A row of logits in, a token out.

The chain is llama.cpp's default one, in its order:

1. top-k keeps the k highest logits. With a vocabulary of 262144 and a k of 64, sorting the row would cost more than the draw it serves, so the selection runs through a heap of k entries — the root is the worst kept candidate, and anything that does not beat it is dropped on sight. Only the survivors are sorted.
2. top-p softmaxes the survivors and keeps the shortest prefix whose probabilities reach p, including the token that crosses the threshold. One candidate always survives: an empty distribution has nothing to draw from.
3. temperature divides what is left before the final softmax. Zero or less never reaches this point — Pick returns the highest logit straight away.
4. the draw walks the cumulative probabilities against one float from the generator.

Ties, everywhere, go to the lower identifier, which is what llama.cpp does and what makes a greedy run reproducible.

The generator is math/rand/v2's PCG, seeded from Params.Seed. A sampler owns it, so two samplers in the same process do not disturb each other and a seed plus a sequence of logit rows determines the tokens exactly.

s := sample.New(sample.Params{Temperature: 1, TopK: 64, TopP: 0.95, Seed: 7})
id := s.Pick(logits)

Defaults() returns the values Gemma 4's file declares for itself; gemma reads that file's own general.sampling.* keys and falls back to them.

What is not here: repetition and presence penalties, min-p, typical-p, locally typical sampling, mirostat, grammars. They are additions to this chain rather than changes to it, and nothing in golem asks for them yet.