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What is Top-P?
Top-P, also known as nucleus sampling, is a configuration setting used in Large Language Models (LLMs) to restrict the predicted next token to come from tokens with the top predicted probabilities[1]. This sampling setting helps to control the randomness and diversity of generated text[1]. Top-P sampling selects the top tokens whose cumulative probability does not exceed a certain value (P)[1]. Values for P range from 0 (greedy decoding) to 1 (all tokens in the LLM’s vocabulary)[1].
The best way to choose between top-K and top-P is to experiment with both methods (or both together) and see which one produces the results you are looking for[1].
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