Instructions to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF",
	filename="Qwen3.6-27B-Omnimerge-v4-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

Ollama
How to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with Ollama:
```
ollama run hf.co/ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M
```

Unsloth Studio

How to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF to start chatting

How to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with Docker Model Runner:
```
docker model run hf.co/ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M
```

Lemonade

How to use ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-Omnimerge-v4-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-27B-Omnimerge-v4-GGUF / README.md

ManniX-ITA

Update: canonical full-198q greedy GPQA (78.28%); add MTP companion pointer; refresh HE/MBPP with pod 37268930 numbers

1d0c680 verified 23 days ago

preview code

raw

history blame contribute delete

7.9 kB

	---
	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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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](https://gist.github.com/bartowski1182/82ae9b520227f57d79ba04add13d0d0d), 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`](https://huggingface.co/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](https://huggingface.co/ManniX-ITA/Qwen3.6-27B-Omnimerge-v4) 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](https://huggingface.co/ManniX-ITA/Qwen3.6-27B-Omnimerge-v4) 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](https://github.com/ggml-org/llama.cpp):

	```bash
	# 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`](https://huggingface.co/bartowski/Qwen_Qwen3.6-27B-GGUF) and works with this model unchanged (vision tower is preserved verbatim from the base).

	With [ollama](https://ollama.ai): 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/`](https://huggingface.co/ManniX-ITA/Qwen3.6-27B-Omnimerge-v4/tree/main/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](https://huggingface.co/Qwen) for the Qwen3.6 base
	- [rico03](https://huggingface.co/rico03), [ValiantLabs](https://huggingface.co/ValiantLabs), [kai-os](https://huggingface.co/kai-os) for the fine-tunes
	- [bartowski](https://huggingface.co/bartowski) for the calibration_datav5.txt set used here
	- DARE / TIES / DARE-TIES authors and the [arcee-ai/mergekit](https://github.com/arcee-ai/mergekit) community