Top-k sampling
Also: top-k
Keeping only the k highest-probability tokens, renormalising across them, and sampling from what is left. A fixed-size cutoff, regardless of how confident the model was.
A definition is not an understanding. This is taught properly in How a model picks the next token, part of Sampling & decoding.
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