It never picks a word
Here is the one idea that makes every knob in this module make sense.
A language model does not choose a word. It publishes odds.
Give it The capital of France is and it doesn't reach for "Paris". It scores every token in its vocabulary — somewhere between 50,000 and 200,000 of them, depending on the model — and hands back the whole ranked list with a probability attached to each one:
Paris— 92%the— 3%located— 2%now— 0.4%- ...and a very long tail of near-zero no-hopers
That list is the model's entire output. Every time. It is a forecast, not a decision.
The jar of raffle tickets
Picture a jar. Every candidate token gets raffle tickets in proportion to its probability — Paris gets 92,000 tickets, now gets 400, and thousands of hopeless tokens get one or two each.
To produce a word, something reaches in and draws a ticket.
That draw is sampling. It happens after the model has finished thinking, it is a completely separate step, and it is the only place randomness lives. Same input, same odds — the forward pass is deterministic. The draw is not.
Which gives you the sentence to carry through this topic:
Temperature and top-p never change what the model believes. They change how the jar is stocked before you draw.
Every setting below is jar management. None of it is model management.