diff --git a/neuralprophet/utils.py b/neuralprophet/utils.py
index 2c198b929..fd209cb56 100644
--- a/neuralprophet/utils.py
+++ b/neuralprophet/utils.py
@@ -23,31 +23,50 @@
 
 
 def save(forecaster, path: str):
-    """save a fitted np model to a disk file.
+    """Save a fitted Neural Prophet model to disk.
 
-    Parameters
-    ----------
+    Parameters:
         forecaster : np.forecaster.NeuralProphet
             input forecaster that is fitted
         path : str
             path and filename to be saved. filename could be any but suggested to have extension .np.
-    Examples
-    --------
+
     After you fitted a model, you may save the model to save_test_model.np
         >>> from neuralprophet import save
         >>> save(forecaster, "test_save_model.np")
     """
-    # Remove the Lightning trainer since it does not serialise correcly with torch.save
-    attrs_to_remove = ["trainer"]
+    # List of attributes to remove
+    attrs_to_remove_forecaster = ["trainer"]
+    attrs_to_remove_model = ["_trainer"]
+
+    # Store removed attributes temporarily
     removed_attrs = {}
-    for attr in attrs_to_remove:
-        removed_attrs[attr] = getattr(forecaster, attr)
-        setattr(forecaster, attr, None)
-    torch.save(forecaster, path)
-
-    # Restore the Lightning trainer
-    for attr in attrs_to_remove:
-        setattr(forecaster, attr, removed_attrs[attr])
+
+    # Remove specified attributes from forecaster
+    for attr in attrs_to_remove_forecaster:
+        if hasattr(forecaster, attr):
+            removed_attrs[attr] = getattr(forecaster, attr)
+            setattr(forecaster, attr, None)
+
+    # Remove specified attributes from forecaster.model
+    for attr in attrs_to_remove_model:
+        if hasattr(forecaster.model, attr):
+            removed_attrs[attr] = getattr(forecaster.model, attr)
+            setattr(forecaster.model, attr, None)
+
+    # Perform the save operation
+    try:
+        torch.save(forecaster, path)
+    except Exception as e:
+        print(f"An error occurred while saving the model: {e}")
+        raise
+    finally:
+        # Restore the removed attributes
+        for attr, value in removed_attrs.items():
+            if hasattr(forecaster, attr):
+                setattr(forecaster, attr, value)
+            elif hasattr(forecaster.model, attr):
+                setattr(forecaster.model, attr, value)
 
 
 def load(path: str):