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

618 points · 145 comments · Ardakilic

  1. ranyume · · focus · HN ↗
    >Run decision models locally.

    >example is a text classification task instead of a decision

    1. OgAstorga · · focus · HN ↗
      text classification is equivalente to decision. This is exactly the same thing Jev does.
      1. abirch · · focus · HN ↗
        Jev does it more efficiently because it doesn&#x27;t use an LLM <a href="https:&#x2F;&#x2F;typesafe.ai&#x2F;blog&#x2F;introducing-system-one-models-and-jev" rel="nofollow">https:&#x2F;&#x2F;typesafe.ai&#x2F;blog&#x2F;introducing-system-one-models-and-j...
        1. rockinghigh · · focus · HN ↗
          Their marketing language is misleading. They must still use some transformer language model backbone to encode the text input (BERT or decoder-only LLM). The biggest difference is the output, instead of auto-regressively generating tokens, they produce probabilities over a bounded set of decisions (more flexible classification).
          1. [deleted] · · focus · HN ↗

            [deleted]

      2. ricardobeat · · focus · HN ↗
        It is not. In a benchmark with actual decisions - navigation, traffic, waypoints - laya does only slightly better than a small classifier.
        1. cobanov · · focus · HN ↗

          [dead]

      3. ranyume · · focus · HN ↗
        If it has four legs, a tail and barks why not call it a dog?
        1. gchamonlive · · focus · HN ↗
          Because this specific dog only barks in structured text
        2. seemaze · · focus · HN ↗
          This dog only barks when given biscuits
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