def __init__(self, **kwargs: Any) -> None:
        """
        Initializes the HuggingFace multi-modal model and processor based on the provided configuration.
        """
        super().__init__(**kwargs)
        try:
            # Load model configuration
            self._config = AutoConfig.from_pretrained(
                self.model_name, trust_remote_code=True
            )
            architecture = self._config.architectures[0]
            AutoModelClass = AutoModelForCausalLM  # Default model class

            # Special cases for specific model architectures
            if "Qwen2VLForConditionalGeneration" in architecture:
                AutoModelClass = Qwen2VLForConditionalGeneration
            if "PaliGemmaForConditionalGeneration" in architecture:
                AutoModelClass = PaliGemmaForConditionalGeneration
            if "MllamaForConditionalGeneration" in architecture:
                AutoModelClass = MllamaForConditionalGeneration

            # Load the model based on the architecture
            self._model = AutoModelClass.from_pretrained(
                self.model_name,
                device_map=self.device_map,
                torch_dtype=self.torch_dtype,
                trust_remote_code=self.trust_remote_code,
                **self.additional_kwargs,
            )
            # Load the processor (for handling text and image inputs)
            self._processor = AutoProcessor.from_pretrained(
                self.model_name, trust_remote_code=self.trust_remote_code
            )
        except Exception as e:
            raise ValueError(f"Failed to initialize the model and processor: {e!s}")

        Initializes the HuggingFace multi-modal model and processor based on the provided configuration.
        
llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-huggingface/llama_index/multi_modal_llms/huggingface/base.py