Ok, I gave the 35B MoE weights a shot at Q6. It failed my go-to code reasoning benchmark (local models usually do), but delivered the most advanced output I've seen so far for my (non-agentic) coding execution benchmark. It was also full of bugs, though more in the form of coding mistakes than the deep logic bugs I usually see in local models. The code was also a bit haphazard, and some of those bugs could have been prevented entirely with a better code structure.
It was fairly good at diagnosing the bugs once informed of their symptoms. However, if I mischaracterized the symptoms, it would weigh my input too heavily and reject its own (correct) hunch about the root cause.
So it's an interesting one. There's definitely some latent capability in there that arguably exceeds Qwen 3.6, which is absolutely no small feat. But that capability seems to come in a somewhat erratic package.
It's probably worth benchmarking it unquantized if you can. I've grown to suspect that quantization damages small models more than perplexity and KL divergence accurately reflect.
It was fairly good at diagnosing the bugs once informed of their symptoms. However, if I mischaracterized the symptoms, it would weigh my input too heavily and reject its own (correct) hunch about the root cause.
So it's an interesting one. There's definitely some latent capability in there that arguably exceeds Qwen 3.6, which is absolutely no small feat. But that capability seems to come in a somewhat erratic package.
It's probably worth benchmarking it unquantized if you can. I've grown to suspect that quantization damages small models more than perplexity and KL divergence accurately reflect.
I'll also give the 9B weights a shot when I can.