Qwopus3.6-27B-Coder-GGUF-Predator-Q (MoQ-4.0)

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.

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

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

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)
ffn_gate → IQ3_S (aggressive)
ffn_up → IQ4_XS (slightly more conservative)
attn_qkv (most layers) → IQ4_XS
attn_qkv (special every-4th full-attention layers) → split: attn_q=IQ4_NL, attn_k=bf16, attn_v=bf16, attn_output=Q5_K
token_embd → Q4_K
Norms → bf16

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.

Build Details

# 1. Generate imatrix (WikiText-2 test set, 100 chunks)
llama-imatrix -m qwopus_f16.gguf -f wikitext-2-raw/wiki.test.raw \
  -o qwopus_imatrix.gguf -ngl 50 --chunks 100

# 2. Quantize with MoQ recipe + imatrix
llama-quantize --imatrix qwopus_imatrix.gguf \
  --tensor-type-file MoQ_qwen3.6-27b_tensors_4.0.txt \
  qwopus_f16.gguf qwopus_moq_4.0.gguf Q4_K

Evaluation Details

WikiText-2 KL-div: llama-perplexity -c 512 -b 512 --kl-divergence --kl-divergence-base base_logits_0.dat --chunks 50 --seed 1337
HumanEval+ / MBPP+: evalplus library, untrusted_check with base+plus variants, max_tokens=1024, temperature=0
BigCodeBench: custom harness using unittest test cases, 30s timeout per problem
LCB-30: LiveCodeBench easy subset, 30 problems × 3 attempts each (pass@1)
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%).

Provenance

Base model: Jackrong/Qwopus3.6-27B-Coder (BF16)
Recipe source: kaitchup/Qwen3.6-27B-GGUF-MoQ MoQ_qwen3.6-27b_tensors_4.0.txt
Methodology reference: Waleed Ahmad's MoQ (w-ahmad); kaitchup substack article
Calibration data: WikiText-2 test set (~80k lines), chunks=100
Multi-benchmark validation date: 2026-06-24 (Phase 5)
Build date: 2026-06-23
SHA256: 587840e75895199e5ad771bfa7dfd9682f6d85ae295ad00001b78adb485c52c1

Reproduce

# Clone recipes from kaitchup
wget https://huggingface.co/kaitchup/Qwen3.6-27B-GGUF-MoQ/resolve/main/MoQ_qwen3.6-27b_tensors_4.0.txt

# Download Qwopus BF16 shards and convert to F16 GGUF
# (see Jackrong/Qwopus3.6-27B-Coder for conversion instructions)

# Generate imatrix
llama-imatrix -m qwopus_f16.gguf -f wikitext-2-raw/wiki.test.raw \
  -o qwopus_imatrix.gguf -ngl 50 --chunks 100

# Quantize
llama-quantize --imatrix qwopus_imatrix.gguf \
  --tensor-type-file MoQ_qwen3.6-27b_tensors_4.0.txt \
  qwopus_f16.gguf qwopus_moq_4.0.gguf Q4_K

# Validate
./llama-perplexity -m qwopus_moq_4.0.gguf -f wikitext-2-raw/wiki.test.raw \
  -c 2048 --chunks 30

License

Apache 2.0 (inherited from base model)

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