Wonderful Matrices: Combining for a More Efficient and Effective
  Foundation Model Architecture
Authors: name: Jingze Shi, name: Bingheng Wu

In order to make the foundation model more efficient and effective, our idea
is combining sequence transformation and state transformation. First, we prove
the availability of rotary position embedding in the state space duality
algorithm, which reduces the perplexity of the hybrid quadratic causal
self-attention and state space duality by more than 4%, to ensure that the
combining sequence transformation unifies position encoding. Second, we propose
dynamic mask attention, which maintains 100% accuracy in the more challenging
multi-query associative recall task, improving by more than 150% compared to
quadratic causal self-attention and state space duality, to ensure that the
combining sequence transformation selectively filters relevant information.
Third, we design cross domain mixture of experts, which makes the computational
speed of expert retrieval with more than 1024 experts 8 to 10 times faster than
the mixture of experts, to ensure that the combining state transformation
quickly retrieval mixture. Finally, we summarize these matrix algorithms that
can form the foundation model: Wonderful Matrices, which can be a competitor to
popular model architectures.