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Chapter 2: The Transformer and Attention
Modern language models are built on the transformer architecture. Its central idea is attention: instead of reading a sequence strictly left to right, the model lets every token look at every other token and decide which ones matter. This is why a transformer can connect a pronoun to a noun that appeared many tokens earlier.
Attention works on the embedding of each token. For every token the model computes three vectors — a query, a key, and a value — and uses them to weigh how much each token should attend to the others.
def attention(query, key, value):
scores = query @ key.T # similarity between tokens
weights = softmax(scores) # attention weights
return weights @ value # weighted embedding
Because attention compares every token with every other token, its cost grows quickly as the prompt gets longer. This is the root cause of the latency problems we will attack in the next chapter. Still, attention is what gives the transformer its power: during inference, it lets the model route information flexibly across the whole prompt rather than through a fixed pipeline.