Full Int8 QAT not working

Just a quick question. I want my final model to be full int8 instead of float32 for input and outputs. I want the training to be as accurate as possible. Do I train with quantised input and outputs? Because I have followed the common procedure in the comprehensive guide (with my custom model) and it hasn't worked.
So

1. I trained using the comprehensive guide but modified it to my model
2. After training I use these settings to quantise my model

converter.experimental_new_converter = True
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8

3. When I go to evaluate the model it is completely inaccurate

What do I need to do to allow for full int8 to work?

All help welcome

Start with the TensorFlow comprehensive guide and the custom model's conversion and evaluation path, checking the converter settings and representative_dataset shown in the report. Done means identifying why full-int8 conversion produces inaccurate evaluation and documenting or reproducing the required training or conversion change.