diff --git a/src/graphnet/models/easy_model.py b/src/graphnet/models/easy_model.py
index b43b7d63e..befc707b9 100644
--- a/src/graphnet/models/easy_model.py
+++ b/src/graphnet/models/easy_model.py
@@ -125,6 +125,7 @@ def fit(
         log_every_n_steps: int = 1,
         gradient_clip_val: Optional[float] = None,
         distribution_strategy: Optional[str] = "ddp",
+        fast_dev_run: Optional[int] = None,
         **trainer_kwargs: Any,
     ) -> None:
         """Fit `StandardModel` using `pytorch_lightning.Trainer`."""
@@ -162,6 +163,7 @@ def fit(
             log_every_n_steps=log_every_n_steps,
             gradient_clip_val=gradient_clip_val,
             distribution_strategy=distribution_strategy,
+            fast_dev_run=fast_dev_run,
             **trainer_kwargs,
         )
 
@@ -174,16 +176,17 @@ def fit(
             pass
 
         # Load weights from best-fit model after training if possible
-        if has_early_stopping & has_model_checkpoint:
-            for callback in callbacks:
-                if isinstance(callback, ModelCheckpoint):
-                    checkpoint_callback = callback
-            self.load_state_dict(
-                torch.load(
-                    checkpoint_callback.best_model_path, weights_only=False
-                )["state_dict"]
-            )
-            self.info("Best-fit weights from EarlyStopping loaded.")
+        if fast_dev_run is None:
+            if has_early_stopping & has_model_checkpoint:
+                for callback in callbacks:
+                    if isinstance(callback, ModelCheckpoint):
+                        checkpoint_callback = callback
+                self.load_state_dict(
+                    torch.load(
+                        checkpoint_callback.best_model_path, weights_only=False
+                    )["state_dict"]
+                )
+                self.info("Best-fit weights from EarlyStopping loaded.")
 
     def _print_callbacks(self, callbacks: List[Callback]) -> None:
         callback_names = []