diff --git a/README.md b/README.md
index b3eae37c..6b8737eb 100644
--- a/README.md
+++ b/README.md
@@ -86,7 +86,7 @@ Would you like to build/train a model using Keras/Python? And would you like to
 Usage
 -----
 
-1) Use Keras/Python to build (`model.compile(...)`), train (`model.fit(...)`) and test (`model.evaluate(...)`) your model as usual. Then save it to a single HDF5 file using `model.save('....keras')`. The `image_data_format` in your model must be `channels_last`, which is the default when using the TensorFlow backend. Models created with a different `image_data_format` and other backends are not supported.
+1) Use Keras/Python to build (`model.compile(...)`), train (`model.fit(...)`) and test (`model.evaluate(...)`) your model as usual. Then save it to a single file using `model.save('....keras')`. The `image_data_format` in your model must be `channels_last`, which is the default when using the TensorFlow backend. Models created with a different `image_data_format` and other backends are not supported.
 
 2) Now convert it to the frugally-deep file format with `keras_export/convert_model.py`
 
diff --git a/keras_export/convert_model.py b/keras_export/convert_model.py
index 66fa9259..7b5c7443 100755
--- a/keras_export/convert_model.py
+++ b/keras_export/convert_model.py
@@ -887,6 +887,9 @@ def workaround_cudnn_not_found_problem():
     for gpu in gpus:
         tf.config.experimental.set_memory_growth(gpu, True)
 
+def assert_model_type(model):
+    import keras
+    assert type(model) in [keras.src.models.sequential.Sequential, keras.src.models.functional.Functional]
 
 def convert(in_path, out_path, no_tests=False):
     """Convert any (h5-)stored Keras model to the frugally-deep model format."""