What makes SimPO better than DPO?

SimPO is more computationally efficient because it eliminates the need for a reference model during training, reducing VRAM requirements while typically outperforming DPO on alignment benchmarks.

What hardware is required for SimPO training?

For a 7B model, an NVIDIA A100 (40GB) with DeepSpeed ZeRO-3 is recommended. Larger 70B models generally require an 8x A100 (80GB) node.

Can I use SimPO for math and coding tasks?

Yes, SimPO is effective for reasoning-intensive tasks. It is recommended to use a lower learning rate (e.g., 3e-7) and a higher beta value to provide a stronger optimization signal.

How do I prevent the model from forgetting capabilities during SimPO?

You can use the 'sft_weight' hyperparameter to add a Supervised Fine-Tuning loss component, which helps the model retain its original capabilities while learning preferences.

SimPO LLM Alignment

Name: SimPO LLM Alignment
Author: zechenzhangAGI

byzechenzhangAGI

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384

データサイエンスとML

Simplifies large language model alignment using reference-free preference optimization to improve model performance without the overhead of PPO or DPO.

概要

SimPO (Simple Preference Optimization) is a cutting-edge technique for aligning Large Language Models (LLMs) with human preferences, offering a more efficient and higher-performing alternative to standard Direct Preference Optimization (DPO). By eliminating the need for a reference model during training, SimPO significantly reduces memory requirements and computational overhead while achieving superior results on benchmarks like AlpacaEval 2.0. This skill provides comprehensive workflows for training base and instruct models, reasoning-intensive task optimization, and troubleshooting common issues like loss divergence or capability forgetting, making it essential for AI researchers and engineers aiming for state-of-the-art model performance with limited compute resources.

主な機能

Integrated SFT regularization to maintain model capabilities and prevent forgetting
384 GitHub stars
Customizable loss functions including sigmoid and hinge with adjustable target margins
Memory-efficient workflows optimized for 7B, 8B, and 70B parameter models
Reference-free preference optimization requiring no baseline model during training
Superior alignment performance with +6.4 point gains on AlpacaEval 2.0 over DPO

ユースケース

Reducing training costs and VRAM usage for post-training alignment stages
Aligning base models like Mistral or Llama to human preference data without reference models
Enhancing reasoning and coding performance in specialized models with targeted preference sets

概要

主な機能

Integrated SFT regularization to maintain model capabilities and prevent forgetting
384 GitHub stars
Customizable loss functions including sigmoid and hinge with adjustable target margins
Memory-efficient workflows optimized for 7B, 8B, and 70B parameter models
Reference-free preference optimization requiring no baseline model during training
Superior alignment performance with +6.4 point gains on AlpacaEval 2.0 over DPO

ユースケース

Reducing training costs and VRAM usage for post-training alignment stages
Aligning base models like Mistral or Llama to human preference data without reference models
Enhancing reasoning and coding performance in specialized models with targeted preference sets