Comment by npn
9 hours ago
Show me a single example how is this jev thing better than a modern Bert solution?
Or even llm if you claim about versatility. You can easily modify the llm inference code to make it predict a single token represent the classification choice and extract the probability that way.
Sure jev will still be faster, but a local deployed Bert model is way faster than both.
And to get the most out of it you still need to fine tune the models anyway, unless your classification task is just one of those mainstream ones.
I did a GRPO run (multiple now actually) with a per sample rubric that leans heavily on subjective preference judgements that BERT wouldn't have the learning capacity for: not to metion you'd need to finetune hundreds of instances and host them somewhere.
And even if BERT wasn't woefully underintelligent for the task... have 100+ instances of BERT running locally faster than Jev API response times? Sweet rig you must have...
LLMs would not be fast enough without constrained decoding tricks that people fundamentally don't seem to understand make the models much dumber, and sure wouldn't be cheaper or faster.
Again I feel this deep discomfort because presumably you're somewhat intelligent but your opening salvo made it hard not to scream DO YOU EVEN HAVE A SINGLE CLUE WHAT IT DOES instead of giving you my actual answer... yet you're speaking from the chest! If I didn't try it for myself I would have been 100% sucked into you and this ocean of clueless negativity.
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I apologize if that sounds harsh but it angers me because why should I have to deal with this kind of noise in an already insanely noisy environment? What do you gain from being cluelessly pessimistic?
And dwelling a but more I think it breaks one of my most used filters which was assuming people who know the "old world" of AI/ML are better at judging the "new world" full of hype and noise. Maybe my frustration is also just fear that things moved so quickly that the "old world" is becoming increasingly irrelevant. That'd be really disappointing.
> And even if BERT wasn't woefully underintelligent for the task... have 100+ instances of BERT running locally faster than Jev API response times? Sweet rig you must have...
why the heck do you need 100+ instances of bert. do you even attempt to research about this before?
the laya paper show that you can do the similar stuff with jev using modern bert only: https://laya.convaiinnovations.com/
and even without the newer wave of applying llm techniques to the older bert models, even flan-t5 was trained for handling 1800+ tasks.
Finetune and infer: One instance ModernBERT didn't have the learning capacity for a single problem in the shape of my subjective preference task with finetuning, do you not have the basic research taste to realize no conceivable post-training recipe will result in an instance that can zero-shot hundred plus similar questions that vary with each sample?!
You really need to try that to find out?
And again have you actually tried Jev? It has a ton of world knowledge: it's able to infer user personas based on TV show watch histories using fairly recent titles... where the hell do you think that capability is emerging in 395M params?
The irony is if you really want to die on this hill, there are much better angles by focusing on LLMs that've had diffusion heads attached for fast inference with as much of a constrained decoding intelligence penalty: at least that'd put you in the ballpark.
I was being charitable that you know the field and are clueless about Jev, mea culpa for giving you the space to think I'm the one that's missing something.