Temperature is a parameter that controls the randomness and creativity of text generated by an autoregressive language model during inference. Higher values increase randomness, while lower values make outputs more deterministic.
Helps AI builders design and scale robust architectures; mastering the implementation of Temperature improves latency, accuracy, and operational efficiency for response tuning, generation formatting control, and brainstorming triggers.
Temperature is a configuration parameter that controls the randomness and creativity of generative model outputs. By scaling the logits before the softmax calculation, a low temperature (near 0) forces deterministic, high-probability token selections, while a high temperature (near 1) increases token diversity.
The model becomes deterministic, choosing the single most probable word token at every step (greedy decoding).
During creative writing tasks, brainstorming sessions, or scenario generation where diverse outputs are desired.
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