Image-Text-to-Text
MLX
Safetensors
qwen3_vl
apple-silicon
quantized
mixed-precision
axquant
axq
development
qwen3-vl
4bit
4-bit precision
v2
vision
conversational
Instructions to use AutomatosX/AX-Qwen3-VL-8B-Instruct-MLX-AXQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-Qwen3-VL-8B-Instruct-MLX-AXQ-4bit 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-VL-8B-Instruct-MLX-AXQ-4bit") config = load_config("AutomatosX/AX-Qwen3-VL-8B-Instruct-MLX-AXQ-4bit") # 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-VL-8B-Instruct-MLX-AXQ-4bit 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-VL-8B-Instruct-MLX-AXQ-4bit"
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-VL-8B-Instruct-MLX-AXQ-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use AutomatosX/AX-Qwen3-VL-8B-Instruct-MLX-AXQ-4bit 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-VL-8B-Instruct-MLX-AXQ-4bit"
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-VL-8B-Instruct-MLX-AXQ-4bit" \ --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"
- Hermes Agent
How to use AutomatosX/AX-Qwen3-VL-8B-Instruct-MLX-AXQ-4bit 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-VL-8B-Instruct-MLX-AXQ-4bit"
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-VL-8B-Instruct-MLX-AXQ-4bit
Run Hermes
hermes
File size: 1,224 Bytes
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"kernel_evidence": "unmeasured",
"model_manifest": "model-manifest.json",
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},
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"created_at": "2026-08-05T21:32:02.989689Z",
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"optimization_scope": "text-path",
"primary_runtime": {
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"manifest": "config.json",
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"name": "mlx-vlm",
"notes": [
"MLX-VLM loads the protected modality tower and AXQ language decoder."
],
"standard_inference": true,
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