diff --git a/plugins/train/model/_base/io.py b/plugins/train/model/_base/io.py
index 0cce82d93c..1ce357eced 100644
--- a/plugins/train/model/_base/io.py
+++ b/plugins/train/model/_base/io.py
@@ -23,6 +23,7 @@
 
 if T.TYPE_CHECKING:
     from .model import ModelBase
+    from keras.optimizers import Optimizer
 
 logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
 
@@ -168,42 +169,46 @@ def load(self) -> kmodels.Model:
         logger.info("Loaded model from disk: '%s'", self.filename)
         return model
 
-    def save(self,
-             is_exit: bool = False,
-             force_save_optimizer: bool = False) -> None:
-        """ Backup and save the model and state file.
+    def _remove_optimizer(self) -> Optimizer:
+        """ Keras 3 `.keras` format ignores the `save_optimizer` kwarg. To hack around this we
+        remove the optimizer from the model prior to saving and then re-attach it to the model
+
+        Returns
+        -------
+        :class:`keras.optimizers.Optimizer` | None
+            The optimizer for the model, if it should not be saved. ``None`` if it should be saved
+        """
+        retval = self._plugin.model.optimizer
+        del self._plugin.model.optimizer
+        logger.debug("Removed optimizer for saving: %s", retval)
+        return retval
+
+    def _save_model(self, is_exit: bool, force_save_optimizer: bool) -> None:
+        """ Save the model either with or without the optimizer weights
+
+        Keras 3 ignores 'save_optimizer` so if it should not be saved, we remove it from
+        the model for saving, then re-attach it
 
         Parameters
         ----------
-        is_exit: bool, optional
+        is_exit: bool
             ``True`` if the save request has come from an exit process request otherwise ``False``.
-            Default: ``False``
-        force_save_optimizer: bool, optional
+        force_save_optimizer: bool
             ``True`` to force saving the optimizer weights with the model, otherwise ``False``.
-            Default:``False``
         """
-        logger.debug("Backing up and saving models")
         include_optimizer = (force_save_optimizer or
                              self._save_optimizer == "always" or
                              (self._save_optimizer == "exit" and is_exit))
 
-        print("\x1b[2K", end="\r")  # Clear last line
-        logger.info("Saving Model...")
-        self._plugin.model.save(self.filename, include_optimizer=include_optimizer)
+        if not include_optimizer:
+            optimizer = self._remove_optimizer()
+
+        self._plugin.model.save(self.filename)
         self._plugin.state.save()
 
-        save_average = self._get_save_average()
-        should_backup = self._should_backup(save_average)
-        if save_average and should_backup:
-            self._backup.backup_model(self.filename)
-            self._backup.backup_model(self._plugin.state.filename)
-
-        msg = "[Saved optimizer state for Snapshot]" if force_save_optimizer else "[Saved model]"
-        if save_average:
-            msg += f" - Average total loss since last save: {save_average:.5f}"
-        if should_backup:
-            msg += " [Model backed up]"
-        logger.info(msg)
+        if not include_optimizer:
+            logger.debug("Re-attaching optimizer: %s", optimizer)
+            setattr(self._plugin.model, "optimizer", optimizer)
 
     def _get_save_average(self) -> float:
         """ Return the average loss since the last save iteration and reset historical loss
@@ -215,7 +220,7 @@ def _get_save_average(self) -> float:
         """
         logger.debug("Getting save averages")
         if not self._history:
-            logger.info("No loss in history")
+            logger.debug("No loss in history")
             retval = 0.0
         else:
             retval = sum(self._history) / len(self._history)
@@ -258,6 +263,56 @@ def _should_backup(self, save_average: float) -> bool:
         logger.debug("Should backup: %s", backup)
         return backup
 
+    def _maybe_backup(self) -> tuple[float, bool]:
+        """ Backup the model if total average loss has dropped for the save iteration
+
+        Returns
+        -------
+        float
+            The total loss average since the last save iteration
+        bool
+            ``True`` if the model was backed up
+        """
+        save_average = self._get_save_average()
+        should_backup = self._should_backup(save_average)
+        if not save_average or not should_backup:
+            logger.debug("Not backing up model (save_average: %s, should_backup: %s)",
+                         save_average, should_backup)
+            return save_average, False
+
+        logger.debug("Backing up model")
+        self._backup.backup_model(self.filename)
+        self._backup.backup_model(self._plugin.state.filename)
+        return save_average, True
+
+    def save(self,
+             is_exit: bool = False,
+             force_save_optimizer: bool = False) -> None:
+        """ Backup and save the model and state file.
+
+        Parameters
+        ----------
+        is_exit: bool, optional
+            ``True`` if the save request has come from an exit process request otherwise ``False``.
+            Default: ``False``
+        force_save_optimizer: bool, optional
+            ``True`` to force saving the optimizer weights with the model, otherwise ``False``.
+            Default:``False``
+        """
+        logger.debug("Backing up and saving models")
+        print("\x1b[2K", end="\r")  # Clear last line
+        logger.info("Saving Model...")
+
+        self._save_model(is_exit, force_save_optimizer)
+        save_average, backed_up = self._maybe_backup()
+
+        msg = "[Saved optimizer state for Snapshot]" if force_save_optimizer else "[Saved model]"
+        if save_average:
+            msg += f" - Average total loss since last save: {save_average:.5f}"
+        if backed_up:
+            msg += " [Model backed up]"
+        logger.info(msg)
+
     def snapshot(self) -> None:
         """ Perform a model snapshot.