  In this paper cum technical report, we present PHUDGE A fine tuned Phi3 model
that achieved SOTA results in 4 tasks as Feedback Test, Feedback OOD, MT Human,
Preference Test surpassing each and every existing model in latency and
throughput. It shows very strong correlation not only with GPT4 but with Human
annotators too in unseen data as well as in both absolute and relative grading
tasks. We have not only addressed the usage of small LMs for cost effective
production grade systems but have also shown that Causal modelling is not only
slow in nature but sometimes it can hinder models learning capabilities and
should be replaced by simpler tasks whenever we can to make the overall system
faster and better. We show that by following systematic ML experimentation,
thoughtful data augmentation and re purposing the problem itself, we can even
beat 10x bigger models even with lesser training data. To the best of our
knowledge, we are re the first one to experiment and showcase the usage of
generalised version of Earth Movers Distance AKA Wasserstein distance by using
Minkowski Distance with a penalty to control loss smoothing and can be used as
a loss function instead of Cross Entropy to get stable training and better
results for grading tasks.
