diff --git a/s3torchbenchmarking/src/s3torchbenchmarking/models.py b/s3torchbenchmarking/src/s3torchbenchmarking/models.py
index 94e62a56..9520eada 100644
--- a/s3torchbenchmarking/src/s3torchbenchmarking/models.py
+++ b/s3torchbenchmarking/src/s3torchbenchmarking/models.py
@@ -279,43 +279,3 @@ def save_checkpoint_to_disk(model: nn.Module, uri: str, batch_idx: int):
     save_time = end_time - start_time
     print(f"Saving checkpoint to {path} took {save_time} seconds")
     return save_time
-
-
-def run():
-    model = LightningAdapter(
-        sample_transformer=Transforms.transform_image,
-        model=ViTForImageClassification.from_pretrained(
-            "google/vit-base-patch16-224-in21k", num_labels=1024
-        ),
-        config=DictConfig(
-            content={
-                "checkpoint": {
-                    "destination": "disk",
-                    "uri": "/tmp/checkpoints/",
-                    "save_one_in": 2,
-                    # "destination": "s3",
-                    # "uri": "s3://swift-benchmark-dataset/checkpoints/",
-                    "region": "eu-west-2",
-                }
-            }
-        ),
-    )
-    # model = ViT(1024, DictConfig({
-    #     "destination": "disk",
-    #     "save_one_in": 1,
-    #     "uri": "checkpoints/",
-    #     "region": "eu-west-2",
-    # }))
-    dataset = S3IterableDataset.from_prefix(
-        "s3://swift-benchmark-dataset/4_images/", region="eu-west-2"
-    )
-    dataset = torchdata.datapipes.iter.IterableWrapper(dataset)
-    dataset = dataset.map(model.load_sample)
-    dataset = dataset.sharding_filter()
-    dataloader = DataLoader(dataset=dataset, num_workers=8)
-    result = model.train(dataloader=dataloader, epochs=1)
-    print(f"print!s: {result=!s}")
-
-
-if __name__ == "__main__":
-    run()