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simpo-training

Optimizes LLM preferences with SimPO using torch, transformers, datasets, trl, and accelerate.

Updated Jun 9, 2026
Search Tags:#training#preference optimization#language model#LLM alignment#SimPO
One-click install
npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill simpo-training-zhouboyu-xreal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/zhouboyu-xreal/Hermes-Memory/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill simpo-training-zhouboyu-xreal

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 addresses the challenge of preference alignment in large language models (LLMs), providing a reference-free alternative to DPO with better performance.

Core Features & Use Cases

  • Reference-Free Optimization: SimPO offers a simple and efficient method for LLM preference alignment without the need for a reference model.
  • Performance Improvement: Achieves performance gains of +6.4 points on AlpacaEval 2.0.
  • Use Case: Ideal for preference alignment when you want a simpler and faster training process compared to DPO/PPO.

Quick Start

Install the SimPO skill and run the training command for your desired model and configuration.

Frequently Asked Questions about simpo-training

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

FAQPage Schema
What is reference-free preference optimization for large language models?▼

Reference-free preference optimization is an LLM alignment method that trains models on preference data without requiring a separate reference model. SimPO simplifies the training process while achieving better performance than standard methods like DPO.

How do I align LLM preferences without using a reference model?▼

You can align LLM preferences without a reference model by using SimPO, a reference-free optimization method. This approach simplifies the training pipeline by eliminating the need for a reference model during preference alignment.

Does SimPO work with the Hugging Face transformers and trl libraries?▼

Yes, SimPO works with transformers and trl, alongside required dependencies like torch, datasets, and accelerate. These libraries provide the foundational environment for loading models and running the preference optimization training scripts.

Why choose SimPO over DPO for preference optimization?▼

Choose SimPO over DPO to achieve a simpler and faster training process. SimPO is a reference-free alternative to DPO that has demonstrated performance gains of +6.4 points on the AlpacaEval 2.0 benchmark.

Do I need a reference model for LLM alignment with SimPO?▼

No, you do not need a reference model for LLM alignment with SimPO. SimPO is explicitly designed as a reference-free preference optimization method, reducing computational overhead and simplifying the training workflow.

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