diff --git a/examples/callbacks/SAM.py b/examples/callbacks/SAM.py
index dbb215b4..93f1c150 100644
--- a/examples/callbacks/SAM.py
+++ b/examples/callbacks/SAM.py
@@ -36,6 +36,8 @@
     },
 )
 
-# run a quick training loop
-trainer = pl.Trainer(fast_dev_run=1000, callbacks=[SAM()])
+# run a quick training loop, skipping the first five steps
+trainer = pl.Trainer(
+    fast_dev_run=100, callbacks=[SAM(skip_step_count=5, logging=True, log_level="INFO")]
+)
 trainer.fit(task, datamodule=dm)
diff --git a/matsciml/lightning/callbacks.py b/matsciml/lightning/callbacks.py
index af7ffa54..3239e830 100644
--- a/matsciml/lightning/callbacks.py
+++ b/matsciml/lightning/callbacks.py
@@ -776,12 +776,12 @@ def __init__(
         super().__init__()
         self.rho = rho
         self.adaptive = adaptive
-        if skip_epoch_count and skip_epoch_count:
+        if skip_step_count and skip_epoch_count:
             raise ValueError(
                 "`skip_epoch_count` and `skip_step_count` are mutually exclusive for SAM."
             )
         self.skip_step_count = skip_step_count
-        if skip_epoch_count and not skip_epoch_count.is_integer():
+        if skip_epoch_count and isinstance(skip_epoch_count, float):
             assert (
                 0 < skip_epoch_count < 1.0
             ), "Decimal `skip_epoch_count` passed not within [0,1]."
@@ -808,8 +808,12 @@ def on_fit_start(
             self.max_steps = train_len * self.max_epochs
         # if a fractional epoch skip is specified, convert it to
         # an integer count for easier comparison
-        if self.skip_epoch_count and not self.skip_epoch_count.is_integer():
+        if self.skip_epoch_count and isinstance(self.skip_epoch_count, float):
             self.skip_epoch_count = int(self.max_epochs * self.skip_epoch_count)
+            if self.logger:
+                self.logger.info(
+                    f"Fractional epoch skip - will start SAM from epoch {self.skip_epoch_count}, max epochs {self.max_epochs}."
+                )
         # add floating point epsilon for later use
         self.epsilon = torch.tensor(
             [torch.finfo(pl_module.dtype).eps],