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?

Has anyone actually seen better or the same results with Laya compared to Jev? From my experience, Laya performs significantly worse. It's less confident and often makes wrong decisions with more complex queries.

>Run decision models locally.

>example is a text classification task instead of a decision

This inference engine is soooo much faster btw: https://github.com/tamnd/kime

Does anyone know what laya multi lang is faster than laya en? I would have thought focusing on a single language would be faster.

It would be really cool to have LLMs and System One in a single tool - in this case, if Ollama implemented it.

Sounds good on latency but how is its actual decision quality vs. Jev?

Are there many models that are comparable to Jev for generic decision making?

Smarter move if you have an eval set is to just train a classifier and call it a day.

Cool... but this does seem undermined by the fact that Ollama can add support for decision models at any time.

I am fairly confident if Jev-style decision models are seen as prominent (which, they seem to be), Ollama will support them. Surprised the team hasn't implemented this already.

great project for empowering open-source alternatives.

I have also tried this and its really awesome

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Hey Claude, make ollama for Jev like models. Make no mistakes /s