Yeah it’s basically impossible to keep up with new releases, heh.

Anyway, Gemma 12B is really popular now, and TBH much smarter than Nemo. You can grab a special “QAT” Q4_0 from Google (that works in kobold.cpp, but fits much more context with base llama.cpp) with basically the same performance as unquantized, would highly recommend that.

I'd also highly recommend trying 24B when you get the rig! It’s so much better than Nemo, even more than the size would suggest, so it should still win out even if you have to go down to 2.9 bpw, I’d wager.

Qwen3 30B A3B is also popular now, and would work on your 3770 and kobold.cpp with no changes (though there are speed gains to be had with the right framework, namely ik_llama.cpp)

One other random thing, some of kobold.cpps sampling presets are very funky with new models. I’d recommend resetting everything to off, then start with like 0.4 temp, 0.04 MinP, 0.02/1024 rep penalty and 0.4 DRY, not the crazy high temp sampling they normally use, with newer models than llama2.

I can host specific model/quantization on the kobold.cpp API to try if you want, to save tweaking time. Just ask (or PM me, as replies sometimes don’t send notifications).

Good luck with exams! No worries about response times, /c/localllama is a slow, relaxed community.