Instructions to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF",
	filename="Qwen3.6-27B-NVFP4-MTP-GGUF.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-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 michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

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 michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

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 michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

Use Docker

docker model run hf.co/michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

LM Studio
Jan
Ollama
How to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF with Ollama:
```
ollama run hf.co/michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4
```

Unsloth Studio

How to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-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 michaelw9999/Qwen3.6-27B-NVFP4-MTP-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 michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF to start chatting

How to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

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": "michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-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 michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

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 michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

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 "michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4" \
  --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"

Docker Model Runner
How to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4
```

Lemonade

How to use michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF:NVFP4

Run and chat with the model

lemonade run user.Qwen3.6-27B-NVFP4-MTP-GGUF-NVFP4

List all available models

lemonade list

Recently re-quantized with the latest version of advanced-gguf-quantizer tool.

MTP included.

Qwen3.6-27B RSF NVFP4 GGUF v4

This latest (6-June-2026) NVFP4 quant of Qwen3.6-27B keeps quality very high, while improving KLD, tail KLD metrics, top-token stability, and probability error compared to the earlier releases. It is faster and smaller in size. It applies my RSF scale fitting technique to the Q_K quants and uses a different tensor mix layout compared to previously. 6-June-2026: changed the MTP tensors to be NVFP4, ~130tk/s tg is now possible in some configurations on 5090.

I am experimenting with a smaller verison of this model designed to be used on machines with 16GB of VRAM:
Qwen3.6-27B-NVFP4-SMALL-MTP-GGUF.
The SMALL version will be slower than this model but should maintain similar quality, although I am still evaluating it.

Feedback to improve this is appreciated.

Results below on RTX 5090 with Wikitest:

Model	Size GiB	pp512	tg128	pg32768,256
RSF NVFP4	15.27	5174.31 ± 1.30	76.61 ± 0.18	2751.93 ± 23.73

Quality Metric	Earlier NVFP4	Improved RSF NVFP4	Change
Mean PPL(Q)	7.128133 ± 0.047613	7.030348 ± 0.046636	1.37% lower
Mean PPL(base)	6.900856 ± 0.045374	6.900856 ± 0.045374	(Same Base)
PPL gap vs base	0.227277 ± 0.008252	0.129492 ± 0.006913	43.0% smaller gap
Mean PPL(Q) / PPL(base)	1.032935 ± 0.001175	1.018765 ± 0.000996	43.0% lower overhead
Mean ln(PPL(Q) / PPL(base))	0.032404 ± 0.001137	0.018591 ± 0.000978	42.6% lower
Correlation ln(PPL)	98.54%	98.91%	+0.37 pp

KLD Metric	Earlier v2	Latest RSF NVFP4	Change
Mean KLD	0.058781 ± 0.000935	0.044590 ± 0.000832	24.1% lower
99.9% KLD	4.869176	3.726478	23.5% lower
99.0% KLD	0.591435	0.422199	28.6% lower
95.0% KLD	0.165489	0.122647	25.9% lower
90.0% KLD	0.096133	0.070556	26.6% lower
Median KLD	0.019355	0.013642	29.5% lower
Maximum KLD	20.671190	24.703529	higher

Token Probability Metric	Earlier NVFP4	Improved RSF NVFP4	Change
RMS Δp	6.613 ± 0.060%	5.774 ± 0.060%	12.7% lower
Same-top probability	90.450 ± 0.076%	91.924 ± 0.071%	+1.474 pp
Top-flip weight	0.009715	0.007193	26.0% lower
Top-prob RMSE	0.080946	0.069932	13.6% lower
Entropy RMSE	0.248197	0.212250	14.5% lower

Downloads last month: 43,643

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Inference Providers NEW

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

Model tree for michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

(664)

this model

Collection including michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF

Quantized NVFP4 Models

Collection

6 items • Updated Jun 12 • 2