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simpo-training

Run reference-free preference optimization on large language models.

1|Updated Jul 31, 2026
Search Tags:#llm#machine learning#alignment#training#preference optimization#simpo
One-click install
npx skills add https://github.com/icyzh/hermes-web --skill simpo-training-icyzh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/icyzh/hermes-web/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/icyzh/hermes-web --skill simpo-training-icyzh

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

SimPO simplifies the complex process of aligning Large Language Models by removing the need for a reference model, which is typically required by methods like DPO.

Core Features & Use Cases

  • Reference-Free Alignment: Optimizes model preferences directly without the computational overhead of a reference model.
  • Flexible Training: Supports various model architectures including Mistral, Llama 3, and DeepSeek.
  • Use Case: Use this skill to fine-tune a base model on preference datasets to improve instruction-following capabilities while maintaining reasoning performance.

Quick Start

Execute the training script using the accelerate launch command with your chosen configuration file to begin the preference optimization process.

Frequently Asked Questions about simpo-training

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

FAQPage Schema
How do I align LLMs with human preferences without using a reference model?▼

SimPO performs reference-free preference optimization to align LLMs directly on preference datasets like UltraFeedback, eliminating the computational overhead of loading and maintaining a reference model required by DPO.

What is the best way to fine-tune Llama 3 for instruction following on a single node?▼

Reference-free preference optimization targets single-node training workflows to improve instruction-following capabilities in models like Llama 3 while maintaining reasoning performance and avoiding heavy compute overhead.

How do I start preference optimization training using transformers and accelerate?▼

You execute the training script using the accelerate launch command with your chosen configuration file to begin the preference optimization process on base or instruct models.

Does reference-free preference optimization work with Mistral and DeepSeek architectures?▼

Yes, reference-free preference optimization supports flexible training across various model architectures including Mistral, Llama 3, and DeepSeek for post-training alignment workflows.

Why does preference optimization require less memory than Direct Preference Optimization?▼

Reference-free preference optimization requires less memory and compute than DPO because it eliminates the need to load and run a separate reference model during the alignment training process.

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