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

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

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

AX-Qwen3-VL-8B-Instruct-MLX-AXQ-4bit / axquant_runtime.json

AutomatosX

Publish AXQuant v2 development artifact

e00257e verified 4 days ago

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	{
	"ax_engine": {
	"decode_kernel": null,
	"fused_mtp": null,
	"kernel_evidence": "unmeasured",
	"model_manifest": "model-manifest.json",
	"preferred_group_size": 32
	},
	"compatible_runtimes": [],
	"created_at": "2026-08-05T21:32:02.989689Z",
	"kv_cache": null,
	"memory_policy": {
	"kv_cache_precision": "runtime-default",
	"mtp_buffers": "not-required",
	"prefix_cache": "runtime-managed",
	"unified_memory_safety_margin": "benchmark-required"
	},
	"mtp": {
	"acceptance_retention": null,
	"detected": false,
	"draft_tokens": null,
	"enabled_by_default": false,
	"head_precision": null,
	"measured_speedup": null,
	"optimized": false,
	"recommended_temperature_max": null,
	"sidecar_file": null,
	"verification_mode": null
	},
	"optimization_scope": "text-path",
	"primary_runtime": {
	"compatibility_level": "A",
	"manifest": "config.json",
	"mtp_support": "none",
	"name": "mlx-vlm",
	"notes": [
	"MLX-VLM loads the protected modality tower and AXQ language decoder."
	],
	"standard_inference": true,
	"standard_mlx_weights": true,
	"support_level": "standard-inference"
	},
	"schema_version": "axquant.runtime.v1"
	}