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simpo-training

Align LLMs without a reference model using SimPO optimization.

Updated Mar 26, 2026
Search Tags:#mlops#simpo#rlhf#reference-free#preference-optimization#llm-alignment
One-click install
npx skills add https://github.com/cloudliness/Hermes-Autonomous-AI-Agent-Dialed-In-For-Windows-11 --skill simpo-training-cloudliness
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/cloudliness/Hermes-Autonomous-AI-Agent-Dialed-In-For-Windows-11/tree/main/skills/mlops/training/simpo
Command: npx skills add https://github.com/cloudliness/Hermes-Autonomous-AI-Agent-Dialed-In-For-Windows-11 --skill simpo-training-cloudliness

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Align LLMs without a costly reference model to achieve efficient, scalable alignment.

Core Features & Use Cases

  • Reference-free SimPO optimization that directly improves preference alignment without needing a reference policy.
  • Works across model scales from 7B to 70B and supports various preference datasets for robust fine-tuning.
  • Real-world use: train an instruct/open-ended model to prefer helpful responses while preserving capabilities.

Quick Start

Install the required packages and start a SimPO training run 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 LLMs with preference datasets without using a reference model?▼

You can use reference-free preference optimization to align LLMs without a reference model. SimPO directly improves preference alignment by eliminating the need for a costly reference policy during training.

Can I use SimPO for fine-tuning large 70B-scale language models?▼

Yes, SimPO supports fine-tuning and alignment across 7B to 70B-scale models. It processes diverse preference datasets to robustly align large language models efficiently.

What PyTorch libraries do I need to start reference-free preference optimization?▼

You need PyTorch, transformers, datasets, trl, and accelerate to run reference-free preference optimization. Install these required packages before starting a SimPO training run.

How does reference-free preference optimization differ from standard RLHF?▼

Reference-free preference optimization differs from standard RLHF by directly aligning models without a reference policy. This makes preference alignment more efficient and scalable by removing the costly reference model dependency.

What SimPO configuration options are available for LLM alignment?▼

SimPO provides configurable options including dataset_mixer, model_name_or_path, beta, gamma_beta_ratio, loss_type, and sft_weight. These parameters control the preference optimization process for LLM alignment.

When should I use reference-free preference optimization for LLM training?▼

Use reference-free preference optimization when you need efficient, scalable LLM alignment without a costly reference model. It is ideal for training instruct models to prefer helpful responses while preserving existing capabilities.

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