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

618 points · 145 comments · Ardakilic

  1. alex7o · · focus · HN ↗
    Guys I have a real q, what is the difference between an instruct based re-ranker and laya/jev I just don't see it.

    Edit: One is that jev/laya are tuned to have better probabilities, but a reranker can be fine tuned to do that as well. And jev/laya use RLCD?

    1. Swizec · · focus · HN ↗
      > difference between an instruct based re-ranker and laya/jev I just don't see it

      Main difference is that laya/jev/et-al give you a zero-shot classifier that requires no training. You can prompt engineer your way to a quick fairly reliable cheap enough decision engine that you can use to iterate quickly (by prompt engineering).

      Right now a lot of people are doing this with LLMs and it's too slow and expensive.

      Imo the right iterative approach to productionizing these systems is something like:

          1. Build it with an LLM. Iterate on the prompt
          2. Start building a real-world dataset
          3. When the prompt works, turn it into a clear rubric for Jev or similar
          4. Keep iterating until desired accuracy achieved
          5. Use the real-world evals you've built to train a custom classifier fine-tuned to your needs
      
      You now have a system that has produced useful results in production from the very beginning and by the end it's a reliable super cheap classifier that can make thousands of decisions per second.
      1. rosegroove · · focus · HN ↗

        [dead]

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