[fcpk]

something I have been wondering about is doing regressive layer specific quantization based on large test sets. ie reduce very specifically layers that don't improve general quality.

[buildbot]

This is a thing! For example, https://arxiv.org/abs/2511.06516

[fcpk]

that's brilliant, I wonder why we haven't seen much use of it to do very heavy quantization

[woadwarrior01]

This is a very well established idea. It's called dynamic quantization. Vary the quantization bit-width (or skip quantization altogether) on a layer by layer basis, using a calibration dataset.

EvoPress is the first time that comes to my mind, when I think of dynamic quantization.

https://arxiv.org/abs/2410.14649

[qskousen]

I've experimented with this with diffusion models with a safetensors - gguf tool I wrote. even with relatively few sample images (~10k, still enough to keep my 3090 spinning for days straight) the benefits are quite noticeable - a smaller file with overall better results.