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fine-tuning-with-trl

Implement SFT, DPO, PPO, and GRPO workflows for language model alignment.

2|1|Updated Jul 14, 2026
Search Tags:#fine-tuning#reinforcement learning#ppo#dpo#trl#rlhf#grpo
One-click install
npx skills add https://github.com/heysuhas/hermes_cli --skill fine-tuning-with-trl-heysuhas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/heysuhas/hermes_cli/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/heysuhas/hermes_cli --skill fine-tuning-with-trl-heysuhas

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the challenge of aligning large language models with human preferences, ensuring they follow instructions accurately and behave safely without requiring manual, ad-hoc fine-tuning.

Core Features & Use Cases

  • Post-Training Methods: Supports SFT, DPO, PPO, and GRPO for comprehensive model alignment.
  • Reward Modeling: Enables the creation of custom reward models to score model generations based on specific criteria.
  • Use Case: A developer can use this skill to take a base model like Qwen and fine-tune it to follow complex reasoning chains or adhere to specific output formats using GRPO.

Quick Start

Use the fine-tuning-with-trl skill to initiate a supervised fine-tuning run on your instruction 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 large language model with reinforcement learning using HuggingFace transformers?▼

The framework supports SFT, DPO, PPO, and GRPO workflows for comprehensive post-training alignment. These methods enable preference alignment and instruction following without requiring manual, ad-hoc fine-tuning processes.

Can I use this to align a base model like Qwen to follow complex reasoning chains?▼

Yes, this framework supports the creation of custom reward models to score model generations based on specific criteria. This enables tailored reinforcement learning workflows for specialized alignment tasks.

What dependencies do I need to run RLHF workflows for language model alignment?▼

Supervised fine-tuning (SFT) is used for initial instruction tuning on your instruction dataset. Direct Preference Optimization (DPO) is then applied for preference alignment without needing a separate reward model.

What is the difference between PPO and GRPO for RLHF post-training?▼

You can start a supervised fine-tuning run by using the SFTTrainer class on your instruction dataset. This provides the foundational instruction following behavior before applying further preference alignment techniques.

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