For about half a year I stuck with using 7B models and got a strong 4 bit quantisation on them, because I had very bad experiences with an old qwen 0.5B model.

But recently I tried running a ~smaller~ ~model~ like llama3.2 3B with 8bit quant and qwen2.5-1.5B-coder on full 16bit floating point quants, and those performed super good aswell on my 6GB VRAM gpu (gtx1060).

So now I am wondering: Should I pull strong quants of big models, or low quants/raw 16bit fp versions of smaller models?

What are your experiences with strong quants? I saw a video by that technovangelist guy on youtube and he said that sometimes even 2bit quants can be perfectly fine.

UPDATE: Woah I just tried llama3.1 8B Q4 on ollama again, and what a WORLD of difference to a llama3.2 3B 16fp!

The difference is super massive. The 3B and 1B llama3.2 models seem to be mostly good at summarizing text and maybe generating some JSON based on previous input. But the bigger 3.1 8B model can actually be used in a chat environment! It has a good response length (about 3 lines per message) and it doesn't stretch out its answer. It seems like a really good model and I will now use it for more complex tasks.

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 1 points 3 weeks ago (1 children)

Is this VPTQ similar to that 1.58Q I've heard about? Where they quantized the Llama 8B down to just 1.5 Bits and it somehow still was rather comprehensive?

[–] brucethemoose 2 points 3 weeks ago

No, from what I've seen it falls off below 4bpw (just less slowly than other models) and makes ~2.25 bit quants somewhat usable instead of totally impractical, largely like AQLM.

You are thinking of bitnet, which (so far, though not after many tries) requires models to be trained from scratch that way to be effective.