Thread

Ollaya – Ollama for open-source, Jev-style decision models

618 points · 145 comments · Ardakilic

solaire_oa · 1790372935 · focus · HN ↗
I installed it, I tried the examples, it works.... But forgive my lack of imagination... what is this useful for?
Like, their example is of classification for a support interface.... `refund_requested`. Pretty convenient bool given the example is about a refund- what if 99% of submissions don't ask about a refund? Also, is that user not a `churn_risk`? What could possibly qualify as a churn risk if not a user asking for a refund?
<a href="https://ollaya.dev/library/laya" rel="nofollow">https://ollaya.dev/library/laya The examples suffer the same problem of why I'd prefer to use a string column vs an enum. Changing an enum means you need to update the db, using a string you can do whatever.
I'm not trying to be negative, I genuinely want to know about some practical examples (that don't require tons of backwards maintenance).
1. devttyeu · 1790374164 · focus · HN ↗
  I have a lot of semi-practical examples of how you can use this model wrapped in unix-ish tools - <a href="https://github.com/aurorainfra/grev" rel="nofollow">https://github.com/aurorainfra/grev (readme links to docs of each tool with some more or less practical examples)
  Really I think "smart grep" is a pretty good one ('look for an error looking vaguely like this'). Also I think sql-based shell history + decision model is quite good to make the last 'which one of those choices is best fit given users past few commands' etc.
  1. spaniard89277 · 1790374354 · focus · HN ↗
    Isn't it better to use an LLM to train modernbert or xgboost et al?
    motoboi · 1790421224 · focus · HN ↗
    Laya, the open source model, it’s actually modernBERT based.

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