It's not novel research, but I think it drives home the point that many narrow applications of AI do not require the largest, latest (and most expensive) models. And in many of those cases, a small fine-tuned model is the most performant and cost-effective.
It is probably obvious to most who follow the space closely, but you'd be surprised how many engineers don't recognize this.
It is probably obvious to most who follow the space closely, but you'd be surprised how many engineers don't recognize this.