The tunable hyperparameters include:

model - this is a required input, specifying the model ID to use.
prompt/messages - the input prompt/messages to the model, which provides the context for the text generation task.
max_tokens - the maximum number of tokens (words or word pieces) to generate in the output.
temperature - a value between 0 and 1 that controls the randomness of the generated text. A higher temperature will result in more random and diverse text, while a lower temperature will result in more predictable text.
top_p - a value between 0 and 1 that controls the sampling probability mass for each token generation. A lower top_p value will make it more likely to generate text based on the most likely tokens, while a higher value will allow the model to explore a wider range of possible tokens.
n - the number of responses to generate for a given prompt. Generating multiple responses can provide more diverse and potentially more useful output, but it also increases the cost of the request.
stop - a list of strings that, when encountered in the generated text, will cause the generation to stop. This can be used to control the length or the validity of the output.
presence_p