I use TFLiteConvert post_training_quantize=True but my model is still too big for being hosted in Firebase ML Kit's Custom servers

I have written a TensorFlow / Keras Super-Resolution GAN. I've converted the resulting trained .h5 model to a .tflite model, using the below code, executed in Google Colab:

import tensorflow as tf
model = tf.keras.models.load_model('/content/drive/My Drive/srgan/output/srgan.h5')
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.post_training_quantize=True
tflite_model = converter.convert()
open("/content/drive/My Drive/srgan/output/converted_model_quantized.tflite", "wb").write(tflite_model)

As you can see I use converter.post_training_quantize=True which was censed to help to output a lighter .tflite model than the size of my original .h5 model, which is 159MB. The resulting .tflite model is still 159MB however.

It's so big that I can't upload it to Google Firebase Machine Learning Kit's servers in the Google Firebase Console.

How could I either:

• decrease the size of the current .tflite model which is 159MB (for example using a tool),

• or after having deleted the current .tflite model which is 159MB, convert the .h5 model to a lighter .tflite model (for example using a tool)?

Related questions

How to decrease size of .tflite which I converted from keras: no answer, but a comment telling to use converter.post_training_quantize=True. However, as I explained it, this solution doesn't seem to work in my case.

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