Qwen models are always good. The 35B A3 model is a MoE model which means it has higher performance in RAM constrained environments compared to the 27B dense model (which is better at coding).
I don't have experience to rate it's Hebrew or Greek performance but apparently it's not bad.
For smaller ones like my native Latvian, the output could be confused for good translation from across the room, the words do look like Latvian words. But the quality is Google translate circa 20 years ago, tops.
It could probably do a decent enough translation to English, if all you need is to get the gist of text. But for smaller European language outputs, nothing comes close to Gemini.
I'm not doing translations, rather querying Hebrew text with a Hebrew prompt.
(not recommendation, I've not used it .. yet)
Which is unsurprising in the AI space.
You get a wall of text showing you various random fine-tuned models by random people, and that is basically it.
Actual sane default requirements like "just give me the normal AI labs", "please filter for dense only" and "I want this exact context size at this quant" are not part of the tool, apparently. Neither is "compare these quants for me for the same model".
Or maybe it's just hidden enough that I did not find them before I've stopped caring.
Conway's law is at it again.
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Edit:
I have since then had qwen3.6 ponder the codebase and think about my complaints.
Seems to require a major data model overhaul to actually fix those, so they're legit. Which I didn't doubt, but nice to have some extra fabricated confirmation after it initially refused and said "nooooo the readme says otherwise nooo hypfer is just a hater noo"
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Edit 2:
It gets worse the longer I stare at it. This could've been a web calculator.
(Honestly I think Apple's "AI push" could do worse than just focus on a curated model library, a couple of Apple-standard Gemini distillations, an OS-level model manager and some sort of tweak of their containers system to do what Docker's sbx does. They could demystify a lot of this shit.)