[benjiro29]

Models are not like a fps counter where losing a few percentage has no actual impact. These percentages may be the difference between a model that writes code and one that goes into loops, and think rm -rf . is a good idea.

There is a reason why most models try to stay in the FP4 or higher range, because the reduced accuracy can have major consequences.

You are better off with a 8b FP4+ model then a 27b Q2 model.

[smeeth]

You're conflating two concepts: native bit width and post-training quantization.

Consider two models: one is 16B and trained natively in 2 bits; one is 8B and trained natively in FP4. These models have the same total number of weight bits, but one has twice as many parameters. There is no real reason the FP4 one should be better just because it's FP4. It might be, but that is an empirical question, not a general rule.

Post-training quantization is another thing entirely. Taking a model trained at higher precision and forcing it down to 2 bits is going to hurt performance, often very badly. But this was never my point.