Instructions to use AutomatosX/AX-Qwen3.5-9B-MLX-4bit-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-Qwen3.5-9B-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-Qwen3.5-9B-MLX-4bit-MTP") config = load_config("AutomatosX/AX-Qwen3.5-9B-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-Qwen3.5-9B-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-Qwen3.5-9B-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-Qwen3.5-9B-MLX-4bit-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AutomatosX/AX-Qwen3.5-9B-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-Qwen3.5-9B-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-Qwen3.5-9B-MLX-4bit-MTP
Run Hermes
hermes
- OpenClaw new
How to use AutomatosX/AX-Qwen3.5-9B-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-Qwen3.5-9B-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-Qwen3.5-9B-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 Qwen3.5 9B MLX 4-bit MTP
Parameter count: approximately 9.41B logical target parameters (9B 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 package for Apple Silicon. It combines the 4-bit MLX quantization of Qwen3.5-9B with an AX Engine-compatible multi-token-prediction (MTP) sidecar.
The target model verifies drafted tokens during speculative decoding. This repository does not contain PyTorch or GGUF weights.
Model details
- Base model: Qwen/Qwen3.5-9B
- MLX target: mlx-community/Qwen3.5-9B-MLX-4bit, revision
938d8919941c6e7efd3c7150eff7fe9d12afa631 - Format: MLX Safetensors
- Quantization: 4-bit affine, group size 64
- Task: image-text-to-text and conversational generation
- Configured context length: 262,144 tokens
- MTP sidecar: 15 tensors, recurrent draft depth 1
- Intended hardware: Apple Silicon
The MLX target weights are byte-identical to the pinned source revision. The
MTP tensors were extracted from
Qwen/Qwen3.5-9B at revision
c202236235762e1c871ad0ccb60c8ee5ba337b9a and normalized with AX Engine's
prepare_mtp_sidecar.py flow.
Download
hf download AutomatosX/AX-Qwen3.5-9B-MLX-4bit-MTP \
--local-dir ./AX-Qwen3.5-9B-MLX-4bit-MTP
The repository is approximately 6.5 GB. Ensure that the destination has enough free space before downloading.
Use as a standard MLX model
Standard MLX-VLM inference loads the two model-*.safetensors target shards
and ignores the AX-specific MTP sidecar:
pip install -U mlx-vlm
python -m mlx_vlm.generate \
--model AutomatosX/AX-Qwen3.5-9B-MLX-4bit-MTP \
--max-tokens 100 \
--temperature 0.0 \
--prompt "Describe this image." \
--image <path-to-image>
Use with AX Engine MTP
Install AX Engine, download the repository, and serve the local directory:
ax-engine serve ./AX-Qwen3.5-9B-MLX-4bit-MTP --port 31418
The MTP package includes:
mtp.safetensors: normalized 15-tensor MTP headmtplx_runtime.json: draft-depth and sampler guidanceax_mtp_sidecar_manifest.json: pinned source hashes and transformation provenancemodel-manifest.json: AX Engine native target manifestconfig.json: MLX target configuration with MTP sidecar fields
Validation and provenance
Validated on macOS arm64 with AX Engine 6.9.0 on 2026-07-20:
- AX native artifact validation:
ready, with no issues - All target weight shards: byte-exact against the pinned MLX source; the shard index keeps the upstream weight map and adds only the logical parameter count
- MTP tensor count: 15
- MTP draft depth: 1
- Norm transform: add 1 to MTP RMSNorm delta weights
- Exactness baseline: maximum absolute difference
0.0at context length 2,048 - MTP provenance strict local re-hash: passed
The public provenance files use repository-relative paths and immutable Hugging Face URIs. They intentionally contain no local filesystem paths.
License
Apache License 2.0. See LICENSE and the original Qwen model card for model
limitations and responsible-use guidance.
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