Meta Llama 3.1 70B

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

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

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

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

This setting defines the maximum length that the model can generate in a single response. Setting a higher value allows the model to produce longer replies, while a lower value restricts the length of the response, making it more concise. Adjusting this value appropriately based on different application scenarios can help achieve the desired response length and level of detail. View Documentation

This setting controls the intensity of reasoning the model applies before generating a response. Low intensity prioritizes response speed and saves tokens, while high intensity provides more comprehensive reasoning but consumes more tokens and slows down response time. The default value is medium, balancing reasoning accuracy with response speed. View Documentation