After creating a foundation model in Einstein Studio, which hyperparameter should an AI Specialist use to adjust the balance between consistency and randomness of a response?
The Temperature hyperparameter controls the randomness of model outputs:
Low Temperature (e.g., 0.2): More deterministic, consistent responses.
High Temperature (e.g., 1.0): More creative, varied responses.
Presence Penalty (Option A): Discourages repetition of tokens, unrelated to randomness.
Variability (Option B): Not a standard hyperparameter in Einstein Studio.
References:
Einstein Studio Documentation: Model Hyperparameters
Explicitly states "Temperature adjusts the balance between predictable and random outputs."
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