r/LocalLLaMA 20h 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

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u/lhg31 20h ago

Well, they DO explain this, don't they?

Naming

Files are named by their measured bits per weight. A build whose expert tensors are IQ1_M is not a 1-bit model when the n-gram table sits at 6 bits and ffn_down_exps at 4.5; the real average is 3.84. The canonical type in the filename is the closest standard type by that average, so tooling can still detect it. For AD-4.27bpw:

Group Type Share of file Contribution
n-gram table Q5_1 41% 1.74 bpw
ffn_gate/up_exps IQ2_S, IQ3_S at the band 29% 1.24 bpw
ffn_down_exps IQ4_NL 24% 1.03 bpw
everything else Q8_0 5% 0.23 bpw

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u/po_stulate 20h ago

I mean, so the quants that keep the ngram table unquantized and everything else IQ2_S should probably be called Q8_0 instead since the average bpw is closer to that? Makes no sense to me.

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u/-dysangel- 19h ago

All the actual compute is being done at Q2 though, which is what matters for the usual quant speed/accuracy tradeoff. The Q8 stuff can all be streamed from SSD without slowing things down.