prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        is_retry: bool = False,
        **kwargs: Any,
    ) -> str:
        """Call out to a MosaicML LLM inference endpoint.
        Args:
            prompt: The prompt to pass into the model.
            stop: Optional list of stop words to use when generating.
        Returns:
            The string generated by the model.
        Example:
            .. code-block:: python
                response = mosaic_llm("Tell me a joke.")
        """
        _model_kwargs = self.model_kwargs or {}
        prompt = self._transform_prompt(prompt)
        payload = {"inputs": [prompt]}
        payload.update(_model_kwargs)
        payload.update(kwargs)
        # HTTP headers for authorization
        headers = {
            "Authorization": f"{self.mosaicml_api_token}",
            "Content-Type": "application/json",
        }
        # send request
        try:
            response = requests.post(self.endpoint_url, headers=headers, json=payload)
        except requests.exceptions.RequestException as e:
            raise ValueError(f"Error raised by inference endpoint: {e}")
        try:
            if response.status_code == 429:
                if not is_retry:
                    import time
                    time.sleep(self.retry_sleep)
                    return self._call(prompt, stop, run_manager, is_retry=True)
                raise ValueError(
                    f"Error raised by inference API: rate limit exceeded.\nResponse: "
                    f"{response.text}"
                )
            parsed_response = response.json()
            # The inference API has changed a couple of times, so we add some handling
            # to be robust to multiple response formats.