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== Top_p == Top_p tells the model to pick the next word from the top tokens based on the sum of their probabilities. For example, if p is 0.9, then the model will only consider the words that have a cumulative probability of 0.9 or higher as the next word. This means that the number of candidates can vary depending on their probabilities. This is more dynamic than top_k and is often used to exclude outputs with lower probabilities.
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