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Jeff – Jev-compatible 0.8B decision models, trained at home, ~30 ms

575 points · 225 comments · firelex

  1. AgentMasterRace · · focus · HN ↗
    I compared it to Jev in my current use cases and it's very inaccurate. 70% vs 94% . for classification, it's unacceptable.
    1. tbeseda · · focus · HN ↗
      For _your_ classification it's unacceptable. The OP seems to have anticipated this and mentions you can fine tune it for your use case. Did you try that?

      I don't think the point is to displace Jev, but to show it's possible to build an MVP on open weights without years of work and millions of dollars.

      Why (presumably) an engineer would dismiss exploring a lightweight, custom alternative to locking into a fashionable PaaS, I'll never know.

      1. senko · · focus · HN ↗
        > The OP seems to have anticipated this and mentions you can fine tune it for your use case. Did you try that?

        You can already so that with classification models such as ModernBERT, at 0.4B.

        Jev's value is its zero shot performance without having to fine-tune.

        1. beepbooptheory · · focus · HN ↗
          I am sure I am missing something obvious here, but why is that valuable? Like, what kinds of projects are there where you need to classify stuff but are unable to make a bespoke model targeting the specific problem?
          1. senko · · focus · HN ↗
            You need to have enough high quality data to train with, knowledge how to do it, and developer time.

            In practice, that's enough of a barrier to not even try the approach on a number of cases where it might potentially be useful.

            I wouldn't be surprised if Jev turned out to be a "gateway drug" that validates approach on a use case, the team gathers experience and labeled data, and switches to an in house locally tuned model to minimize costs.

            1. davrosthedalek · · focus · HN ↗
              Exactly. And that labeled data could be collected by just recording what they feed jev and what the decision is.
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