Deceptive model quantization from AtomicChat?

[u/po_stulate]

I kept seeing guys in this sub saying how AtomicChat's Qwen3.8-Flash-Next quant is so good, fits in their machine when unsloth's can't, runs faster than other quants etc, so I went check out what's happening there.

First thing I noticed was that AtomicChat's Q4_K_M quant is suspiciously small when the ngram table is removed (only ~56GB), it seems like most of the tensors in this quant are IQ2_S instead of the usual Q4_K, Q5_K and Q6_K that you usually find in Q4_K_M quants, the GGUF filetype metadata also says IQ2_S instead of Q4_K_M. In their model card, their Q4_K_M also has suspiciously high KLD (0.084).

It seems pretty obvious to me that they're pretending a IQ2_S quant as a Q4_K_M, but at the same time I'm genuinely not sure because it can't be only me who found this right? How can nobody be pointing this out? Am I missing something or what may they be doing?

Their HF repo ID: AtomicChat/Qwen3.8-Flash-Next-GGUF

[u/drFennec]

I was wondering the same thing, here is what llama.cpp prints on loading:

[34m0.00.803.003[0m [32mI [0mllama_model_loader: - type f32: 388 tensors [34m0.00.803.004[0m [32mI [0mllama_model_loader: - type f16: 1 tensors [34m0.00.803.005[0m [32mI [0mllama_model_loader: - type q5_1: 1 tensors [34m0.00.803.005[0m [32mI [0mllama_model_loader: - type q8_0: 666 tensors [34m0.00.803.006[0m [32mI [0mllama_model_loader: - type iq2_s: 24 tensors [34m0.00.803.007[0m [32mI [0mllama_model_loader: - type iq1_m: 72 tensors [34m0.00.803.007[0m [32mI [0mllama_model_loader: - type bf16: 24 tensors [34m0.00.803.008[0m [32mI [0mllama_model_loader: - type mxfp4: 48 tensors [34m0.00.803.027[0m [32mI [0mprint_info: file format = GGUF V3 (latest) [34m0.00.803.028[0m [32mI [0mprint_info: file type = IQ1_M - 1.75 bpw [34m0.00.803.033[0m [32mI [0mprint_info: file size = 79.09 GiB (3.84 BPW)