Writing an LLM from scratch, part 8 -- trainable self-attention

A detailed explanation of implementing trainable self-attention in LLMs, focusing on scaled dot product attention and matrix projections. The article breaks down how attention scores are calculated through query, key, and value matrices, demonstrating how five matrix multiplications can efficiently process token relationships.

The FFT Strikes Back: An Efficient Alternative to Self-Attention

FFTNet introduces a novel approach to sequence processing using Fast Fourier Transform, achieving O(n log n) complexity compared to traditional self-attention's quadratic complexity. The framework employs spectral filtering and modReLU activation to efficiently capture long-range dependencies, demonstrating superior performance on Long Range Arena and ImageNet benchmarks.