Transformer upgrade path: 7. Length extrapolation and local attention - Code World

Transformer upgrade path: 7. Length extrapolation and local attention

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Transformer upgrade path: 7. Length extrapolation and local attention

Alibi:Attention With Linear Biases Enables Input Length Extrapolation

On the Length Extrapolation of LLM

To crack the mystery of self-attention reasoning defects, Ant develops a new generation of Transformer or realizes lossless extrapolation

Transformer upgrade path: 1. Sinusoidal position code tracing to the source

Dr. Stanford made Attention 9 times faster by himself! FlashAttention explodes video memory, and Transformer context length increases to an epic level

Understanding of attention in Transformer

7. Transformer-XL principles introduced

A solution to the length limitation of the transformer model

A little exploration of Attention (attention mechanism) in Transformer

Transformer —— attention is all you need

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Study Notes -Transformer the attention mechanism

self-attention与Transformer补充

Transformer-01 Attention Mechanism

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BERT (1)-Detailed explanation of BERT transformer attention

[Notes] Transformer framework: Attention is all you need

Attention mechanism - Spatial Transformer Networks (STN)

LITE TRANSFORMER WITH LONG-SHORT RANGE ATTENTION

Machine Learning Notes - Visualizing Attention in Vision Transformer

[Transformers 01] All information about attention and transformer

Attention is All You Need (Introduction to Transformer)

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