diff --git a/model_conditioning_examples/weight_clustering.py b/model_conditioning_examples/weight_clustering.py
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+#  Copyright (c) 2021 Arm Limited. All rights reserved.
+#  SPDX-License-Identifier: Apache-2.0
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+"""
+This script will provide you with a short example of how to perform clustering of weights (weight sharing) in
+TensorFlow using the TensorFlow Model Optimization Toolkit.
+
+The output from this example will be a TensorFlow Lite model file where weights in each layer have been 'clustered' into
+16 clusters during training - quantization has then been applied on top of this.
+
+By clustering the model we can improve compression of the model file. This can be essential for deploying certain
+models on systems with limited resources - such as embedded systems using an Arm Ethos NPU.
+
+After performing clustering we do post-training quantization to quantize the model and then generate a TensorFlow Lite file.
+
+If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+compiler for further optimizations before it can be used.
+
+For more information on using Vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
+For more information on clustering see: https://www.tensorflow.org/model_optimization/guide/clustering
+"""
+import pathlib
+
+import tensorflow as tf
+import tensorflow_model_optimization as tfmot
+
+from training_utils import get_data, create_model
+from post_training_quantization import post_training_quantize, evaluate_tflite_model
+
+
+def prepare_for_clustering(keras_model):
+    """Prepares a Keras model for clustering."""
+
+    # Choose the number of clusters to use and how to initialize them. Using more clusters will generally
+    # reduce accuracy so you will need to find the optimal number for your use-case.
+    number_of_clusters = 16
+    cluster_centroids_init = tfmot.clustering.keras.CentroidInitialization.LINEAR
+
+    # Apply the clustering wrapper to the whole model so weights in every layer will get clustered. You may find that
+    # to avoid too much accuracy loss only certain non-critical layers in your model should be clustered.
+    clustering_ready_model = tfmot.clustering.keras.cluster_weights(keras_model,
+                                                                    number_of_clusters=number_of_clusters,
+                                                                    cluster_centroids_init=cluster_centroids_init)
+
+    # We must recompile the model after making it ready for clustering.
+    clustering_ready_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+                                   loss=tf.keras.losses.sparse_categorical_crossentropy,
+                                   metrics=['accuracy'])
+
+    return clustering_ready_model
+
+
+def main():
+    x_train, y_train, x_test, y_test = get_data()
+    model = create_model()
+
+    # Compile and train the model first.
+    # In general it is easier to do clustering as a fine-tuning step after the model is fully trained.
+    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+                  loss=tf.keras.losses.sparse_categorical_crossentropy,
+                  metrics=['accuracy'])
+
+    model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True)
+
+    # Test the trained model accuracy.
+    test_loss, test_acc = model.evaluate(x_test, y_test)
+    print(f"Test accuracy before clustering: {test_acc:.3f}")
+
+    # Prepare the model for clustering.
+    clustered_model = prepare_for_clustering(model)
+
+    # Continue training the model but now with clustering applied.
+    clustered_model.fit(x=x_train, y=y_train, batch_size=128, epochs=1, verbose=1, shuffle=True)
+    test_loss, test_acc = clustered_model.evaluate(x_test, y_test)
+    print(f"Test accuracy after clustering: {test_acc:.3f}")
+
+    # Remove all variables that clustering only needed in the training phase.
+    model_for_export = tfmot.clustering.keras.strip_clustering(clustered_model)
+
+    # Apply post-training quantization on top of the clustering and save the resulting TensorFlow Lite model to file.
+    tflite_model = post_training_quantize(model_for_export, x_train)
+
+    tflite_models_dir = pathlib.Path('./conditioned_models/')
+    tflite_models_dir.mkdir(exist_ok=True, parents=True)
+
+    clustered_quant_model_save_path = tflite_models_dir / 'clustered_post_training_quant_model.tflite'
+    with open(clustered_quant_model_save_path, 'wb') as f:
+        f.write(tflite_model)
+
+    # Test the clustered quantized model accuracy. Save time by only testing a subset of the whole data.
+    num_test_samples = 1000
+    evaluate_tflite_model(clustered_quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples])
+
+
+if __name__ == "__main__":
+    main()