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simpo-training

Optimize LLM preference alignment with the reference-free SimPO algorithm.

Updated May 4, 2026
Search Tags:#llm#machine learning#alignment#training#preference optimization#simpo
One-click install
npx skills add https://github.com/InverterNetwork/hermes-agent --skill simpo-training-inverternetwork
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/InverterNetwork/hermes-agent/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/InverterNetwork/hermes-agent --skill simpo-training-inverternetwork

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

SimPO simplifies the complex process of aligning Large Language Models by removing the need for a reference model, which is typically required by methods like DPO.

Core Features & Use Cases

  • Reference-Free Optimization: Train models on preference data without the computational overhead of a reference model.
  • Flexible Alignment: Supports various preference datasets and provides configurable reward scaling and margin control.
  • Use Case: Use this skill to fine-tune a base model like Mistral 7B or an instruct model like Llama 3 8B on preference pairs to improve instruction-following capabilities and reasoning performance.

Quick Start

Run the simpo training script using the provided accelerate configuration and your specific model training yaml file.

Frequently Asked Questions about simpo-training

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

FAQPage Schema
How does reference-free preference optimization work for LLM training?▼

Reference-free preference optimization aligns Large Language Models using preference data without a reference model. The SimPO algorithm uses configurable reward scaling and margin-based loss functions to bypass the computational overhead required by methods like DPO.

How do I fine-tune a Llama 3 8B model using SimPO?▼

To fine-tune a Llama 3 8B model using SimPO, run the provided training script with accelerate configuration and your specific model training YAML file. This applies preference pairs to improve instruction-following capabilities on a single node.

Can I use the SimPO algorithm for single-node training with transformers?▼

Yes, SimPO supports efficient single-node training using PyTorch and transformers. It integrates dependencies like datasets, trl, and accelerate to optimize preference alignment without needing a reference model.

What is the best way to align a base model for reasoning-intensive tasks?▼

The best way to align a base model for reasoning-intensive tasks is using the SimPO algorithm. It applies reference-free preference optimization with configurable reward scaling to improve reasoning performance and instruction-following.

Why does SimPO remove the need for a reference model in preference alignment?▼

SimPO removes the need for a reference model to simplify the complex alignment process and reduce computational overhead. It achieves preference alignment directly through margin-based loss functions and reward scaling.

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