Running Local LLMs with Ollama on openSUSE Tumbleweed
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Try any of Latitude's series. They're 'uninhibited' dungeonmaster models, but they should be smart enough (and retain enough of that personality) for some flexibility:
https://huggingface.co/LatitudeGames
Perhaps more optimally for your hardware, try this:
https://huggingface.co/Gryphe/Pantheon-Proto-RP-1.8-30B-A3B
It's trained from Qwen A3B base, not instruct. Base models usually don't have the severe ChatGPT-isms you describe, hence while I haven't personally tried this model, it seems promising. And it should be fast on your Xeon.
The 30B-A3Bs I've tried have been suuuuuuuper repetitive. Do you have any specific settings to recommend to get them to work well?
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Kobold.cpp is fantastic. Sometimes there are more optimal ways to squeeze models into VRAM (depends on the model/hardware), but TBH I have no complaints.
I would recommend croco.cpp, a drop-in fork: https://github.com/Nexesenex/croco.cpp
It has support for more the advanced quantization schemes of ik_llama.cpp. Specifically, you can get really fast performance offloading MoEs, and you can also use much higher quality quantizations, with even ~3.2bpw being relatively low loss. You'd have to make the quants yourself, but it's quite doable... just poorly documented, heh.
The other warning I'd have is that some of it's default sampling presets are fdfunky, if only because they're from the old days of Pygmalion 6B and Llama 1/2. Newer models like much, much lower temperature and rep penalty.
Kobold is great. Never heard of Croco. I'll have to look into it.
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Kobold is great. Never heard of Croco. I'll have to look into it.
It's great, albiet not super useful unless you make your own quantizations (or find the few K-quant/trellis quant GGUFs hidden on huggingface).
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The 30B-A3Bs I've tried have been suuuuuuuper repetitive. Do you have any specific settings to recommend to get them to work well?
wrote last edited by [email protected]Random thing, I did not get a notification for this comment, I stumbled upon it. This happens all the time, and it makes me wonder how many replies I miss...
I don't run A3B specifically, but for Qwen3 32B Instruct I put something like "vary your prose; avoid repetitive vocabulary and sentence structure" in the system prompt, run at least 0.5 DRY, and maybe some dynamic sampler like mirostat if supported. Too much regular rep penalty makes it dumb, unfortunately.
But I have much better luck with base model derived models. Look up the finetunes you tried, and see if they were trained from A3B instruct or base. Qwen3 Instruct is pretty overtuned.
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Random thing, I did not get a notification for this comment, I stumbled upon it. This happens all the time, and it makes me wonder how many replies I miss...
I don't run A3B specifically, but for Qwen3 32B Instruct I put something like "vary your prose; avoid repetitive vocabulary and sentence structure" in the system prompt, run at least 0.5 DRY, and maybe some dynamic sampler like mirostat if supported. Too much regular rep penalty makes it dumb, unfortunately.
But I have much better luck with base model derived models. Look up the finetunes you tried, and see if they were trained from A3B instruct or base. Qwen3 Instruct is pretty overtuned.
They may have been based on Instruct. It left such a bad impression, I didn't play around with them much. Good to know for the future, though. I haven't used DRY or mirostat really in the past, but I'll try them next time I look at the Qwen3s.
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They may have been based on Instruct. It left such a bad impression, I didn't play around with them much. Good to know for the future, though. I haven't used DRY or mirostat really in the past, but I'll try them next time I look at the Qwen3s.
wrote last edited by [email protected]Honestly I don’t use Qwen3 instruct unless it’s for code or “logic.” Even the 32B is soo dry and focused on that, and countering it with sampling dumbs it down.
Not sure if it’s too big, but I have been super impressed with Jamba 52B. It knows tons of fiction trivia and writing styles for such a “small” model, though I haven’t tried to manipulate its prompt for writing yet. And it’s an MoE model like A3B.
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Honestly I don’t use Qwen3 instruct unless it’s for code or “logic.” Even the 32B is soo dry and focused on that, and countering it with sampling dumbs it down.
Not sure if it’s too big, but I have been super impressed with Jamba 52B. It knows tons of fiction trivia and writing styles for such a “small” model, though I haven’t tried to manipulate its prompt for writing yet. And it’s an MoE model like A3B.
Interesting. I hadn't heard of this one before, but the design sounds innovative. The biggest I run is 35B or 7x8B, but I'll have to try and check it out.
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Interesting. I hadn't heard of this one before, but the design sounds innovative. The biggest I run is 35B or 7x8B, but I'll have to try and check it out.
wrote last edited by [email protected]Jamba is a killer model flying under the radar, though it does have a quirk I more recently discovered: no prompt caching in llama.cpp (yet).
If you have a 24GB GPU you can cram Nemotron 49B in it with no offloading, including the new reasoning version. It’s a monster at STEM stuff, and I can upload my special quantization (3bpw, with 4bpw KV heads, exllamav3) if you ask.
Qwen 30B coder is ridiculously fast for how smart it is at coding, just came out today…
TBH the last week or two has been nuts with new releases.
But FYI if you are looking for pure prose quality, I still use EVA Gutenberg 32B (based on Qwen 2.5 base) and Jonboro's brand new QWQ 32B fine tune, as new models have not surpassed them IMO. But for creative writing, I tend to write novel style instead of multi turn, so YMMV.
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Jamba is a killer model flying under the radar, though it does have a quirk I more recently discovered: no prompt caching in llama.cpp (yet).
If you have a 24GB GPU you can cram Nemotron 49B in it with no offloading, including the new reasoning version. It’s a monster at STEM stuff, and I can upload my special quantization (3bpw, with 4bpw KV heads, exllamav3) if you ask.
Qwen 30B coder is ridiculously fast for how smart it is at coding, just came out today…
TBH the last week or two has been nuts with new releases.
But FYI if you are looking for pure prose quality, I still use EVA Gutenberg 32B (based on Qwen 2.5 base) and Jonboro's brand new QWQ 32B fine tune, as new models have not surpassed them IMO. But for creative writing, I tend to write novel style instead of multi turn, so YMMV.
I only have a 16GB card, and my CPU is new enough that it's better to offload some layers of all but 7-8B models, so I haven't tried exllama, but you're making me think I should, if only for comparison.
I like Qwen 2.5 based models in the 14B size range, but I don't think I tried the bigger ones. I tried the QWQ and didn't really like it, but I haven't seen this new one. You've given me a whole list of things to try, so thanks.
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I only have a 16GB card, and my CPU is new enough that it's better to offload some layers of all but 7-8B models, so I haven't tried exllama, but you're making me think I should, if only for comparison.
I like Qwen 2.5 based models in the 14B size range, but I don't think I tried the bigger ones. I tried the QWQ and didn't really like it, but I haven't seen this new one. You've given me a whole list of things to try, so thanks.
wrote last edited by [email protected]16GB
Is it 3000 series or newer?
If so, with exllamav3, you can squeeze 32Bs in that 16GB card with relatively little loss. For instance: https://huggingface.co/turboderp/EXAONE-4.0-32B-exl3/tree/3.0bpw
The 3bpw weights are 13 GB, say another 1.5GB for some q5_q4 context, and you are looking at 14.5GB-15GB or so. It will be tight, but it will be leagues smarter than 14Bs.
24B Mistral models will fit much more easily. No need to CPU offload those on a 16GB card, you just need to be careful with your settings.
