skills.rest
ruiyangruiyi
ruiyangruiyiCOMMUNITY
Explore all skills by this author →
GitHub

fine-tuning-with-trl

Fine-tune large language models with TRL methods including SFT, DPO, PPO, GRPO, and reward modeling.

Updated May 26, 2026
Search Tags:#ppo#dpo#trl#sft#grpo#llm alignment#reward modeling
One-click install
npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill fine-tuning-with-trl-ruiyangruiyi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/ruiyangruiyi/hermes-agent/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill fine-tuning-with-trl-ruiyangruiyi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the problem of aligning large language models to human preferences and target behaviors by providing practical TRL training recipes for SFT, DPO, PPO, GRPO, and reward modeling.

Core Features & Use Cases

  • Supervised Fine-Tuning (SFT): Train instruction-following behavior from prompt-completion datasets (e.g., turning a base model into an instruction-tuned model).
  • Preference Alignment (DPO): Use chosen/rejected pairs to align without training a separate reward model.
  • Reinforcement Learning Alignment (PPO/GRPO): Optimize policies using either a learned reward model (PPO) or direct group-relative rewards without a reward model (GRPO).
  • Reward Modeling: Train a reward model that predicts which completion is preferred to enable PPO-style RLHF.

Quick Start

Use DPO to align an instruct model with preference pairs by running a TRL DPO training command with your model checkpoint, preference dataset, and output directory.

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 align a large language model using DPO with preference pairs?▼

DPO aligns large language models using chosen and rejected preference pairs without training a separate reward model. You can run a TRL DPO training command with your model checkpoint, preference dataset, and output directory to achieve preference-based alignment.

What is the difference between PPO and GRPO for reinforcement learning alignment?▼

PPO optimizes policies using a learned reward model, whereas GRPO applies direct group-relative rewards without requiring a separate reward model. Both are supported TRL methods for reward-driven optimization in RLHF post-training pipelines.

How do I turn a base model into an instruction-tuned model?▼

Supervised Fine-Tuning (SFT) trains instruction-following behavior from prompt-completion datasets. You can use TRL SFT to transform a base model into an instruction-tuned model by providing TRL-compatible prompt-completion datasets.

Do I need a separate reward model for PPO-style RLHF?▼

Yes, PPO-style RLHF requires training a reward model to predict which completion is preferred. You can train a reward model using TRL with appropriate model heads and preference datasets to enable subsequent PPO optimization.

Can I use PEFT for efficient fine-tuning with TRL methods?▼

Yes, TRL supports Hugging Face tooling including PEFT for efficient fine-tuning across SFT, DPO, PPO, and GRPO methods. You can apply PEFT alongside transformers and datasets to optimize resource usage during LLM alignment.

What dataset formats are required for LLM alignment with TRL?▼

TRL requires TRL-compatible datasets formatted as either prompt-completion pairs for SFT or chosen/rejected pairs for DPO and reward modeling. These formats are necessary to properly execute instruction tuning and preference-based alignment.

Related Skills

Explore More Software Engineering Skills→
ricablericable

fine-tuning-with-trl

Fine-tune large language models with TRL for supervised and preference alignment.

Community
Advanced
vuralserhat86vuralserhat86
43

model_finetuning

Fine-tune LLMs with SFT, DPO, and PPO/GRPO using HuggingFace Transformers and datasets.

Community
Advanced
twjarviszyra-webtwjarviszyra-web
3

fine-tuning-with-trl

Train and align language models with TRL using supervised fine-tuning and preference optimization.

Community
Advanced
devMoezdevMoez
1

fine-tuning-with-trl

Automates LLM post-training alignment with TRL using SFT, DPO, and PPO/GRPO workflows.

Community
Advanced