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simpo-training

Optimize large language models for preference alignment without a reference model.

Updated Jul 13, 2026
Search Tags:#llm#machine learning#alignment#training#preference optimization#simpo
One-click install
npx skills add https://github.com/zangjeicy/Hermes --skill simpo-training-zangjeicy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/zangjeicy/Hermes/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/zangjeicy/Hermes --skill simpo-training-zangjeicy

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the complexity and computational overhead of traditional LLM alignment methods like DPO and PPO by providing a streamlined, reference-free optimization process.

Core Features & Use Cases

  • Reference-Free Optimization: Aligns models using preference pairs without the need for a secondary reference model, reducing memory and compute requirements.
  • Performance Gains: Delivers superior alignment results compared to DPO, with demonstrated improvements on benchmarks like AlpacaEval 2.0.
  • Use Case: Use this Skill to fine-tune a base model like Mistral 7B or Llama 3 on preference datasets to improve instruction-following capabilities while maintaining reasoning performance.

Quick Start

Execute the simpo training workflow using the provided accelerate configuration and your specific model training yaml file.

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 on a preference dataset without using a reference model?▼

SimPO provides reference-free LLM alignment by directly optimizing preference pairs, eliminating the need for a secondary reference model and reducing memory and compute overhead during training.

What is the best way to fine-tune Mistral 7B or Llama 3 for better instruction following?▼

Applying SimPO preference optimization to fine-tune base models like Mistral 7B or Llama 3 on preference datasets improves instruction-following capabilities while maintaining reasoning performance.

How does SimPO compare to DPO and PPO for large language model alignment?▼

SimPO outperforms DPO and PPO by streamlining reference-free preference optimization, delivering superior alignment results demonstrated by improvements on benchmarks like AlpacaEval 2.0.

Can I run SimPO preference optimization on a single-node hardware setup?▼

Yes, SimPO facilitates efficient, high-performance model alignment across various hardware configurations, explicitly supporting single-node setups using provided accelerate configurations and training files.

What dependencies do I need to run Simple Preference Optimization training?▼

You need torch, transformers, datasets, trl, and accelerate installed to execute the SimPO training workflow using the provided scripts and accelerate configuration files.

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