I am hearing about Jev for the first time here so no idea about the hype.
So their(Jev) is that the thing is faster at classification than a frontier model? Because the whole type safe aspect is already fully solvable with structured output.
But their example is classification but that would also be possible and faster with a classic BERT model.
So their pitch is a task specific smaller model or am I completely misunderstanding the whole thing?
I am in no way trying to sell Jev here as some panacea of the modern world; I'm only responding to your questions:
> But their example is classification but that would also be possible and faster with a classic BERT model.
With BERT, you need a large, labeled dataset, and you have to train/fine-tune the model. Jev is pitched as a zero- or 'few-shot' model. You define the schema in code, give it instructions, and it works without a traditional training pipeline.
> So their pitch is a task specific smaller model or am I completely misunderstanding the whole thing?
Yup; that about sums it up: it is more or less an optimized, task-specific small model with the flexible understanding of a traditional LLM.
> With BERT, you need a large, labeled dataset, and you have to train/fine-tune the model.
BERT requires a huge corpus, but it isn't labeled. BERT is trained through self-supervised learning using mask tokens and next sentence prediction. Fine-tuning is useful for specific tasks, but isn't absolutely essential for the model to function.
bruhhhhhh · · focus · HN ↗
garciasn · · focus · HN ↗
> But their example is classification but that would also be possible and faster with a classic BERT model.
With BERT, you need a large, labeled dataset, and you have to train/fine-tune the model. Jev is pitched as a zero- or 'few-shot' model. You define the schema in code, give it instructions, and it works without a traditional training pipeline.
> So their pitch is a task specific smaller model or am I completely misunderstanding the whole thing?
Yup; that about sums it up: it is more or less an optimized, task-specific small model with the flexible understanding of a traditional LLM.
elgertam · · focus · HN ↗
BERT requires a huge corpus, but it isn't labeled. BERT is trained through self-supervised learning using mask tokens and next sentence prediction. Fine-tuning is useful for specific tasks, but isn't absolutely essential for the model to function.