HvskyAI

I’m late to the party on this one.

I’ve been loving the 2.4BPW EXL2 quants from Lone Striker recently, specifically using Euryale 1.3 70B and LZLV 70B.

Even at the smaller quant, they’re very capable, and leagues ahead of smaller models in terms of comprehension and reasoning. Min-P sampling parameters have been a big step forward, as well.

The only downside I can see is the limitation to context length on a single 24GB VRAM card. Perhaps further testing of Nous-Capyabara 34B at 4.65BPW on EXL2 is in order.

Agreed - I’m personally using 70B models at 2.4BPW EXL2 quants, as well. They hold up great even at a small quantization as long as sampling parameters are set correctly, and the models are subjectively more pleasant in prose (Euryale 1.3 and LZLV both come to mind).

At 2.4BPW, they fit into 24GB of VRAM and inference is extremely fast, and EXL2 also appears to be very promising as a quantization method. I believe the potential upsides are yet to be fully leveraged.

I am having better luck with 2.4BPW EXL2 quants of 70B models from Lone_Striker lately - Euryale 1.3, LZLV, etc.

Even at the smaller quants, they are quite strong at the correct settings. Easily comparable to a 34B at Q4_K_M, from my experience.

I see, the model does tend to run a bit hot as-is. I’ll go ahead and try these settings out tomorrow.

Yes, the BOS token is disabled in my parameters