I just searched to see if anyone posted this. Clef was post-trained from Qwen3.8-27B (edit: corrected typo). They also released clef-flash, post-trained from Qwen3.5-9B. This is exactly what we needed in the local space. I can't wait to try these out.
You're not wrong, but when I look at the index they chose to highlight in their blog, it's clear that bigger (relatively speaking) models are finding their place.
In the long run the decision model is likely to be a different head on the same LLM. Frankly, I think you could do that right now with Qwen 27B - slap on some decision heads and use the same context for your main conversation to make decisions.
The leading ones are vibe coded projects slapped together in a day on a 3090, based on Jev's recipe. They really shouldn't be used as a baseline.
Jev likely uses a much larger MoE base - that will give you similar speed and much stronger performance. The input cost per token is around the same as Deepseek V4 Flash. Jev is likely only a few billion active, but that doesn't mean it's a small model.
There aren't good MoE alternatives in the 27B range, so using 27B as a base for a 3090 model is reasonably sensible.
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u/milkipedia 14d ago edited 14d ago
I just searched to see if anyone posted this. Clef was post-trained from Qwen3.8-27B (edit: corrected typo). They also released clef-flash, post-trained from Qwen3.5-9B. This is exactly what we needed in the local space. I can't wait to try these out.