I was playing with Deepseek-Lite and noticed that

• Quantization mixes are inadequate, so added a few quick changes to that
• As some of the tensors row sizes are not divisible by 256, we get quite a few tensors quantized with IQ4_NL, so I noticed that after repacking to IQ4_NL_R4 it does not work for row sizes that are not a multiple of 128 (4 blocks). So, I fixed that (AVX2 and Zen4)
• Once at it, also fixed Q5_0_R4 and Q6_0_R4

Quantization error as measured by PPL is surprisingly low for the low-bit quants, even IQ1_S is kind of semi-usable. It is not a "true" IQ1_S quantization as quite a few tensors get quantized to IQ4_NL, and I changed the attention tensors, which represent a tiny fraction of the overall model sizes, to be quantized with much higher bpw. We end up using 2.525 bpw for the repeating layers, and PPL(IQ1_S)/PPL(fp16) - 1 = 49.4%. But I now understand the hype around the Internet when the other day somebody was pretending to have invented 1-bit quantization and quantization mixes by using IQ1_S in llama.cpp for Deepseek-R1.