Decoding Attention is specially optimized for multi head attention (MHA) using CUDA core for the decoding stage of LLM inference.
-
Updated
Sep 14, 2024 - C++
Decoding Attention is specially optimized for multi head attention (MHA) using CUDA core for the decoding stage of LLM inference.
Machine learning development toolkit built upon Transformer encoder network architectures and tailored for the realm of high-energy physics and particle-collision event analysis.
Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.
[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.
Self-Supervised Vision Transformers for multiplexed imaging datasets
The original transformer implementation from scratch. It contains informative comments on each block
Simple character level Transformer
several types of attention modules written in PyTorch
完整的原版transformer程序,complete origin transformer program
This repository contains code for implementing Vision Transformer (ViT) model for image classification
HydraViT is a PyTorch implementation of the HydraViT model, an adaptive multi-branch transformer for multi-label disease classification from chest X-ray images. The repository provides the necessary code to train and evaluate the HydraViT model on the NIH Chest X-ray dataset.
Exploring attention weights in transformer-based models with linguistic knowledge.
A Basic Multi layered Neural Network, With Attention Masking Features
The Transformer model implemented from scratch using PyTorch. The model uses weight sharing between the embedding layers and the pre-softmax linear layer. Training on the Multi30k machine translation task is shown.
Multi^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT (Findings of ACL: EMNLP 2020)
Transformer translator website with multithreaded web server in Rust
This project aims to implement the Scaled-Dot-Product Attention layer and the Multi-Head Attention layer using various Positional Encoding methods.
A Faster Pytorch Implementation of Multi-Head Self-Attention
Image Captioning with Encoder as Efficientnet and Decoder as Decoder of Transformer combined with the attention mechanism.
Add a description, image, and links to the multi-head-attention topic page so that developers can more easily learn about it.
To associate your repository with the multi-head-attention topic, visit your repo's landing page and select "manage topics."