformers, datasets, and torch to execute memory-efficient post-training pipelines for language model alignment and instruction tuning.

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