[D] Could the 1.58 bit paper be implemented into a simple feed forward network and be trained on the MNSIT digits dataset?

From what I understand the paper "The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits", talks about doing inference and training on -1, 0 and 1 as possible values for weights.

Could this be applied to a simple feed forward networks and train it on the MNIST dataset to test and compare performance? Also as a simple example that can be used as a learning experience.

Or are some LLM parts necessary for this to work, …

dataset digits inference language language models large language large language models llms machinelearning network paper simple talks training values