---

base_model: ManniX-ITA/Qwen3.6-27B-Omnimerge-v4

base_model_relation: quantized

license: apache-2.0

language:

• en

tags:

- gguf

- imatrix

- quantized

- merge

- mergekit

- qwen3_5

- reasoning

- code

pipeline_tag: image-text-to-text

library_name: gguf

---

Qwen3.6-27B-Omnimerge-v4-GGUF

GGUF quantizations of ManniX-ITA/Qwen3.6-27B-Omnimerge-v4 — the MLP-passthrough variant that defends against the Qwen3.6 think-policy fragility we discovered. Source dtype is BF16; this repo provides the standard bartowski quant ladder (F16 → IQ2_XXS) for llama.cpp.

> Source model: ManniX-ITA/Qwen3.6-27B-Omnimerge-v4 (BF16 weights, model card with full benchmarks and methodology).

> NOT a quant of clean Qwen/Qwen3.6-27B — these GGUFs contain the v4 merge.

>

> MTP companion (2× decode speedup): weight-identical GGUFs with the MTP head retained for llama.cpp --spec-type draft-mtp self-speculative decoding are at ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-MTP-GGUF. Quality is statistically indistinguishable from this repo (HE 137/164 ↔ 137/164, GPQA 155/198 ↔ 154/198); aggregate decode is 2.0-2.3 × faster on a single 24 GB GPU. Use that repo for interactive / single-request workloads where latency matters.

All quants made using imatrix with calibration data v5, the same calibration set bartowski uses for the Qwen3.6 base release — so quality fingerprints are directly comparable to bartowski's Qwen_Qwen3.6-27B-GGUF repo.

Why this merge exists

Same-base DARE-TIES (Omnimerge_v2 method) merge of Qwen/Qwen3.6-27B + 3 Qwen3.6 fine-tunes. Direct successor to ManniX-ITA/Qwen3.5-27B-Omnimerge-v2 on the newer Qwen3.6 base, with mlp.{gate,up,down}_proj copied verbatim from clean Qwen3.6 (the "MLP-passthrough" surgery) to defend against a Qwen3.6-specific reasoning-tag fragility we found during forensic delta inspection. See the v4 model card for the full story, scripts, and benchmark methodology.

Benchmark headline (Q6_K, head-to-head vs Qwen3.6 base + Omnimerge-v2)

All scored under identical llama.cpp + lm_eval conditions (--reasoning-format deepseek --reasoning-budget 8192 --parallel 2, raw /v1/completions, no chat template).

| Benchmark | Qwen3.6 base Q6_K (bartowski) | Omnimerge-v2 (Qwen3.5 base) | Omnimerge-v4-MLP (this) | Δ vs base | Δ vs v2 |

|---|---|---|---|---|---|

| HumanEval pass@1 (164q) | 84.76% | 79.27% | 83.54% (137/164) | −1.22 pp | +4.27 pp |

| MBPP pass@1 (500q) — corrected\* | 57.60% | 74.60% | 73.00% (365/500) | +15.40 pp | −1.60 pp |

| GPQA Diamond pass@1 (flex) — full greedy§ | not measured | 69.19% (full 198q) | 78.28% (155/198) | — | +9.09 pp |

\* MBPP scores are post-<think>-stripping (lm_eval's raw scorer SyntaxErrors on literal < in exec(prompt+completion+tests)). See the v4 model card for the per-model recovery breakdown.

§ Canonical full-198q greedy GPQA result measured 2026-05-22 on pod 37268930 (Vast.ai 3090) with the patched eval chain (lm-eval 0.4.11 + max_length=32768 override + the api_models.py:545 UnboundLocalError patch + aiohttp lifecycle workaround). Sampler: do_sample=False, temperature=0.0, max_gen_toks=8192. Wall time 4 h 55 min. Companion strict-match (rigid Answer: X template) is 7.58 % — the model emits CoT verbosely rather than the strict template, so flex is the real quality signal. Earlier card revisions reported an ≈ 84.75 % partial result (177/198 sampled at T=0.6, budget=16384); that number is superseded by this canonical greedy measurement on the full bench — the 6.5 pp difference is driven by the methodology change (sampler / budget / completeness), not by a model change.

