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simpo-training

Optimize LLM alignment with reference-free preference optimization using PyTorch and Accelerate.

Updated Jun 25, 2026
Search Tags:#llm#machine learning#alignment#training#preference optimization#simpo
One-click install
npx skills add https://github.com/Rheasilvia/hermes-desktop --skill simpo-training-rheasilvia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/Rheasilvia/hermes-desktop/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/Rheasilvia/hermes-desktop --skill simpo-training-rheasilvia

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the complex process of aligning Large Language Models with human preferences by removing the need for a reference model, which is typically required in methods like DPO.

Core Features & Use Cases

  • Reference-Free Optimization: Train models on preference data without the computational overhead of maintaining a reference model.
  • Flexible Loss Functions: Supports both sigmoid and hinge loss types to suit different alignment requirements.
  • Use Case: Use this to fine-tune a base model like Mistral 7B on preference datasets to improve instruction-following capabilities while maintaining high training stability.

Quick Start

Execute the simpo training process by running the accelerate launch command with your chosen configuration file to begin the preference optimization.

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 with human preferences without using a reference model?▼

Reference-free preference optimization aligns LLMs with human preferences by eliminating the reference model required in methods like DPO. This reduces computational overhead while maintaining training stability for instruction-tuned models.

Can I fine-tune Mistral 7B on a preference dataset using PyTorch and Accelerate?▼

Yes, you can fine-tune base models like Mistral 7B on preference datasets using PyTorch, Transformers, and Accelerate. This supports efficient, single-node training to improve instruction-following capabilities while maintaining stability.

How do I start reference-free preference optimization training?▼

Execute the preference optimization process by running the accelerate launch command with your chosen configuration file. This initiates the reference-free alignment workflow for your instruction-tuned models.

What loss functions are supported for preference optimization in this workflow?▼

The preference optimization workflow supports both sigmoid and hinge loss types. These flexible loss functions allow you to suit different alignment requirements when training your models on preference data.

Why does reference-free alignment improve training efficiency compared to DPO?▼

Reference-free alignment improves training efficiency by removing the need to maintain a separate reference model during optimization. Unlike DPO, this approach reduces computational overhead and memory usage while achieving high training stability.

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