最近忙完夏令营面试,开始对最新的大模型相关研究感兴趣,在我的1650Ti上跑了个7B的RWKV,甚是惊异,遂follow一下该结构进展,但是理解的时候出现了一些难处,主要是An Attention Free Transformer相关工作的启发,这篇文章记录一下理解,有不对之处请指正。代码学习参考github上的,笔记的代码比较零散;论文地址 核心结构 整体...
We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention. In an AFT layer, the key and value are first combined with a set of learned position biases, the result of which is multiplied with the query in an el...
We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention. In an AFT layer, the key and value are first combined with a set of learned position biases, the result of which is multiplied with the query in an el...
Ultimate-Awesome-Transformer-Attention This repo contains a comprehensive paper list of Vision Transformer & Attention, including papers, codes, and related websites. This list is maintained by Min-Hung Chen. (Actively keep updating) If you find some ignored papers, feel free to create pull re...
We propose an attention-based multiscale transformer network (AMTNet) that utilizes a CNN-transformer structure to address this issue. Our Siamese network based on the CNN-transformer architecture uses ConvNets as the backbone to extract multiscale features from the raw input image pair. We then ...
Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet) - Baseline 1a (small model) (2 heads, 2 encoder/decoder layers, sinusoid positional encoding, 128 units, SGD, beam5) w/ dropout (0.1) (source and target embeddings, sub-layers (attention + feedforward)) and label smoothing...
现有深度视觉网络模型,不同层之间混合2种特征:1)给定空间位置处的特征;2)不同空间位置的特征。CNN通过N*N的卷积实现2),1*1的卷积实现1);Transformer的self-attention可以同时实现1)和2)。Mixer通过通道混合实现1)、通过tokens混合实现2)。 如下图1展示了Mixer网络结构。输入为图像(H*W)分割后的S个图像块(S...
So, what is the masked multi-head attention layer responsible for? During the generation of the next English word, the network is allowed to use all the words from the French word. However, when dealing with a given word in the target sequence (English translation), the network only has ...
RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly ...
To produce the.ipynbnotebook file using the markdown source, run (under the hood, thenotebookbuild target simply runsjupytext --to ipynb the_annotated_transformer.py): make notebook To produce the html version of the notebook, run: ...