How to convert trained HuggingFace PaliGemma Model to TFLite?

TensorFlow General Discussion
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Hi there! Recently I managed to fine-tune and train the PaliGemma VLM released by Google and achieved great results.

I was wondering if anyone knows how to convert this PaliGemma model into Tensorflow Lite format so that it can be deployed to mobile devices efficiently offline? Can’t seem to find any working methods online so far.

Any help is GREATLY appreciated! Thank you!

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Hi,

To ensure you can smoothly convert and run efficiently w/ TF Lite, you will need to re-author the model using our Torch Generative API, some examples can be found here:

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To convert the PaliGemma VLM model to TensorFlow Lite format, follow these steps:

  1. Export to TensorFlow SavedModel: Ensure your PaliGemma model is in TensorFlow SavedModel format.

python

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model.save('path/to/saved_model')
  1. Install TensorFlow Lite Converter: If you haven’t already, install TensorFlow and TensorFlow Lite.

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pip install tensorflow
  1. Convert to TensorFlow Lite: Use the TensorFlow Lite Converter to convert the SavedModel to TFLite format.

python

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import tensorflow as tf

# Load the SavedModel
converter = tf.lite.TFLiteConverter.from_saved_model('path/to/saved_model')

# Set conversion parameters if needed
# converter.optimizations = [tf.lite.Optimize.DEFAULT]

# Convert the model
tflite_model = converter.convert()

# Save the TFLite model
with open('path/to/model.tflite', 'wb') as f:
    f.write(tflite_model)
  1. Test and Deploy: Test the TFLite model using TensorFlow Lite interpreter and deploy it on your mobile device.

For specific issues or advanced configurations, refer to TensorFlow Lite documentation and support forums.

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Hi, thanks for your input! Actually have tried that before, but I dont seem to see any examples for PaliGemma, only for Gemma. And faced a lot of errors in reauthoring the model itself. Was wondering if you have a working solution available / could guide me through in more detail? Thanks and appreciate it

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Hi thanks for your help! Have tried this but doesn’t seem like it’s working due to incompatible layers in the model architecture

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