Should I accept a model with good validation loss & accuracy but bad training one?
The validation set having just 12 samples is too small to consider deploying a model which performs well on the validation set.
A model that performs well on the training set with 110 samples and then performs better on the small validation set would be preferable.
Since your set is too small, all though it is not prudent, you should consider running the model on the entire 120 samples to see how it performs on the entire dataset.
I presume you can't get more samples, if you can, that would be a good step.