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- Temperature scales the logits before sampling: lower values (close to 0) make the model more deterministic and pick the highest-probability token; higher values increase randomness. Top-p (nucleus) and top-k are complementary sampling controls. Learning rate, batch size, and dropout are training-time hyperparameters not exposed at inference.