Instructions to use AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP") config = load_config("AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP"
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 AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP
Run Hermes
hermes
- OpenClaw new
How to use AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP"
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 "AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
AX Qwen3.6 27B MLX 4-bit MTP
Parameter count: approximately 27.36B logical target parameters (27B class).
4-bitis the target quantization precision, not a 4B model-size claim. The separately packaged MTP sidecar is not included in the target count.
This is an MLX model for Apple Silicon. It packages the 4-bit MLX quantization of Qwen3.6-27B together with an AX Engine-compatible multi-token-prediction (MTP) sidecar.
This repository does not contain PyTorch or GGUF weights.
Model details
- Base model: Qwen/Qwen3.6-27B
- MLX base: mlx-community/Qwen3.6-27B-4bit
- Format: MLX Safetensors
- Quantization: 4-bit affine, group size 64
- Task: image-text-to-text and conversational generation
- Context length: 262,144 tokens in the model configuration
- MTP sidecar: 15 tensors, draft depth up to 3
- Intended hardware: Apple Silicon
The base MLX conversion was produced with mlx-vlm 0.4.4. The MTP tensors
come from the official Qwen checkpoint and were normalized with AX Engine's
prepare_qwen36_mtp_sidecar.py packaging flow.
Download
hf download AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP \
--local-dir ./AX-Qwen3.6-27B-MLX-4bit-MTP
The repository is approximately 16.93 GB. Ensure that the destination has enough free space before downloading.
Use as an MLX model
For standard MLX-VLM inference without AX MTP acceleration:
pip install -U mlx-vlm
python -m mlx_vlm.generate \
--model AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP \
--max-tokens 100 \
--temperature 0.0 \
--prompt "Describe this image." \
--image <path-to-image>
The three model-*.safetensors shards are the MLX base weights. Standard
MLX-VLM loading uses those shards through model.safetensors.index.json and
does not use the AX-specific mtp.safetensors sidecar.
Use with AX Engine MTP
Install AX Engine, download the repository, and serve the local directory:
hf download AutomatosX/AX-Qwen3.6-27B-MLX-4bit-MTP \
--local-dir ./AX-Qwen3.6-27B-MLX-4bit-MTP
ax-engine serve ./AX-Qwen3.6-27B-MLX-4bit-MTP --port 31418
The MTP package includes:
mtp.safetensors: the normalized MTP head.mtplx_runtime.json: draft-depth and sampler guidance.ax_mtp_sidecar_manifest.json: source hashes and transformation provenance.config.json: the MLX base configuration with MTP sidecar fields.
Validation and provenance
The sidecar preparation gate recorded:
- Runtime architecture identifier:
qwen3-next-mtp - MTP tensor count: 15
- Norm transform: add 1 to MTP RMSNorm delta weights
- Exactness baseline: maximum absolute difference
0.0at context length 2048 - Validation platform: macOS on arm64
- Validation timestamp: 2026-06-23
The provenance manifest uses repository-relative paths and public Hugging Face URIs. It intentionally excludes local filesystem paths.
License
This repository follows the upstream Apache License 2.0. See LICENSE and the
original Qwen model card for the model's terms, limitations, and responsible-use
guidance.
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Base model
Qwen/Qwen3.6-27B