this post was submitted on 23 Jul 2024
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At long context (close to the full 128K), Nemo is way better than llama 8B in my testing.
Turns out they are both very sensitive to quantization though.
TBH I didn't know people here were running LLMs. Seems like most of Lemmy is very broadly anti AI?
Yeah, there's a massive negative circlejerk going on, but mostly with parroted arguments. Being able to locally run a model with this kind of context is huge. Can't wait for the finetunes that will result from this (*cough* NeverSleep's *-maid models come to mind).
I am looking into doing it on the 12B for myself, not so much for RP but novel style prose.
I am thinking literature + a fanfic dump as a dataset?
Ah, that's a wonderful use case. One of my favourite models has a storytelling lora applied to it, maybe that would be useful to you too?
At any rate, if you'd end up publishing your model, I'd love to hear about it.
[Oh, my friend, you have to switch to this: https://huggingface.co/BeaverAI/mistral-doryV2-12b
It's so much smarter than llama 13B. And it goes all the way out to 128K!
Oof - not on my 12gb 3060 it doesn't :/ Even at 48k context and the Q4_K quantization, it's ollama its doing a lot of offloading to the cpu. What kind of hardware are you running it on?
A 3090.
But it should be fine on a 3060, with zero offloading.
Dump ollama for long context. Grab a 5-6bpw exl2 quantization and load it with Q4 or Q6 cache depending on how much context you want. I personally use EXUI, but text-gen-webui and tabbyapi (with some other frontend) will also load them.