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)

vs Best Source Model (Claude-distill)

Method: Omnimerge v2

Four enhancements over standard DARE-TIES (v1):

1. 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.

2. DAREx rescaling (based on DAREx, arXiv 2410.09344, ICLR 2025): Survivors divided by configurable q instead of density. Lower variance than standard DARE rescaling.

3. 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

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

License

Apache-2.0

Base model

Qwen/Qwen3.5-27B

(279)