output. A temperature less than 1 favors more correctness and is appropriate 
        for question answering or summarization. A value greater than 1 introduces more 
        randomness in the output.
    """
    top_k: Optional[int] = None
    """Used to limit the number of choices for the next predicted word or token. It 
        specifies the maximum number of tokens to consider at each step, based on their 
        probability of occurrence. This technique helps to speed up the generation 
        process and can improve the quality of the generated text by focusing on the 
        most likely options.
    """
    max_tokens: Optional[int] = None
    """The maximum number of tokens to generate."""
    repetition_penalty: Optional[float] = None
    """A number that controls the diversity of generated text by reducing the 
        likelihood of repeated sequences. Higher values decrease repetition.
    """
    logprobs: Optional[int] = None
    """An integer that specifies how many top token log probabilities are included in 
        the response for each token generation step.
    """
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key exists in environment."""
        values["together_api_key"] = convert_to_secret_str(
            get_from_dict_or_env(values, "together_api_key", "TOGETHER_API_KEY")
        )
        return values
    @property
    def _llm_type(self) -> str:
        """Return type of model."""
        return "together"
    def _format_output(self, output: dict) -> str:
        return output["output"]["choices"][0]["text"]