Fix: use torch_dtype instead of device_map to avoid accelerate dependency

plagiarism_detection.py

@@ -80,13 +80,14 @@ def ai_plagiarism_detection(text, threshold=0.5, show_results=False):
     model_directory = "desklib/ai-text-detector-v1.01"
     # Set up device
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    # Load tokenizer and model directly to the target device to avoid meta tensor issues
+    # Load tokenizer and model
     tokenizer = AutoTokenizer.from_pretrained(model_directory)
+    # Load model to CPU first, then move to device (avoids meta tensor issues)
     model = DesklibAIDetectionModel.from_pretrained(
         model_directory,
-        device_map=device,
-        low_cpu_mem_usage=False
+        torch_dtype=torch.float32
     )
+    model = model.to(device)
     # Predict
     probability, ai_detected = predict_single_text(text, model, tokenizer, device, threshold=threshold)
     # to print results