Jev uses a different training architecture called RLCF (Reinforcement Learning from Calibrated Decisions) vs the traditional RLHF that most TF models use.
So at the end of the day the groundbreaking work wasn't the model itself inherently but the way it was trained and then the way the harness interacts with it.
So this demo here is showing the harness side of things afaict but then TypeSafe's Jev takes it a step further via a specific training regimine.
One of the biggest issues with LLMs is that they don't work well as a classifier. They tend to pick up on the patterns of the examples and not the intent of the examples (gets worse the more examples/intents).
Does Jev solve this?
who cares how it was trained.