BeRT, BART and FLAN-T5, for text classification. All of those models are ~half a decade old now, are based on the transformer model, and small enough to run locally. They're not perfectly SOTA, but perform quite well in embedded applications. Fine for low-stakes stuff, I think.
Image classifiers are a hugely competitive space. You've got BeiT, DeiT, MobileOne, ConvNext, FastViT and several more models that all support image classification pipelines.
Thanks for quick reply. I downloaded FLAN for comparison and you are quite right, gives similar results, with the benefit of privacy and self-sovereignty in serving the model.
So other than untestable claims of _how_ Jev generates responses, it's main feature right now is its API design?
You're allowed to be excited about new technology.
What are you allowed to feel when it's old technology?
Anything you want. Do as you please, stop waiting for the internet to tell you how to feel about things.
> what software was doing this prior to Jev
BeRT, BART and FLAN-T5, for text classification. All of those models are ~half a decade old now, are based on the transformer model, and small enough to run locally. They're not perfectly SOTA, but perform quite well in embedded applications. Fine for low-stakes stuff, I think.
Image classifiers are a hugely competitive space. You've got BeiT, DeiT, MobileOne, ConvNext, FastViT and several more models that all support image classification pipelines.
Thanks for quick reply. I downloaded FLAN for comparison and you are quite right, gives similar results, with the benefit of privacy and self-sovereignty in serving the model.
So other than untestable claims of _how_ Jev generates responses, it's main feature right now is its API design?