Selective Attention Transformer

This repository contains an unofficial implementation of the Selective Attention Transformer, a novel architecture designed to enhance attention mechanisms in deep learning models.

For more details, you can refer to the arXiv paper.

Overview

The Selective Attention Transformer aims to improve the efficiency and effectiveness of attention mechanisms by selectively focusing on relevant parts of the input data. This implementation is based on the original paper by Author(s) Name(s).

Features

• Selective Attention Mechanism: Focuses on the most relevant parts of the input data.
• Scalable Architecture: Designed to work efficiently with large datasets.
• Modular Design: Easy to integrate with existing deep learning frameworks.
• Key-Value Cache (kv_cache): Enhances performance by caching key-value pairs for faster access during inference.