Why does the GPT-2 conversion to TensorFlow Lite fail and how can I troubleshoot the issue

Question

Can you tell me Why does the GPT-2 conversion to TensorFlow Lite fail, and how can I troubleshoot the issue?

Answer 1

GPT-2 conversion to TensorFlow Lite (TFLite) can fail due to unsupported operations, model size limitations, or conversion errors.

Here is the code snippet you can refer to:

In the above code we are using the following points:

• Loads GPT-2 using Hugging Face Transformers: Converts the model to TensorFlow format first.
• Uses TensorFlow Lite Converter: Applies optimizations for smaller model size.
• Handles Unsupported Operations: Uses tf.lite.OpsSet.SELECT_TF_OPS to allow TensorFlow ops in TFLite.
• Reduces Model Size: Enables tf.lite.Optimize.DEFAULT to optimize for mobile/embedded deployment.

Hence, GPT-2 TFLite conversion fails mainly due to unsupported operations and size constraints, which can be resolved by using TensorFlow ops support, applying optimizations, and reducing model complexity.