diff --git a/keras_nlp/src/api_export.py b/keras_nlp/src/api_export.py
index cfa3519ce9..050f3ee964 100644
--- a/keras_nlp/src/api_export.py
+++ b/keras_nlp/src/api_export.py
@@ -22,7 +22,16 @@
     namex = None
 
 
-def maybe_register_serializable(symbol):
+def maybe_register_serializable(path, symbol):
+    # If we have multiple export names, actually make sure to register these
+    # first. This makes sure we have a backward compat mapping of old serialized
+    # name to new class.
+    if isinstance(path, (list, tuple)):
+        for name in path:
+            name = name.split(".")[-1]
+            keras.saving.register_keras_serializable(
+                package="keras_nlp", name=name
+            )(symbol)
     if isinstance(symbol, types.FunctionType) or hasattr(symbol, "get_config"):
         keras.saving.register_keras_serializable(package="keras_nlp")(symbol)
 
@@ -34,7 +43,7 @@ def __init__(self, path):
             super().__init__(package="keras_nlp", path=path)
 
         def __call__(self, symbol):
-            maybe_register_serializable(symbol)
+            maybe_register_serializable(self.path, symbol)
             return super().__call__(symbol)
 
 else:
diff --git a/keras_nlp/src/models/bert/bert_text_classifier_test.py b/keras_nlp/src/models/bert/bert_text_classifier_test.py
index 44b9a3d846..fa4f4cadfc 100644
--- a/keras_nlp/src/models/bert/bert_text_classifier_test.py
+++ b/keras_nlp/src/models/bert/bert_text_classifier_test.py
@@ -67,6 +67,15 @@ def test_saved_model(self):
             input_data=self.input_data,
         )
 
+    @pytest.mark.large
+    def test_smallest_preset(self):
+        self.run_preset_test(
+            cls=BertTextClassifier,
+            preset="bert_tiny_en_uncased_sst2",
+            input_data=self.input_data,
+            expected_output_shape=(2, 2),
+        )
+
     @pytest.mark.extra_large
     def test_all_presets(self):
         for preset in BertTextClassifier.presets:
diff --git a/keras_nlp/src/utils/preset_utils.py b/keras_nlp/src/utils/preset_utils.py
index 34c96de777..bb93e38e4d 100644
--- a/keras_nlp/src/utils/preset_utils.py
+++ b/keras_nlp/src/utils/preset_utils.py
@@ -582,7 +582,16 @@ def load_serialized_object(config, **kwargs):
 
 def check_config_class(config):
     """Validate a preset is being loaded on the correct class."""
-    return keras.saving.get_registered_object(config["registered_name"])
+    registered_name = config["registered_name"]
+    cls = keras.saving.get_registered_object(registered_name)
+    if cls is None:
+        raise ValueError(
+            f"Attempting to load class {registered_name} with "
+            "`from_preset()`, but there is no class registered with Keras "
+            f"for {registered_name}. Make sure to register any custom "
+            "classes with `register_keras_serializable()`."
+        )
+    return cls
 
 
 def jax_memory_cleanup(layer):
diff --git a/keras_nlp/src/utils/preset_utils_test.py b/keras_nlp/src/utils/preset_utils_test.py
index 0e3e022d82..1c73c863b4 100644
--- a/keras_nlp/src/utils/preset_utils_test.py
+++ b/keras_nlp/src/utils/preset_utils_test.py
@@ -12,8 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 import os
 
+import keras
 import pytest
 from absl.testing import parameterized
 
@@ -38,6 +40,15 @@ def test_preset_errors(self):
         with self.assertRaisesRegex(ValueError, "Unknown preset identifier"):
             AlbertTextClassifier.from_preset("snaggle://bort/bort/bort")
 
+        backbone = BertBackbone.from_preset("bert_tiny_en_uncased")
+        preset_dir = self.get_temp_dir()
+        config = keras.utils.serialize_keras_object(backbone)
+        config["registered_name"] = "keras_nlp>BortBackbone"
+        with open(os.path.join(preset_dir, CONFIG_FILE), "w") as config_file:
+            config_file.write(json.dumps(config, indent=4))
+        with self.assertRaisesRegex(ValueError, "class keras_nlp>BortBackbone"):
+            BertBackbone.from_preset(preset_dir)
+
     def test_upload_empty_preset(self):
         temp_dir = self.get_temp_dir()
         empty_preset = os.path.join(temp_dir, "empty")