Available Quantizations

All 27 files (F16 + 26 imatrix-quantized tiers, ~417 GB total) are uploaded and ready. imatrix.dat (used for every quant) is in the repo root for audit and reproduction.

| Quantization | File size | Use case |

|---|---|---|

| F16 (full precision) | 50.11 GB | Conversion source / lossless reference |

| Q8_0 | 26.63 GB | Highest fidelity, large |

| Q6_K_L | 21.14 GB | Q6_K with embed/output at Q8_0 |

| Q6_K | 20.57 GB | Recommended high tier — eval methodology used this |

| Q5_K_L | 18.64 GB | Q5_K_M with embed/output at Q8_0 |

| Q5_K_M | 17.91 GB | Strong fidelity, balanced |

| Q5_K_S | 17.40 GB | Slightly smaller K-mix |

| Q4_K_L | 16.29 GB | Q4_K_M with embed/output at Q8_0 |

| Q4_1 | 15.91 GB | Legacy 4-bit, dense |

| Q4_K_M | 15.41 GB | Recommended balanced tier for most users |

| IQ4_NL | 14.72 GB | Importance-aware 4-bit non-linear |

| Q4_K_S | 14.52 GB | K-mix small variant |

| Q4_0 | 14.41 GB | Legacy 4-bit |

| IQ4_XS | 14.05 GB | IQ4 extra-small |

| Q3_K_XL | 13.42 GB | Q3_K_L with embed/output at Q8_0 |

| Q3_K_L | 13.36 GB | 3-bit K-mix large |

| Q3_K_M | 12.39 GB | 3-bit K-mix medium |

| IQ3_M | 11.72 GB | Importance-aware 3-bit medium |

| Q3_K_S | 11.24 GB | 3-bit K-mix small |

| IQ3_XS | 11.15 GB | IQ3 extra-small |

| Q2_K_L | 11.13 GB | Q2_K with embed/output at Q8_0 |

| IQ3_XXS | 10.42 GB | IQ3 extra-extra-small |

| Q2_K | 9.98 GB | 2-bit K-mix |

| IQ2_M | 9.32 GB | Importance-aware 2-bit medium |

| IQ2_S | 8.72 GB | IQ2 small |

| IQ2_XS | 8.47 GB | IQ2 extra-small |

| IQ2_XXS | 7.85 GB | IQ2 extra-extra-small (smallest) |

How to Use

With llama.cpp:

# Recommended args for reasoning-tag-emitting models (matches the eval methodology):
llama-server \
    -m Qwen3.6-27B-Omnimerge-v4-Q4_K_M.gguf \
    -c 32768 -ngl 99 -t 12 --no-warmup \
    --reasoning-format deepseek --reasoning-budget 8192

Swap Q4_K_M for any tier from the table above. Q6_K matches the methodology used in our published evals; Q4_K_M is the typical "balanced" choice for most users.

For multimodal (vision) inference: the mmproj projector is in bartowski/Qwen_Qwen3.6-27B-GGUF and works with this model unchanged (vision tower is preserved verbatim from the base).

With ollama: use a Modelfile pointing to one of the GGUFs above, or HF direct load.

imatrix.dat

The imatrix.dat (~14 MB) used to generate every quant in this repo is uploaded alongside the GGUFs at the repo root. Reproducible, auditable.

Reproducing

See scripts/ on the source v4 model repo:

• dare_ties_merge.py — main merger (auto-detects Qwen3.6 base via output_gate_type and applies MLP-skip)
• v4_mlp_passthrough.py — post-process: rebuild merged dir with MLP layers from base
• quantize_gguf.py — the script that built this repo

For dense (non-Gemma-4-MoE) models, pass --exclude CD-Q6_K,CD-Q5_K_M,CD-Q4_K_M,CD-Q3_K_M,CD-Q2_K to skip ContribDynamic tiers (those require Gemma 4 expert-contribution maps).

License

Apache-2.0 (inherited from Qwen/Qwen3.6-27B and the fine-tune sources).

Acknowledgements

• Qwen team for the Qwen3.6 base
• rico03, ValiantLabs, kai-os for the fine-tunes
• bartowski for the calibration_datav5.txt set used here
• DARE / TIES / DARE-TIES authors and the arcee-ai/mergekit community