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Thanks. How reliable is the world knowledge? Say, I use it as a delegation router for picking the best model for a task - how can I be confident it's doing that with enough intelligence?

When I use traditional LLMs I get some sense from the flagship-ness and regular usage. How do we get such confidence for Jev like models?

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The Jev alternative, laya is only 334m parameters and you can post train it as the workflow is open source. The downside is that laya is only 1024 tokens, and Jev is ~32,000 tokens. I think for many tasks 32k tokens might be too small. You could probably design a task complexity router with something like qwen 0.8b with similar speed and a much larger context window (260k token). As always, "it depends". 0.8B is almost as fast as Jev without any of the limitations.

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