skills.rest
Rawgrowth-Consulting
Rawgrowth-ConsultingOFFICIAL
Explore all skills by this author →
GitHub

simpo-training

Automate reference-free preference optimization for LLM alignment with configurable loss functions.

Updated Apr 23, 2026
Search Tags:#llm#alignment#training#datasets#simpo#preference#loss-functions
One-click install
npx skills add https://github.com/Rawgrowth-Consulting/rawclaw-agent --skill simpo-training-rawgrowth-consulting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simpo-training
Source: https://github.com/Rawgrowth-Consulting/rawclaw-agent/tree/main/optional-skills/mlops/simpo
Command: npx skills add https://github.com/Rawgrowth-Consulting/rawclaw-agent --skill simpo-training-rawgrowth-consulting

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SimPO provides a reference-free approach to aligning LLMs by optimizing preference signals directly, eliminating the need for a separate reference model.

Core Features & Use Cases

  • Reference-free preference optimization for model alignment.
  • Configurable loss functions (sigmoid or hinge) with adjustable beta, gamma_beta_ratio, and optional SFT regularization.
  • Supports common alignment workflows with standard datasets and evaluation prompts.
  • Use Case: Fine-tune an LLM to prefer safer, more helpful responses on a custom preference dataset.

Quick Start

Run a SimPO training workflow on your preference dataset to align an LLM without a reference model.

Frequently Asked Questions about simpo-training

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

FAQPage Schema
What is reference-free preference optimization for LLMs?▼

SimPO aligns LLMs without a reference model by directly optimizing preference signals, simplifying fine-tuning workflows. It applies standard loss functions like sigmoid or hinge to preference datasets.

How do I align an LLM with a custom preference dataset without a reference model?▼

Run a SimPO training workflow on your labeled or synthetic preference dataset to align an LLM without a reference model. The process integrates directly with PyTorch, Transformers, datasets, TRL, and accelerate for fine-tuning.

What loss functions and parameters are configurable for preference optimization?▼

The configurable loss framework supports sigmoid or hinge loss types with adjustable beta, gamma_beta_ratio, label_smoothing, and an optional sft_weight for SFT regularization during LLM alignment.

Does preference optimization work with PyTorch and Transformers?▼

Yes, reference-free preference optimization integrates with PyTorch, Transformers, datasets, TRL, and accelerate. It supports fine-tuning and alignment workflows across common LLM sizes using standard datasets and evaluation tasks.

Why use reference-free alignment instead of standard preference optimization with a reference model?▼

Reference-free alignment eliminates the need to load a separate reference model during LLM fine-tuning, reducing computational overhead. It directly optimizes preference signals to guide models toward safer, more helpful responses.

Can I use synthetic preference data for LLM alignment?▼

Yes, reference-free preference optimization supports both labeled and synthetic preference data for LLM alignment. This enables fine-tuning workflows across common model sizes using standard datasets and evaluation tasks.

Related Skills

Explore More Data & Analytics Skills→
pmcdowallpmcdowall

fine-tuning-with-trl

Align large language models via reinforcement learning and preference-based fine-tuning workflows.

Community
Advanced
KarlinskySKarlinskyS

simpo-training

Train 7B-70B LLMs with reference-free SimPO preference optimization.

Community
Intermediate
MesferAliMesferAli

simpo-training

Implement SimPO training for LLM alignment using PyTorch and Transformers.

Community
Advanced
kaminocorpkaminocorp
228

simpo-training

Configure reference-free preference optimization for LLM alignment with adjustable beta, gamma, and SFT weight.

Official
Advanced