And when you consider that the usual final step in the pipeline is that a sampler goes ham on the probabilities and just picks some random nonsense, the toleran

And when you consider that the usual final step in the pipeline is that a sampler goes ham on the probabilities and just picks some random nonsense, the tolerance for lossy compression is fairly high. In fact, there's this funny occurrence where Q4 models on occasion perform better than their fp16 counterparts on benchmarks ran with top_k=1 since the outputs are slightly more random and they can less deterministically blunder past the local maximum into a more correct solution.