Instructions to use michaelw9999/Qwen3.6-27B-MXFP6-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-MXFP6-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-MXFP6-MTP-GGUF
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF

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-MXFP6-MTP-GGUF
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwen3.6-27B-MXFP6-MTP-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 michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF
# Run inference directly in the terminal:
./llama-cli -hf michaelw9999/Qwen3.6-27B-MXFP6-MTP-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 michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF

Use Docker

docker model run hf.co/michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF

LM Studio
Jan
Ollama
How to use michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF with Ollama:
```
ollama run hf.co/michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF
```

Unsloth Studio

How to use michaelw9999/Qwen3.6-27B-MXFP6-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-MXFP6-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-MXFP6-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-MXFP6-MTP-GGUF to start chatting

How to use michaelw9999/Qwen3.6-27B-MXFP6-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-MXFP6-MTP-GGUF

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-MXFP6-MTP-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use michaelw9999/Qwen3.6-27B-MXFP6-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-MXFP6-MTP-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 "michaelw9999/Qwen3.6-27B-MXFP6-MTP-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"

Docker Model Runner
How to use michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF
```

Lemonade

How to use michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF

Run and chat with the model

lemonade run user.Qwen3.6-27B-MXFP6-MTP-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use michaelw9999/Qwen3.6-27B-MXFP6-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-MXFP6-MTP-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 michaelw9999/Qwen3.6-27B-MXFP6-MTP-GGUF

Run Hermes

hermes

Atomic Chat

Changed GGML TYPE to 50, latest mxfp6 build is required

This is an proof of concept/work in progress Qwen3.6-27B quantized into MXFP6. It was quantized with my still experimental advanced-gguf-quantizer tool.

This GGUF will ONLY work on llama.cpp. The CPU only PR is posted here:

https://github.com/ggml-org/llama.cpp/pull/22671

The PR runs very slowly because that is for the initial implementation without GPU support.

You may preview the very fast POC CUDA version from my fork:

https://github.com/michaelw9999/llama.cpp/tree/mxfp6-cuda

To merge into your existing llama.cpp installation:

git remote add mxfp6 https://github.com/michaelw9999/llama.cpp
git fetch mxfp6
git merge mxfp6/mxfp6-cuda
cmake -B build -DGGML_CUDA=ON
cmake --build build -j

Or to install fresh:

git clone -b mxfp6-cuda https://github.com/michaelw9999/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build -j

NOTICE:

This is my own work and is experimental and unofficial.

The CUDA version is not part of any llama.cpp PR (yet). This is not associated with NVIDIA in anyway.

Very likely, any future MXFP6 design will not be compatible with this implementation.

While the MoE model is faster than NVFP4 on Tg, it is not yet the case on this 27B Dense model. Further optimization is still needed. The best use case will be an upcoming NVFP4/MXFP6 blend to merge the higher speeds of NVFP4 with the better quality of MXFP6 only where needed.

 Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes, VRAM: 32606 MiB
| model                          |       size |     params | backend    | ngl |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| qwen35 27B NVFP4               |  17.50 GiB |    26.90 B | CUDA       |  99 |           pp512 |       5615.34 ± 7.77 |
| qwen35 27B NVFP4               |  17.50 GiB |    26.90 B | CUDA       |  99 |           tg128 |         64.60 ± 9.77 |

(without MTP)
  Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes, VRAM: 32606 MiB
| model                          |       size |     params | backend    | ngl |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| qwen35 27B MXFP6 - E2M3        |  20.98 GiB |    26.90 B | CUDA       |  99 |           pp512 |       3924.64 ± 4.79 |
| qwen35 27B MXFP6 - E2M3        |  20.98 GiB |    26.90 B | CUDA       |  99 |           tg128 |         41.15 ± 3.57 |

Feedback is both requested and encouraged so I can make further improvements into future llama.cpp PRs.

The NVFP4/MXFP6 quantizer is still being improved and will be posted in the future. Please let me know if you want to see a specific model turned into MXFP6.

The quality improvement of MXFP6 vs NVFP4 is quite striking! The full kld against the BF16 model is below.

MXFP6 KLD vs BF16:

====== Perplexity statistics ======
Mean PPL(Q)                   :   6.918624 ±   0.045510
Mean PPL(base)                :   6.900856 ±   0.045374
Cor(ln(PPL(Q)), ln(PPL(base))):  99.52%
Mean ln(PPL(Q)/PPL(base))     :   0.002571 ±   0.000645
Mean PPL(Q)/PPL(base)         :   1.002575 ±   0.000646
Mean PPL(Q)-PPL(base)         :   0.017768 ±   0.004457

====== KL divergence statistics ======
Mean    KLD:   0.018925 ±   0.000647
Maximum KLD:  28.370348
99.9%   KLD:   2.194927
99.0%   KLD:   0.141343
95.0%   KLD:   0.038436
90.0%   KLD:   0.022466
Median  KLD:   0.004691
10.0%   KLD:   0.000123
 5.0%   KLD:   0.000033
 1.0%   KLD:   0.000004
 0.1%   KLD:  -0.000001
Minimum KLD:  -0.000054

====== Token probability statistics ======
Mean    Δp: -0.102 ± 0.010 %
Maximum Δp: 99.909%
99.9%   Δp: 27.867%
99.0%   Δp:  8.482%
95.0%   Δp:  3.660%
90.0%   Δp:  2.084%
75.0%   Δp:  0.405%
Median  Δp: -0.001%
25.0%   Δp: -0.540%
10.0%   Δp: -2.405%
 5.0%   Δp: -4.104%
 1.0%   Δp: -9.415%
 0.1%   Δp: -29.850%
Minimum Δp: -99.768%
RMS Δp    :  3.662 ± 0.060 %
Same top p: 95.023 ± 0.057 %

and for comparison, NVFP4:

====== Perplexity statistics ======
Mean PPL(Q)                   :   7.321749 ±   0.049291
Mean PPL(base)                :   6.900856 ±   0.045374
Cor(ln(PPL(Q)), ln(PPL(base))):  98.00%
Mean ln(PPL(Q)/PPL(base))     :   0.059204 ±   0.001341
Mean PPL(Q)/PPL(base)         :   1.060991 ±   0.001423
Mean PPL(Q)-PPL(base)         :   0.420893 ±   0.010245

====== KL divergence statistics ======
Mean    KLD:   0.079692 ±   0.001052
Maximum KLD:  24.928333
99.9%   KLD:   5.620802
99.0%   KLD:   0.820752
95.0%   KLD:   0.239744
90.0%   KLD:   0.137482
Median  KLD:   0.027444
10.0%   KLD:   0.000694
 5.0%   KLD:   0.000195
 1.0%   KLD:   0.000026
 0.1%   KLD:   0.000004
Minimum KLD:  -0.000092

====== Token probability statistics ======
Mean    Δp: -0.808 ± 0.020 %
Maximum Δp: 99.897%
99.9%   Δp: 49.456%
99.0%   Δp: 17.496%
95.0%   Δp:  7.536%
90.0%   Δp:  4.210%
75.0%   Δp:  0.701%
Median  Δp: -0.019%
25.0%   Δp: -1.609%
10.0%   Δp: -6.312%
 5.0%   Δp: -10.856%
 1.0%   Δp: -28.085%
 0.1%   Δp: -73.216%
Minimum Δp: -99.926%
RMS Δp    :  7.764 ± 0.063 %
Same top p: 89.020 ± 0.081 %