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attention.py
Medium
def attention(Q, K, V, causal=False):
scores = Q @ K.T / np.sqrt(Q.shape[-1])
if causal:
mask = np.triu(np.ones_like(scores), k=1)
scores = np.where(mask, -np.inf, scores)
w = softmax(scores, axis=-1)
return w @ V, w
Running 4 tests…
  • Output shapes
  • Weights rows sum to 1
  • Known values
  • Causal mask blocks the future
+25 XP · Solved first try!
That's real understanding.
12-day streak
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Transformer internals

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Decoding & sampling

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RAG & retrieval

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Level 7 · Architect
1,240 XP
23 days
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Do I need to install anything?+

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