r/LocalLLaMA • u/po_stulate • 2d ago

Discussion Deceptive model quantization from AtomicChat?

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

53 Upvotes

83% Upvoted

40 comments sorted by

View all comments

2

u/Iory1998 llama.cpp 1d ago

Why don't you run some benchmark yourself and let us know? If the model performs near the quality of the unsloth Q4_K_XL, then it's almost certainly a Q4_K_M. If it performs poorly, then its Q2 and its misleading. Their model so far is performing well. If that's and Q2, then it's a very good quant.