ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter10/book-translation/sample_book/chapter2.md

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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.
+
+```python
+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.