The `top_p` parameter (range: 0.0 - 1.0, default 0.95)

#### The `top_p` parameter (range: 0.0 - 1.0, default 0.95) ##### What is _top_p_? `top_p` controls how the model selects tokens for output by adjusting the probability distribution of the next word in the generated text based on a cumulative probability cutoff. Specifically, it selects the smallest set of tokens whose cumulative probability exceeds the given cutoff probability _p_, and samples from this set uniformly. For example, suppose tokens A, B, and C have a probability of 0.3, 0.2, and 0.1, and the `top_p` value is 0.5. In that case, the model will select either A or B as the next token (using temperature) and not consider C, because the cumulative probability of top_p is <= 0.5. Specify a lower value for less random responses and a higher value for more random responses. ##### How does _top_p_ affect the response? The `top_p` parameter is used to control the diversity of the generated text. A higher `top_p` parameter value results in more "diverse" and "interesting" outputs, with the model being allowed to sample from a larger pool of possibilities. In contrast, a lower `top_p` parameter value resulted in more predictable outputs, with the model being constrained to a smaller set of possible tokens. ##### Example: `top_p = 0.1`: - The cat sat on the mat. - The cat sat on the floor. `top_p = 0.9`: - The cat sat on the windowsill, soaking up the sun's rays. - The cat sat on the edge of the bed, watching the birds outside. For more information on the `top_p` parameter for text models, please refer to the [documentation on model parameters](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/models#text_model_parameters).