A parameter (typically 0–2) that controls the randomness of an LLM's output by scaling the logits before sampling. Lower values make output more deterministic.
A dial that controls how creative or boring the AI is — turn it up for silly, turn it down for serious.
A setting that controls how random or predictable the AI's answers are. Low temperature = focused and consistent. High temperature = creative and surprising.
A parameter (typically 0–2) that controls the randomness of an LLM's output by scaling the logits before sampling. Lower values make output more deterministic.
A softmax temperature parameter that modulates the entropy of the output token distribution — lower values sharpen the distribution toward greedy decoding, higher values increase sampling diversity at the cost of coherence.
The inverse scaling factor τ applied to logits z before softmax normalization: P(xᵢ) = exp(zᵢ/τ)/Σexp(zⱼ/τ) — controlling the entropy of the categorical distribution and thus the explore-exploit tradeoff during autoregressive generation.
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