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simpo-training

Optimize LLM alignment with reference-free SimPO preference training.

Updated Aug 27, 2026
Search Tags:#fine-tuning#preference optimization#simpo#llm training#llm alignment#dpo alternative
One-click install
npx skills add https://github.com/choice5346/BiSHE --skill simpo-training-choice5346
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/choice5346/BiSHE/tree/main/.github/skills/simpo
Command: npx skills add https://github.com/choice5346/BiSHE --skill simpo-training-choice5346

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) with human preferences, outperforming traditional methods like DPO without the need for a reference model.

Core Features & Use Cases

  • Reference-Free Preference Optimization: Train LLMs using only preference data (chosen vs. rejected responses), eliminating the complexity of managing a reference model.
  • Improved Performance: Achieves better alignment results compared to DPO, as demonstrated by performance gains on benchmarks like AlpacaEval 2.0.
  • Efficient Training: Offers a faster and more resource-friendly training process, suitable for various model sizes and tasks.
  • Use Case: Fine-tune a base LLM like Mistral 7B on a dataset of preferred responses to make it more helpful and aligned with user instructions, without the overhead of setting up and maintaining a separate reference model.

Quick Start

Launch SimPO training for Mistral 7B using the provided configuration.

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 without a reference model for preference optimization?▼

Use the SimPO algorithm for reference-free preference optimization, which aligns LLMs using only chosen and rejected responses. This method eliminates the need for a separate reference model, simplifying the training pipeline.

What is the difference between SimPO and DPO for LLM alignment?▼

SimPO improves upon DPO by eliminating the reference model requirement, resulting in a simpler, faster, and more resource-friendly training process. It also achieves better alignment performance on benchmarks like AlpacaEval 2.0.

How do I fine-tune Mistral 7B with preference data using SimPO?▼

Fine-tune Mistral 7B by launching SimPO training with the provided scripts and configurable hyperparameters. You need a dataset of preferred responses containing chosen and rejected pairs to align the base model with user instructions.

Can I use transformers and trl for reference-free LLM training?▼

Yes, this Skill leverages transformers, trl, and accelerate to execute reference-free LLM training. It also integrates flash-attn to ensure an efficient and resource-friendly fine-tuning process across various model sizes.

Does SimPO work for code generation and math reasoning tasks?▼

Yes, SimPO supports diverse tasks including code generation and math reasoning. By leveraging configurable hyperparameters and varied datasets, it effectively aligns base LLMs for these specific domains without a reference model.

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