so i was finally able to figure this out. Posting my own answer in a hope that it might help someone else:
the model gained from the learning process apparently is not a standard tensorflow or keras model and first must be exported using the script called “exporter_main_v2.py”.
This is relatively selfexplainatory as it takes the arguments --trained_checkpoint_dir, --pipeline_config_path and --output_directory
when this step is done a folder is created at the output location which should be called “saved_model”
The following python script can be used to transform this into a TF-Lite model:
MODEL_PATH  = "your/abs/path/to/folder/saved_model"
MODEL_SAVE_PATH = "your/target/save/location"
converter = tf.lite.TFLiteConverter.from_saved_model(MODEL_PATH ,signature_keys=['serving_default'])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]

tflite_model = converter.convert()
with tf.io.gfile.GFile(MODEL_SAVE_PATH, 'wb') as f:
  f.write(tflite_model)

Hope this helps someone.