We focus on a promising 1–bit weight quantization approach for neural networks that optimizes the model and the weights at the same time during training. It has a low training overhead and is hassle–free, scalable, and automatable. We show that this method 2 can generalize to n–bits quantization, granted sufficient int–n support is available on the edge device. This algorithm is model–agnostic and can integrate into any training phase of the TinyMLOps workflow, for any application.