Answer: Low temperature and fixed random seed

## Explanation To achieve **consistent, reproducible outputs** across multiple calls to the same model with the same input, the correct configuration is: - **Low temperature**: Temperature controls the randomness of the output. Lower temperature values (closer to 0) make the model more deterministic and focused on the most likely tokens, reducing randomness. - **Fixed random seed**: Setting a fixed seed ensures that the random number generator produces the same sequence of values each time, making the output reproducible. ### Why other options are incorrect: - **A) High temperature**: Increases randomness and variability in outputs, making them less consistent. - **B) Randomized seed**: Using different seeds would produce different outputs even with the same input. - **D) High top-p value**: Top-p (nucleus sampling) filters the probability distribution, but doesn't guarantee reproducibility like a fixed seed does. This combination ensures deterministic behavior while maintaining model quality for consistent results across multiple inference calls.

Author: Ritesh Yadav