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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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

Hermes Agent

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

michaelw9999 commited on Jun 6

Commit

287d5c2

verified ·

1 Parent(s): 1970b79

Update README.md

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README.md +5 -0

README.md CHANGED Viewed

@@ -22,6 +22,11 @@ Recently re-quantized with the latest version of <A HREF="https://github.com/mic
     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.
   </p>
 <p>Feedback to improve this is appreciated.</p>
 Results below on RTX 5090 with Wikitest:
   <table>

     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.
   </p>
+  <p>I am experimenting with a smaller verison of this model designed to be used on machines with 16GB of VRAM:<BR>
+   <A HREF="https://huggingface.co/michaelw9999/Qwen3.6-27B-NVFP4-SMALL-MTP-GGUF">Qwen3.6-27B-NVFP4-SMALL-MTP-GGUF</A>.<BR>
+    The SMALL version will be slower than this model but should maintain similar quality, although I am still evaluating it.<BR></BR>
+  </p>
 <p>Feedback to improve this is appreciated.</p>
 Results below on RTX 5090 with Wikitest:
   <table>