def generate_inner(self, message: List[dict], dataset: str = None) -> str:
        """Generate response for the given message.

        Args:
            message (List[dict]): A list of elements constructed for
                current line.
            dataset (str): The name of the dataset.

        Returns:
            str: The generated response.
        """
        self.set_image_processor(dataset)
        prompt, image_paths = self.message_to_promptimg(message)
        image_paths = [image_paths]
        if dataset == 'HallusionBench':
            prompt = prompt + \
                ' Please answer yes or no. Answer the question using a single word or phrase.'  # noqa
        elif dataset == 'MMVet':
            prompt = prompt + ' Answer this question in detail.'
        else:
            # use default setting
            pass
        pattern = r'<image \d+>'
        prompt = re.sub(pattern, '\n', prompt)
        messages = self.construct_messages(prompt, image_paths)

        generation_config = {
            'max_new_tokens': 2048,
            'temperature': 0.0,
            'top_p': 0.0,
            'num_beams': 1,
        }
        response = self.model.chat(messages,
                                   self.tokenizer,
                                   self.image_processor,
                                   generation_config)
        return response
Generate response for the given message.

        Args:
            message (List[dict]): A list of elements constructed for
                current line.
            dataset (str): The name of the dataset.

        Returns:
            str: The generated response.
        
vlmeval/vlm/points.py