config.json

def load_model_with_quantization_fallback(
    model_name: str = "deepseek-ai/DeepSeek-R1",
    trust_remote_code: bool = True,
    device_map: Optional[Union[str, Dict[str, Any]]] = "auto",
    **kwargs
) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:

  try:
      model = AutoModel.from_pretrained(
          model_name,
          trust_remote_code=trust_remote_code,
          device_map=device_map,
          **kwargs
      )
      tokenizer = AutoTokenizer.from_pretrained(model_name)
      logger.info("Model loaded successfully with original configuration")
      return model, tokenizer
  except ValueError as e:
      if "Unknown quantization type" in str(e):
          logger.warning(
              "Quantization type not supported directly. "
              "Attempting to load without quantization..."
          )
          
          config = AutoConfig.from_pretrained(
              model_name,
              trust_remote_code=trust_remote_code
          )
          if hasattr(config, "quantization_config"):
              delattr(config, "quantization_config")
          
          try:
              model = AutoModel.from_pretrained(
                  model_name,
                  config=config,
                  trust_remote_code=trust_remote_code,
                  device_map=device_map,
                  **kwargs
              )
              tokenizer = AutoTokenizer.from_pretrained(
                  model_name,
                  trust_remote_code=trust_remote_code
              )
              logger.info("Model loaded successfully without quantization")
              return model, tokenizer
              
          except Exception as inner_e:
              logger.error(f"Failed to load model without quantization: {str(inner_e)}")
              raise
      else:
          logger.error(f"Unexpected error during model loading: {str(e)}")
          raise