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config.json

@@ -3,6 +3,10 @@
   "architectures": [
     "SpatialEmbeddingsModel"
   ],
+  "auto_map": {
+    "AutoConfig": "configuration_spatial_embeddings.SpatialEmbeddingsConfig",
+    "AutoModel": "modeling_spatial_embeddings.SpatialEmbeddingsModel"
+  },
   "backbone_model_name": "facebook/dinov3-vit7b16-pretrain-lvd1689m",
   "dropout": 0.1,
   "dtype": "float32",

modeling_spatial_embeddings.py

@@ -2,9 +2,26 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from transformers import PreTrainedModel, AutoModel
-from .configuration_spatial_embeddings import SpatialEmbeddingsConfig
 from typing import Optional, Tuple, Union, Literal
 
+# Handle import for both local development and HuggingFace Hub loading
+try:
+    from .configuration_spatial_embeddings import SpatialEmbeddingsConfig
+except ImportError:
+    # When loaded from HuggingFace Hub, relative imports may not work
+    # Try absolute import instead
+    try:
+        from configuration_spatial_embeddings import SpatialEmbeddingsConfig
+    except ImportError:
+        # Last resort: import from the module directly
+        import sys
+        from pathlib import Path
+        # Get the directory where this file is located
+        current_dir = Path(__file__).parent
+        if str(current_dir) not in sys.path:
+            sys.path.insert(0, str(current_dir))
+        from configuration_spatial_embeddings import SpatialEmbeddingsConfig
+
 
 class EmbeddingProjector(nn.Module):
     """