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simpo-training

Implement SimPO preference alignment for LLMs without a reference model.

Updated Jul 3, 2026
Search Tags:#llm#alignment#training#large language models#preference optimization
One-click install
npx skills add https://github.com/LynxLabVN/office-agent --skill simpo-training-lynxlabvn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/LynxLabVN/office-agent/tree/main/agent-core/optional-skills/mlops/simpo
Command: npx skills add https://github.com/LynxLabVN/office-agent --skill simpo-training-lynxlabvn

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides an efficient and reference-free method for preference alignment in large language models (LLMs), simplifying and accelerating training processes compared to traditional approaches.

Core Features & Use Cases

  • Reference-Free Alignment: Avoids the need for a reference model, making it more accessible and efficient.
  • Performance Boost: Delivers better performance than traditional methods, with a reported +6.4 points on AlpacaEval 2.0.
  • Use Case: Ideal for preference alignment tasks where simplicity and speed are crucial, such as when fine-tuning LLMs for specific applications or datasets.

Quick Start

To begin training a SimPO model, run the following command:

accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml scripts/run_simpo.py training_configs/mistral-7b-base-simpo.yaml

Frequently Asked Questions about simpo-training

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

FAQPage Schema
How do I align large language models without a reference model?▼

You can achieve reference-free LLM alignment using SimPO, which simplifies and accelerates the training process by eliminating the need for a reference model during preference optimization.

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

To run preference optimization training, execute the provided scripts via accelerate launch with a DeepSpeed config, passing your training configuration YAML to the run_simpo.py script to start the process.

Does SimPO work with torch and trl for fine-tuning custom models?▼

Yes, SimPO works with torch, trl, transformers, datasets, and accelerate to fine-tune custom models on specific datasets, making it suitable for applications requiring simple and fast preference alignment.

What is the best way to speed up LLM preference alignment training?▼

SimPO is a simple and efficient method for LLM preference alignment that enhances training speed and performance, delivering a reported 6.4 points improvement on AlpacaEval 2.0 compared to traditional approaches.

Why does reference-free alignment improve performance over traditional LLM training?▼

Reference-free alignment improves performance by avoiding the overhead of loading and managing a reference model, which streamlines memory usage and accelerates training while delivering better results on benchmarks.

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