Llama-3-Base-8B-SFT Open Source Model - Simplify Preference Alignment with SimPO and Optimize the Usage Experience

Llama 3 Base 8B SFT

Developed by princeton-nlp

SimPO is a preference optimization method that eliminates the need for reference reward models, simplifying the preference alignment process.

Large Language Model

Transformers

#Reference-Free Reward Optimization #Preference Learning Simplification #Efficient Alignment Training

Downloads 5,967

Release Time : 5/17/2024

Model Overview

By directly optimizing preference data, SimPO avoids the complex reward model training steps in traditional methods, improving training efficiency and model performance.

Model Features

No Reference Reward Model Needed

Directly optimizes preference data, eliminating the need to train complex reward models.

High Training Efficiency

Simplifies the preference alignment process, improving training speed.

Superior Performance

Outperforms traditional methods in multiple benchmark tests.

Model Capabilities

Preference Optimization

Language Model Alignment

Reinforcement Learning

Use Cases

Language Model Training

Large Language Model Preference Alignment

Used to optimize human preference alignment for large language models.

Improves the quality and safety of model outputs.

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Llama 3 Base 8B SFT

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 SimPO Model Introduction

🚀 Quick Start