Instructions to use michaelw9999/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF
# Run inference directly in the terminal:
./llama-cli -hf michaelw9999/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf michaelw9999/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF

Use Docker

docker model run hf.co/michaelw9999/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF

LM Studio
Jan
Ollama
How to use michaelw9999/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF with Ollama:
```
ollama run hf.co/michaelw9999/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF
```
Unsloth Desktop

How to use michaelw9999/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-MXFP6-MTP-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": "michaelw9999/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use michaelw9999/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/michaelw9999/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF
```

Lemonade

How to use michaelw9999/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull michaelw9999/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-MXFP6-MTP-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use michaelw9999/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-MXFP6-MTP-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use michaelw9999/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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"

Changed GGML TYPE to 50, latest mxfp6 build is required

This is an proof of concept/work in progress Qwen3.6-35B-A3B quantized into MXFP6.
It was quantized with my experimental advanced-gguf-quantizer tool.
This GGUF will ONLY work with llama.cpp.
The CPU only PR is posted on llama.cpp here:

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

That PR runs slowly because it is for an initial CPU only implementation without GPU support.

You may install 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.

For Qwen3.6-35B, MXFP6 is almost as fast as NVFP4 on prefill, and is now even faster with MTP.

Using FP8 for activations, it is faster than NVFP4 on tokengen.

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

MXFP6: Final estimate: PPL = 6.7890 +/- 0.04420

(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 |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| qwen35moe 35B.A3B MXFP6 - E2M3 |  26.46 GiB |    34.66 B | CUDA       |  99 |           pp512 |      8094.43 ± 49.53 |
| qwen35moe 35B.A3B MXFP6 - E2M3 |  26.46 GiB |    34.66 B | CUDA       |  99 |           tg128 |        188.10 ± 3.20 |

  Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes, VRAM: 32606 MiB
| model                          |       size |     params | backend    | ngl |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| qwen35moe 35B.A3B NVFP4        |  21.48 GiB |    34.66 B | CUDA       |  99 |           pp512 |      8220.18 ± 57.89 |
| qwen35moe 35B.A3B NVFP4        |  21.48 GiB |    34.66 B | CUDA       |  99 |           tg128 |        159.53 ± 0.82 |