sequences generated for each prompt. Common parameters are:
    max_length, max_new_tokens, num_return_sequences, output_scores,
    top_p, top_k, repetition_penalty."""
    streaming: bool = False
    """Whether to stream the results, token by token."""
    @property
    def _identifying_params(self) -> Dict[str, Any]:
        """Get the identifying parameters."""
        return {
            "model": self.model,
            "model_config": self.model_config,
            "generation_config": self.generation_config,
            "streaming": self.streaming,
        }
    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "deepsparse"
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that ``deepsparse`` package is installed."""
        try:
            from deepsparse import Pipeline
        except ImportError:
            raise ImportError(
                "Could not import `deepsparse` package. "
                "Please install it with `pip install deepsparse`"
            )
        model_config = values["model_config"] or {}
        values["pipeline"] = Pipeline.create(
            task="text_generation",
            model_path=values["model"],
            **model_config,
        )
        return values
    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """Generate text from a prompt.
        Args:
            prompt: The prompt to generate text from.
            stop: A list of strings to stop generation when encountered.
        Returns: