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AissenLiu
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simpo-training

Run reference-free SimPO alignment training for LLMs with configurable hyperparameters.

Updated Apr 27, 2026
Search Tags:#datasets#simpo#accelerate#reference-free#preference-optimization#training-config#llm-alignment
One-click install
npx skills add https://github.com/AissenLiu/EasyHermes --skill simpo-training-aissenliu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/AissenLiu/EasyHermes/tree/main/hermes-agent/optional-skills/mlops/simpo
Command: npx skills add https://github.com/AissenLiu/EasyHermes --skill simpo-training-aissenliu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

SimPO provides a lightweight, reference-free approach to aligning large language models by directly optimizing human-preference signals, reducing reliance on expensive reference models and simplifying the training pipeline.

Core Features & Use Cases

  • Reference-free preference optimization for LLMS, enabling faster experimentation and iteration.
  • Supports common base models (e.g., Mistral-7B, Meta-Llama-3-8B-Instruct) with configurable hyperparameters and datasets.
  • Includes guidance on datasets, hyperparameters, and workflows for efficient alignment at scale.
  • Real-world use: train preference models for instruction-following tasks with reduced infrastructure complexity.

Quick Start

Run a sample SimPO training job using the provided config to observe a basic alignment workflow.

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 without a reference model?▼

Reference-free preference optimization aligns LLMs by directly optimizing human-preference signals without a reference model, simplifying the pipeline and reducing infrastructure complexity.

Can I use Mistral-7B or Meta-Llama-3-8B-Instruct for preference optimization?▼

Yes, reference-free preference optimization supports common base models like Mistral-7B and Meta-Llama-3-8B-Instruct for single-node experiments across standard preference datasets with configurable hyperparameters.

What hyperparameters do I need to configure for SimPO training?▼

SimPO training requires configuring model_name_or_path, dataset_mixer, learning_rate, beta, gamma_beta_ratio, sft_weight, loss_type, label_smoothing, and optional SFT settings to execute the alignment workflow.

Does reference-free LLM alignment work with the Hugging Face transformers and trl libraries?▼

Reference-free LLM alignment depends on torch, transformers, datasets, trl, and accelerate to execute end-to-end lightweight alignment training jobs across single-node environments.

Why choose reference-free preference optimization over standard RLHF?▼

Reference-free preference optimization provides a lightweight approach by directly optimizing human-preference signals, reducing reliance on expensive reference models and accelerating experimentation iteration.

When should I not use reference-free preference optimization for instruction-following tasks?▼

Avoid reference-free preference optimization if your alignment task strictly requires a baseline reference model for KL penalty calculations, as this workflow directly optimizes preferences without one.

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