Mixture-of-Quantizations (MoQ) 4.0 BPW GGUF for Qwopus3.6-27B-Coder.
Canonical Predator-Q winner as of 2026-06-24 — verified on 6 benchmarks (HumanEval+ 164, MBPP+ 100, BigCodeBench 50, LCB-30 30). See "Multi-benchmark validation" section below for the full comparison vs. ASI-Evolved v2.
Summary
Metric
Value
File size
12.6 GB
Effective BPW
4.04
WikiText-2 PPL (Q)
6.18 (vs F16 base 6.02)
Mean PPL(Q)/PPL(base)
1.026
Mean KL divergence (vs F16)
0.034
Same top p (vs F16)
91.6%
RMS Δp
5.43%
HumanEval+ base pass@1
92.7% (152/164)
HumanEval+ plus pass@1
89.6% (147/164)
MBPP+ base pass@1
92.0% (92/100)
MBPP+ plus pass@1
79.0% (79/100)
BigCodeBench pass@1
42.0% (21/50)
LCB-30 pass@1
50.0% (15/30)
Multi-benchmark validation (2026-06-24, Phase 5)
This is the canonical winner. A more complex alternative (ASI-Evolved v2) was built and tested on the same 4 multi-task benchmarks. Result: no statistically significant difference on any benchmark (all McNemar p > 0.31, Bonferroni-corrected α = 0.0167). MoQ-4.0 is preferred because it is 2 GB smaller and requires zero LLM iteration cost to build.
Multi-benchmark comparison
Benchmark
MoQ-4.0 (12.4 GiB)
ASI-Evolved v2 (14.4 GiB)
Δ
McNemar p
HumanEval+ base (164)
152 (92.7%)
153 (93.3%)
+0.6pp
0.65
HumanEval+ plus (164)
147 (89.6%)
144 (87.8%)
-1.8pp
0.65
MBPP+ base (100)
92 (92.0%)
93 (93.0%)
+1.0pp
0.56
MBPP+ plus (100)
79 (79.0%)
78 (78.0%)
-1.0pp
0.56
BigCodeBench (50)
21 (42.0%)
22 (44.0%)
+2.0pp
0.32
LCB-30 (30)
15 (50.0%)
16 (53.3%)
+3.3pp
0.72
Summary table
The v2 model has a 16% lower KL-divergence against F16 (0.0287 vs 0.034) but does not produce measurably better outputs on any of the 6 code tasks tested. The 2 GB file size savings of MoQ-4.0 are pure upside.
Pareto frontier
Pareto frontier
Pareto size vs capability
MoQ-4.0 sits at the Pareto-optimal corner: smaller than all baseline quantization recipes, with capability equivalent to (or statistically tied with) the larger alternatives.
Comparison to Baselines
Build
File Size
BPW
KL-div
Same top p
PPL ratio
LCB-30
Q4_K_M
15.4 GB
4.69
0.019
93.6%
1.009
50.0%
4A BEST
16.98 GB
5.05
0.018
93.7%
1.012
50.0%
ASI-Evolved v2
14.4 GiB
4.51
0.0287
92.55%
1.020
53.3%
This (MoQ-4.0)
12.6 GB
4.04
0.034
91.6%
1.026
50.0%
Net result: Same HumanEval+/MBPP+/LCB-30 capability as Q4_K_M and 4A BEST, with 18% smaller file size than Q4_K_M and 26% smaller than 4A BEST. ASI-Evolved v2 has a marginal KL-div win that did not translate to task performance.
What is MoQ?
MoQ (Mixture of Quantizations) is a search-based mixed-precision quantization method by Waleed Ahmad. It estimates per-tensor "elasticity" — sensitivity to low precision — then assigns each tensor a different Q-type to minimize quality loss per bit spent. This produces non-uniform recipes like:
ffn_down → IQ3_S (aggressive — most redundant tensor)
The recipe was applied with importance matrix (imatrix) calibration, which makes IQ-quant decisions properly importance-aware. Without imatrix, the same recipe produces worse KL-div (0.059 vs 0.034).
Recipe Source
The per-tensor recipe used here is MoQ_qwen3.6-27b_tensors_4.0.txt from kaitchup/Qwen3.6-27B-GGUF-MoQ. Since Qwopus3.6-27B-Coder uses the same Qwen3.5 architecture (qwen35, 64 layers, 851 tensors), the recipe transfers directly without modification.
Statistical test: McNemar's test (paired, binary outcomes), Bonferroni correction across 6 benchmarks
Base logits: F16 GGUF reference logits saved once and reused for all variant evaluations
Known Limitations
Code-specific tasks: Tested on 6 coding benchmarks. Performance on math/reasoning/long-form generation not measured.
Long context: Not stress-tested beyond LCB-30's 4096-token context. Qwopus's full 262K context window requires the 4D-RoPE / Mamba-SSM hybrid architecture to work correctly — preserved here since all norms/embeddings are at full precision.
KL-div is higher than Q4_K_M (0.034 vs 0.019). This is the cost of smaller file size with mixed-precision quantization. Same top token agreement remains high (91.6%).
