Upload lightonai_LightOnOCR-1B-1025_0.py with huggingface_hub

lightonai_LightOnOCR-1B-1025_0.py

@@ -4,6 +4,8 @@
 #     "torch",
 #     "torchvision",
 #     "transformers",
+#     "diffusers",
+#     "sentence-transformers",
 #     "accelerate",
 #     "peft",
 #     "slack-sdk",
@@ -11,13 +13,19 @@
 # ///
 
 try:
-    # integration status unknown.
-      
-    # Please clone model and use locally.
+    import torch
+    from PIL import Image
+    from transformers import AutoProcessor, LightOnOCRForConditionalGeneration
+    import requests
+    from io import BytesIO
     
-    # Also feel free to open a Pull request 
-    # for integration of the huggingface model hub
-    # into the corresponding library =)
+    # Load Model
+    model_id = "lightonai/LightOnOCR-1B-1025"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    processor = AutoProcessor.from_pretrained(model_id)
+    model = LightOnOCRForConditionalGeneration.from_pretrained(model_id, dtype=torch.bfloat16, device_map=device, attn_implementation="sdpa")
+    model.eval();
     with open('lightonai_LightOnOCR-1B-1025_0.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in lightonai_LightOnOCR-1B-1025_0.txt')
 except Exception as e:
@@ -32,13 +40,19 @@ except Exception as e:
     with open('lightonai_LightOnOCR-1B-1025_0.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-# integration status unknown.
-  
-# Please clone model and use locally.
+import torch
+from PIL import Image
+from transformers import AutoProcessor, LightOnOCRForConditionalGeneration
+import requests
+from io import BytesIO
+
+# Load Model
+model_id = "lightonai/LightOnOCR-1B-1025"
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Also feel free to open a Pull request 
-# for integration of the huggingface model hub
-# into the corresponding library =)
+processor = AutoProcessor.from_pretrained(model_id)
+model = LightOnOCRForConditionalGeneration.from_pretrained(model_id, dtype=torch.bfloat16, device_map=device, attn_implementation="sdpa")
+model.eval();
 ```
 
 ERROR: