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simpo-training

Optimize Large Language Models with Simple Preference Optimization without a reference model.

2|1|Updated Jul 14, 2026
Search Tags:#llm#machine learning#alignment#training#preference optimization#simpo
One-click install
npx skills add https://github.com/heysuhas/hermes_cli --skill simpo-training-heysuhas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/heysuhas/hermes_cli/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/heysuhas/hermes_cli --skill simpo-training-heysuhas

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the complexity and computational overhead of traditional LLM alignment methods like DPO and PPO by providing a streamlined, reference-free optimization approach.

Core Features & Use Cases

  • Reference-Free Alignment: Eliminates the need for a separate reference model, reducing memory usage and training complexity.
  • Performance Optimization: Delivers superior alignment results compared to DPO, with specific tuning for reasoning and instruction-following tasks.
  • Use Case: Use this Skill to fine-tune a base model like Mistral 7B on preference data to improve its helpfulness and instruction-following capabilities without the overhead of maintaining a reference model.

Quick Start

Execute the simpo training process by running the accelerate launch command with your chosen configuration file from the scripts directory.

Frequently Asked Questions about simpo-training

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

FAQPage Schema
How do I align a large language model with human preferences without using a reference model?▼

You can align a large language model without a reference model using the Simple Preference Optimization algorithm, which minimizes preference-based loss directly to reduce memory usage and training complexity.

How do I fine-tune a base model using SimPO and PyTorch?▼

To fine-tune a base model with preference data, execute the SimPO training process by running the accelerate launch command with your chosen configuration file from the scripts directory.

Does preference optimization with SimPO work well for instruction-following tasks?▼

Yes, preference optimization with SimPO supports instruction-following refinement and reasoning-intensive task adaptation, delivering superior alignment results compared to DPO for these scenarios.

What is the best way to reduce memory overhead during LLM alignment compared to DPO?▼

The best way to reduce memory overhead during LLM alignment is reference-free preference optimization, which eliminates the need to maintain a separate reference model during training.

Do I need TRL and Accelerate installed to run SimPO training?▼

Yes, you need PyTorch, Transformers, Datasets, TRL, and Accelerate installed in your environment to execute the preference-based loss minimization required for SimPO training.

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