I'm trying to parse it down to what we had before vs what is new here.
We already had encoder models that skipped text generation for giving us a numerical output that could be computed as a probability. we also got no hallucinations and faster inference for free there. So we already had
1. "unstructured state in, probabilistic decisions out"
2. "orders of magnitude faster and more efficient"
What was hard there was to train the model head without ML expertise, and considerable amount of data.
This seems like this is a democratization of those encoders? The addition over existing encoders seems to be coming from being able to specify the output shape (up to a cardinality of 255). It is unclear to me if this is possible using Jev without additional labels for fine-tuning.
If so, that is still very impressive, but I think the faster inference and 0 hallucinations might come for free, from it not being generative.
bregmandiv · · focus · HN ↗
We already had encoder models that skipped text generation for giving us a numerical output that could be computed as a probability. we also got no hallucinations and faster inference for free there. So we already had
1. "unstructured state in, probabilistic decisions out" 2. "orders of magnitude faster and more efficient"
What was hard there was to train the model head without ML expertise, and considerable amount of data.
This seems like this is a democratization of those encoders? The addition over existing encoders seems to be coming from being able to specify the output shape (up to a cardinality of 255). It is unclear to me if this is possible using Jev without additional labels for fine-tuning.
If so, that is still very impressive, but I think the faster inference and 0 hallucinations might come for free, from it not being generative.
nijave · · focus · HN ↗
Which has become a lot easier the big, expensive general purpose LLMs
Some of the use cases seem interesting but a lot of them also seem like the "old" stuff can do it faster/cheaper