Review a compact GPT-style transformer implementation
You are given a short implementation of a GPT-style decoder-only transformer. Read it and review it: explain what each part does (embeddings, attention with its causal mask, feed-forward layers, normalization, the output head), point out bugs or questionable choices, and say how you would improve it.
No answer yet. Write the first one; the best answer gets endorsed.
