  Recent studies have demonstrated the effectiveness of Gated Linear Units
(GLU) in enhancing transformer models, particularly in Large Language Models
(LLMs). Additionally, utilizing a parallel configuration within each
Transformer block rather than the conventional serialized method has been
revealed to accelerate the training of LLMs without significantly impacting
performance. However, when the MLP and attention block were run in parallel for
the image classification task, we observed a noticeable decline in performance.
We propose a novel transformer variant that integrates non-linearity within the
attention block to tackle this problem. We implemented the GLU-based activation
function on the Value tensor, and this new technique surpasses the current
state-of-the-art S/16 variant of Vision Transformers by 0.6% on the ImageNet-1K
dataset while utilizing fewer parameters. It also supersedes the B/16 variant
while using only half the parameters. Furthermore, we provide results with the
GELU activation function variant to confirm our assertions. Lastly, we showcase
that the MABViT variants exhibit greater potential when utilized in deep
transformers compared to the standard architecture.
