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simpo-training

Optimize large language model alignment with reference-free preference optimization.

Updated Aug 27, 2026
Search Tags:#alignment#hyperparameters#simpo#llm-training#reference-free#preference-optimization#loss-functions
One-click install
npx skills add https://github.com/helix4u/hermes-agent --skill simpo-training-helix4u
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/helix4u/hermes-agent/tree/main/skills/mlops/training/simpo
Command: npx skills add https://github.com/helix4u/hermes-agent --skill simpo-training-helix4u

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SimPO is a reference-free preference optimization method for aligning large language models by directly optimizing the relative quality of preferred and non-preferred responses.

Core Features & Use Cases

  • Reference-free optimization for alignment tasks across base, instruct, and chat models.
  • Supports configurable hyperparameters (learning_rate, beta, gamma_beta_ratio, sft_weight) and loss types (sigmoid, hinge).
  • Works with common datasets like UltraFeedback and other curated preference pairs for robust evaluation and fine-tuning.

Quick Start

Start a training run with a base model using a prepared preference dataset and SimPO config options such as learning_rate, beta, gamma_beta_ratio, and loss_type.

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 using a reference model for preference optimization?▼

Reference-free preference optimization aligns large language models by directly optimizing the relative quality of preferred and non-preferred responses, eliminating the need for an external reference model during training.

What datasets work with reference-free preference optimization for fine-tuning chat models?▼

Reference-free preference optimization works with common datasets like UltraFeedback and other curated preference pairs to robustly evaluate and fine-tune base, instruct, and chat style large language models.

How do I configure hyperparameters for SimPO loss functions during model fine-tuning?▼

Configure hyperparameters like learning_rate, beta, gamma_beta_ratio, and sft_weight alongside loss types such as sigmoid or hinge to control the preference optimization process for large language models.

Does reference-free preference optimization support both sigmoid and hinge loss types?▼

Yes, reference-free preference optimization supports configurable loss types including sigmoid and hinge, allowing you to adjust the training dynamics when aligning large language models with preference datasets.

What is the difference between reference-free preference optimization and standard RLHF?▼

Reference-free preference optimization directly optimizes response quality using preference pairs without loading an external reference model, whereas standard RLHF typically requires a separate reward or reference model to guide alignment.

When should I avoid using reference-free preference optimization for LLM alignment?▼

Avoid reference-free preference optimization if your alignment task lacks curated preferred and non-preferred response pairs, as the method fundamentally relies on comparing relative response quality to function correctly.

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