This partly answer to question 1.
There is no general rule concerning accuracy or size of the model. It depends on the training data and the processed data.
The lightest is your model compared to the full accuracy model the less accurate it will be. I would run the lite model on test data and compare to the accuracy of the full model to get an exact measure of the difference.
Tensor flow has different options to save the "lite" model (optimized in size, latency, none and default).
The following mostly answer question 2.
Tensor flow lite is intented to provide the ability to use the model to on line predict only and load the model not to train the model.
On the other hand Tensor flow is used to build (train) the model off line.