Top P

Top P, also known as nucleus sampling, is a method used in large language models (LLMs) to select a subset of possible next tokens where the cumulative probability exceeds a specified threshold 'P'. By sampling from this subset, it ensures a balance between randomness and predictability in a model's outputs. This method offers more dynamic sampling than top K and can lead to more varied and high-quality generated content.

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