Instructions to use noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-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 noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-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 noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

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 noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

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 noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

LM Studio
Jan

vLLM

How to use noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

Ollama
How to use noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF with Ollama:
```
ollama run hf.co/noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE
```
Unsloth Desktop

How to use noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

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": "noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF with Docker Model Runner:
```
docker model run hf.co/noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE
```

Lemonade

How to use noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF-MXFP4_MOE

List all available models

lemonade list

Hermes Agent

How to use noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-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 noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

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 noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE

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 "noctrex/Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF:MXFP4_MOE" \
  --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"

noctrex commited on May 16

Commit

de693c8

verified ·

1 Parent(s): 1f99273

Update README.md

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README.md +10 -2

README.md CHANGED Viewed

@@ -11,8 +11,9 @@ These are **MXFP4** quantizations of the model [Qwen3.6-35B-A3B](https://hugging
 This is the multi-token prediction (MTP) version.
-For the time being, the official mainline **llama.cpp** Does not support MTP yet, so you must compile one for yourself.
-This usually will entail pulling the PR 22673, and compiling it. You can find guides online, [like here](https://huggingface.co/havenoammo/Qwen3.6-35B-A3B-MTP-GGUF)
 ## Which version should I choose?
 All variants use **MXFP4** for the MoE (Mixture of Experts) weights to keep the model efficient. The difference lies in how the remaining tensors are handled:
@@ -27,3 +28,10 @@ All variants use **MXFP4** for the MoE (Mixture of Experts) weights to keep the
 Read the guide from unsloth in order to set up the model's recommended settings for MTP:
 [Qwen3.6 - MTP Guide](https://unsloth.ai/docs/models/qwen3.6#mtp-guide)

 This is the multi-token prediction (MTP) version.
+## Quick Start
+1. Download the latest release of **llama.cpp**.
+2. Download your preferred model variant from below.
 ## Which version should I choose?
 All variants use **MXFP4** for the MoE (Mixture of Experts) weights to keep the model efficient. The difference lies in how the remaining tensors are handled:
 Read the guide from unsloth in order to set up the model's recommended settings for MTP:
 [Qwen3.6 - MTP Guide](https://unsloth.ai/docs/models/qwen3.6#mtp-guide)
+On my system it works very well with the commands:
+```
+--spec-type draft-mtp
+--spec-draft-p-min 0.75
+--spec-draft-n-max 3
+```