What exactly are the "parameters" in GPT-3's 175 billion parameters and how are they chosen/generated?
Why does this multiplication of $Q$ and $K$ have a variance of $d_k$, in scaled dot product attention?
Can you confirm that the transformer works strictly deterministically and there is no randomness inside or between the attention layers?
What is the Intermediate (dense) layer in between attention-output and encoder-output dense layers within transformer block in PyTorch implementation?
Can someone help me understand the intuition behind the query, key and value matrices in the transformer architecture?
Why don't people use nonlinear activation functions after projecting the query key value in attention?
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