[genrilz]

Almost certainly not if you set the temperature of the model to 0, since then the output would be deterministic minus MoE stuff.

If the temperature was not zero, then it seems technically possible for the output tokens to weighted closely enough in probability to each other in a way such that the randomization from temperature causes tokens to be printed in the appropriate distribution.

However, I'm not an LLM expert, but I don't think that people use a "temperature" while training the model. Thus the training step would not be able to learn how to output tokens in the given distribution with a given temperature because the training step does not have access to the temperature the user is using.

EDIT: I made the assumption that the LLM was not asked for a sequence of random numbers, but only one number per prompt. I think this fits the use case described in the article, but another use case might be asking for a sequence of such numbers, in which case training might work.