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Qwen3.5-27B-Omnimerge-v2
ManniX-ITA/Qwen3.5-27B-Omnimerge-v2
Qwen3.5-27B-Omnimerge-v2 is a machine learning model from ManniX-ITA. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
An improved 3-way weight-space merge of Qwen3.5-27B reasoning-distilled fine-tunes using the Omnimerge v2 method — combining four recent advances in model merging.
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Model card
From the Hugging Face model README
Qwen3.5-27B-Omnimerge-v2
An improved 3-way weight-space merge of Qwen3.5-27B reasoning-distilled fine-tunes using the Omnimerge v2 method — combining four recent advances in model merging.
GGUF quantizations available at ManniX-ITA/Qwen3.5-27B-Omnimerge-v2-GGUF
Benchmark Results (Q6_K)
| Benchmark | Omnimerge v1 | Omnimerge v2 | Delta |
|---|---|---|---|
| GPQA Diamond (198q, flex) | 61.11% | 69.19% | +8.08 pp |
| MBPP pass@1 | 71.80% | 74.60% | +2.80 pp |
| HumanEval pass@1 | 79.88% | 79.27% | -0.61 pp |
vs Best Source Model (Claude-distill)
| Benchmark | Claude-distill | Omnimerge v2 | Delta |
|---|---|---|---|
| GPQA Diamond (198q, flex) | 53.03% | 69.19% | +16.16 pp |
| MBPP pass@1 | 71.20% | 74.60% | +3.40 pp |
| HumanEval pass@1 | 76.22% | 79.27% | +3.05 pp |
Method: Omnimerge v2
Four enhancements over standard DARE-TIES (v1):
-
OBIM-lite magnitude masking (based on OBIM, arXiv 2502.12217): Deterministic top-k masking by |delta| magnitude instead of random Bernoulli drop. Keeps the most informative parameter changes.
-
DAREx rescaling (based on DAREx, arXiv 2410.09344, ICLR 2025): Survivors divided by configurable
qinstead ofdensity. Lower variance than standard DARE rescaling. -
EMR election (based on EMR-Merging, arXiv 2405.17461, NeurIPS 2024): Sign from weighted-sum consensus, amplitude from max abs across sources. Each parameter gets the strongest signal from whichever source specialized most.
The merge script also supports GPU-accelerated computation (chunks offloaded to CUDA for ~35x speedup over CPU-only).
Not yet implemented (available in the script for future iterations):
- Fisher weighting (based on Fisher-Merging, Matena & Raffel 2022): Per-parameter adaptive weighting using diagonal Fisher information. Requires a calibration pre-computation step per source model. Currently uses fixed source weights.
Merge Configuration
python dare_ties_merge.py \
--base Qwen/Qwen3.5-27B \
--source Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled \
--source ValiantLabs/Qwen3.5-27B-Esper3.1 \
--source Jackrong/Qwen3.5-27B-Gemini-3.1-Pro-Reasoning-Distill \
--method omnimerge_v2 --density 0.53 --weights 0.40,0.35,0.25 \
--darex-q 0.75 --seed 42
Source Models
| Source | Weight | Focus |
|---|---|---|
| Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled | 0.40 | Claude 4.6 Opus reasoning distillation |
| ValiantLabs/Qwen3.5-27B-Esper3.1 | 0.35 | Code / DevOps specialist |
| Jackrong/Qwen3.5-27B-Gemini-3.1-Pro-Reasoning-Distill | 0.25 | Gemini 3.1 Pro reasoning distillation |
Base: Qwen/Qwen3.5-27B
Usage
llama.cpp (recommended)
llama-server -m Qwen3.5-27B-Omnimerge-v2-Q6_K.gguf -c 32768 -ngl 99 \
--reasoning-format deepseek --reasoning-budget 16384 \
--temp 0.6 --top-p 0.95 --top-k 20 --dry-multiplier 0.5
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"ManniX-ITA/Qwen3.5-27B-Omnimerge-v2",
torch_dtype=torch.bfloat16,
device_map="auto",
)
tok = AutoTokenizer.from_pretrained("ManniX-ITA/Qwen3.5-27B-Omnimerge-v2")
Related Models
| Model | Description |
|---|---|
| Qwen3.5-27B-Omnimerge | v1 (DARE-TIES baseline) |
| Qwen3.5-27B-Omnimerge-GGUF | v1 GGUF quants |
| Qwen3.5-27B-Omnimerge-v2-GGUF | v2 GGUF quants |
License
Apache-2.0
- Type
- qwen3_5
- License
- apache-2.0
- Created
- Apr 13, 2026
- Updated
- Apr 13, 2026