The actual underlying neural net that the LLMs use doesn't actually output tokens. It outputs a probability distribution for how likely each token is to come ne

The actual underlying neural net that the LLMs use doesn't actually output tokens. It outputs a probability distribution for how likely each token is to come next. For example, in the sentence "once upon a ", the token with the highest probability is "time", and then probably "child", and so on. In order to make this probability distribution useful, the software chooses a token based on its position in the distribution. I'm simplifying here, but the likelihood that it chooses the most probable next token is based on the model's temperature. A temperature of 0 means that (in theory) it'll always choose the most probable token, making it deterministic. A non-zero temperature means that sometimes it will choose less likely tokens, so it'll output different results every time. Hope this helps.