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Temperature and Sampling Explained

Revision as of Jun 27, 2026 23:47 by albert.

At each step a model outputs a probability for every possible next token. Sampling is how one is chosen — and the knobs control the trade-off between focused and creative. A deeper companion to Temperature & Sampling: Determinism vs Creativity.

The knobs

Setting What it does
Greedy (temp 0) Always pick the single most likely token — deterministic, repetitive
Temperature Flattens/sharpens the distribution. Low = safe/focused, high = wild/creative
Top-k Only consider the k most likely tokens
Top-p (nucleus) Consider the smallest set whose probability sums to p (e.g. 0.9)
Repetition penalty Discourage repeating recent tokens

How temperature reshapes choices

temp 0.2 → [the 0.85] [a 0.10] ...   → almost always "the"  (focused)
temp 1.2 → [the 0.40] [a 0.25] ...   → more variety         (creative)

Sensible defaults

Task Settings
Code / facts / extraction temp 0–0.3
General chat temp 0.7, top-p 0.9
Brainstorm / fiction temp 1.0+

Set these per task — see Ollama Parameters Guide for where they live.

Related: Temperature & Sampling: Determinism vs Creativity · Ollama Parameters Guide · Why LLMs Hallucinate