This setting affects the diversity of the model's responses. Lower values lead to more predictable and typical responses, while higher values encourage more diverse and less common responses. When set to 0, the model always gives the same response to a given input. View Documentation
FLOAT
This setting limits the model's selection to a certain proportion of the most likely vocabulary: only selecting those top words whose cumulative probability reaches P. Lower values make the model's responses more predictable, while the default setting allows the model to choose from the entire range of vocabulary. View Documentation
FLOAT
This setting aims to control the reuse of vocabulary based on its frequency in the input. It attempts to use less of those words that appear more frequently in the input, with usage frequency proportional to occurrence frequency. Vocabulary penalties increase with frequency of occurrence. Negative values encourage vocabulary reuse. View Documentation
FLOAT
This setting adjusts the frequency at which the model reuses specific vocabulary that has already appeared in the input. Higher values reduce the likelihood of such repetition, while negative values have the opposite effect. Vocabulary penalties do not increase with frequency of occurrence. Negative values encourage vocabulary reuse. View Documentation
FLOAT