diff --git a/clip_interrogator/clip_interrogator.py b/clip_interrogator/clip_interrogator.py
index f3f3d6c..a634436 100644
--- a/clip_interrogator/clip_interrogator.py
+++ b/clip_interrogator/clip_interrogator.py
@@ -16,6 +16,10 @@ from torchvision.transforms.functional import InterpolationMode
 from tqdm import tqdm
 from typing import List
 
+BLIP_MODELS = {
+    'base': 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth',
+    'large': 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth'
+}
 
 @dataclass 
 class Config:
@@ -27,7 +31,7 @@ class Config:
     # blip settings
     blip_image_eval_size: int = 384
     blip_max_length: int = 32
-    blip_model_url: str = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth'
+    blip_model_type: str = 'large' # choose between 'base' or 'large'
     blip_num_beams: int = 8
     blip_offload: bool = False
 
@@ -39,11 +43,10 @@ class Config:
     cache_path: str = 'cache'
     chunk_size: int = 2048
     data_path: str = os.path.join(os.path.dirname(__file__), 'data')
-    device: str = 'cuda' if torch.cuda.is_available() else 'cpu'
+    device: str = ("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu")
     flavor_intermediate_count: int = 2048
     quiet: bool = False # when quiet progress bars are not shown
 
-
 class Interrogator():
     def __init__(self, config: Config):
         self.config = config
@@ -56,9 +59,9 @@ class Interrogator():
             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=config.blip_model_url, 
+                pretrained=BLIP_MODELS[config.blip_model_type],
                 image_size=config.blip_image_eval_size, 
-                vit='large', 
+                vit=config.blip_model_type, 
                 med_config=med_config
             )
             blip_model.eval()