You probably don't want temp 0, especially with Gemini which often fails in the greedy sampling mode in practice, often in ridiculous ways (e.g. multiple thousand token loops). As another comment says it's pretty brittle and this is even reflected in their docs somewhere IIRC. You want the "normal" temperature (whatever it is) and oversampling if necessary.
Certain open models have/had the temperature locked on the official APIs, I assume they just have sampling incompatible with static temperature or do some fancy speculative decoding. It's clearly not to hide anything, as the weights are open and there are always alternative providers.
Certain open models have/had the temperature locked on the official APIs, I assume they just have sampling incompatible with static temperature or do some fancy speculative decoding. It's clearly not to hide anything, as the weights are open and there are always alternative providers.