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simpo-training

Optimize LLM alignment with reference-free SimPO preference optimization.

2|Updated Feb 4, 2026
Search Tags:#fine-tuning#deep learning#nlp#preference optimization#simpo#llm alignment
One-click install
npx skills add https://github.com/zhuangbiaowei/smart_bot --skill simpo-training-zhuangbiaowei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/simpo
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill simpo-training-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a simpler and more efficient method for aligning Large Language Models (LLMs) using preference data, eliminating the need for a reference model required by methods like DPO.

Core Features & Use Cases

  • Reference-Free Preference Optimization: Train LLMs using only chosen and rejected response pairs, making the process more accessible and computationally lighter.
  • Improved Performance: Achieves better results than DPO on benchmarks like AlpacaEval 2.0.
  • Use Case: Fine-tune a base LLM to follow user instructions more accurately and safely by training it on human-ranked response pairs, without the overhead of maintaining a separate reference model.

Quick Start

Use the simpo training skill to fine-tune the Mistral 7B model using the HuggingFaceH4/ultrafeedback_binarized dataset.

Frequently Asked Questions about simpo-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I align an LLM with preference data without using a reference model?▼

SimPO is a reference-free preference optimization algorithm for LLM alignment that eliminates the reference model required by DPO. It directly optimizes a policy model against preference data, simplifying the alignment pipeline while reducing computational overhead.

What is the difference between SimPO and DPO for fine-tuning large language models?▼

SimPO differs from DPO by removing the need for a reference model during preference optimization. SimPO directly optimizes the policy model against preference data, simplifying the training pipeline and achieving better results on benchmarks like AlpacaEval 2.0.

Can I use the Hugging Face transformers library to train a model with SimPO?▼

Yes, you can use SimPO with the Hugging Face ecosystem. The implementation relies on dependencies including transformers, datasets, and TRL to fine-tune base models like Mistral 7B using preference datasets such as HuggingFaceH4/ultrafeedback_binarized.

How do I configure hyperparameters for SimPO preference optimization?▼

SimPO supports configurable hyperparameters for fine-tuning across various model sizes and datasets. You can adjust these settings within the training scripts to optimize the policy model's behavior based on the specific chosen and rejected response pairs in your preference data.

Do I need flash-attention installed to run SimPO training scripts?▼

Flash-attn is listed as a dependency for the SimPO training Skill. You need it installed along with torch, transformers, datasets, TRL, and accelerate to ensure the reference-free preference optimization runs efficiently across your hardware environment.

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