Instructions to use kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF
# Run inference directly in the terminal:
llama cli -hf kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF
# Run inference directly in the terminal:
llama cli -hf kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

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 kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF
# Run inference directly in the terminal:
./llama-cli -hf kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

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 kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

Use Docker

docker model run hf.co/kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

LM Studio
Jan
Ollama
How to use kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF with Ollama:
```
ollama run hf.co/kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF
```
Unsloth Desktop

How to use kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF with Docker Model Runner:
```
docker model run hf.co/kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF
```

Lemonade

How to use kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

Run and chat with the model

lemonade run user.Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

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 kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

This is a Q4_K_L quantized GGUF version of ManniX-ITA/Qwen3.6-27B-Omnimerge-v4.

Why this quantization exists?

Based on my tests if you are a personal AI person with 24GB Vram, best quant is not Q4_K_M versions. Q4_K_L quantization is offers better performance-size-speed trio. I think this model is performing better among other Qwen3.6 27B versions for general purpose agentic AI use case.

Original Model

Base: ManniX-ITA/Qwen3.6-27B-Omnimerge-v4
Architecture: qwen3_5
Parameters: 27B
Task: Image-Text-to-Text

Quantization

Format: GGUF
Quantization: Q4_K_L
Quantized by: kdqemre

Usage with llama.cpp (Optimized Params for 24GB VRAM- You can try longer context length)

llama-server -m Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L.gguf \
  -ngl 99 \
  -c 16384 \
  -fa 1 \
  -np 1 --cache-type-k q8_0 --cache-type-v q5_1 --reasoning-format deepseek --reasoning-budget 2048 \
  --spec-type draft-mtp --spec-draft-n-max 3 --cache-type-k-draft q5_1 --cache-type-v-draft q5_1 --kv-unified \
  --port 8088

License

Original model licensed under Apache-2.0. This quantized version preserves the original license.

Downloads last month: 17

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for kdqemre/Qwen3.6-27B-Omnimerge-v4-MTP-Q4_K_L-GGUF

Base model

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

Quantized

(11)

this model