diff --git a/clip_interrogator/clip_interrogator.py b/clip_interrogator/clip_interrogator.py
index 30545a8..c47aa93 100644
--- a/clip_interrogator/clip_interrogator.py
+++ b/clip_interrogator/clip_interrogator.py
@@ -15,7 +15,7 @@ from PIL import Image
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 from tqdm import tqdm
-from typing import List
+from typing import List, Union
 
 BLIP_MODELS = {
     "base": "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth",
@@ -64,7 +64,7 @@ class Config:
     )
     chunk_size: int = 2048  # batch size for CLIP, use smaller for lower VRAM
     data_path: str = os.path.join(os.path.dirname(__file__), "data")
-    device: str = (
+    device: Union[str, torch.device] = (
         "mps"
         if torch.backends.mps.is_available()
         else "cuda"
@@ -89,6 +89,7 @@ class Interrogator:
             blip_path = os.path.dirname(inspect.getfile(blip_decoder))
             configs_path = os.path.join(os.path.dirname(blip_path), "configs")
             med_config = os.path.join(configs_path, "med_config.json")
+
             blip_model = blip_decoder(
                 pretrained=BLIP_MODELS[config.blip_model_type],
                 image_size=config.blip_image_eval_size,
@@ -137,7 +138,14 @@ class Interrogator:
             ) = open_clip.create_model_and_transforms(
                 clip_model_name,
                 pretrained=clip_model_pretrained_name,
-                precision="fp16" if config.device == "cuda" else "fp32",
+                precision="fp16"
+                if (
+                    config.device.type
+                    if isinstance(config.device, torch.device)
+                    else config.device
+                )
+                == "cuda"
+                else "fp32",
                 device="cpu",
                 jit=False,
                 cache_dir=config.clip_model_path,
@@ -480,7 +488,9 @@ class Interrogator:
         )
 
         fast_prompt = self._interrogate_fast(caption, image_features, max_flavours)
-        classic_prompt = self.interrogate_classic(caption, image_features, max_flavours)
+        classic_prompt = self._interrogate_classic(
+            caption, image_features, max_flavours
+        )
         candidates = [caption, classic_prompt, fast_prompt, best_prompt]
         return candidates[np.argmax(self.similarities(image_features, candidates))]