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

575 points · 225 comments · firelex

AgentMasterRace · 1790631907 · 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 · 1790633130 · 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. baobabKoodaa · 1790671696 · focus · HN ↗
    You don't seem to understand what the point of Jev is when you say "you can fine tune it for your use case". Building your own classifiers for your specific business problems is the type of work we all used to do back in 2016 or so. It costs very much. Jev is a cheap and fast general purpose classifier.

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