diff --git a/mnist/main.py b/mnist/main.py
index dee5a384cb..e7618c606d 100644
--- a/mnist/main.py
+++ b/mnist/main.py
@@ -90,8 +90,12 @@ def main():
                         help='random seed (default: 1)')
     parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                         help='how many batches to wait before logging training status')
-    parser.add_argument('--save-model', action='store_true', 
+    parser.add_argument('--save-model', action='store_true',
                         help='For Saving the current Model')
+    parser.add_argument('--model-path', type=str, default='mnist_cnn.pt',
+                        help='path to save the trained model')
+    parser.add_argument('--load-model', type=str, default=None,
+                        help='Path to load a pre-trained model')
     args = parser.parse_args()
 
     use_accel = not args.no_accel and torch.accelerator.is_available()
@@ -125,16 +129,22 @@ def main():
     test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
 
     model = Net().to(device)
-    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
 
-    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
-    for epoch in range(1, args.epochs + 1):
-        train(args, model, device, train_loader, optimizer, epoch)
+    if args.load_model:
+        print(f"Loading model from {args.load_model}")
+        model.load_state_dict(torch.load(args.load_model, map_location=device))
         test(model, device, test_loader)
-        scheduler.step()
+    else:
+        optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
+
+        scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
+        for epoch in range(1, args.epochs + 1):
+            train(args, model, device, train_loader, optimizer, epoch)
+            test(model, device, test_loader)
+            scheduler.step()
 
-    if args.save_model:
-        torch.save(model.state_dict(), "mnist_cnn.pt")
+        if args.save_model:
+            torch.save(model.state_dict(), args.model_path)
 
 
 if __name__ == '__main__':