Instructions to use AutomatosX/AX-Qwen3-VL-4B-Instruct-MLX-AXQ-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AutomatosX/AX-Qwen3-VL-4B-Instruct-MLX-AXQ-MXFP4 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-4B-Instruct-MLX-AXQ-MXFP4")
config = load_config("AutomatosX/AX-Qwen3-VL-4B-Instruct-MLX-AXQ-MXFP4")

# 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-4B-Instruct-MLX-AXQ-MXFP4 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-4B-Instruct-MLX-AXQ-MXFP4"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-4B-Instruct-MLX-AXQ-MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use AutomatosX/AX-Qwen3-VL-4B-Instruct-MLX-AXQ-MXFP4 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-4B-Instruct-MLX-AXQ-MXFP4"

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-4B-Instruct-MLX-AXQ-MXFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AutomatosX/AX-Qwen3-VL-4B-Instruct-MLX-AXQ-MXFP4 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-4B-Instruct-MLX-AXQ-MXFP4"

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-4B-Instruct-MLX-AXQ-MXFP4" \
  --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-VL-4B-Instruct-MLX-AXQ-MXFP4 / axquant_source_binding.json

AutomatosX

Publish AX-Qwen3-VL-4B-Instruct-MLX-AXQ-MXFP4

bb64baf verified 9 days ago

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	{
	"config_sha256": "edac7703329133edfc53e46ac0081835144c99d7eebf28b71c732694d435224d",
	"created_at": "2026-10-04T01:41:33.749120Z",
	"index_sha256": "58a7841d7bff2548dd91577d216274a83cf1b500bc6a534b809d6c1b1707cf2b",
	"members": [
	{
	"path": "model-00001-of-00002.safetensors",
	"size_bytes": 4967229296
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	"plan_sha256": "45d4900f911b1cfae1b29ddebf855400226ed4da94b2112836d41ac9a01b940e",
	"schema_version": "axquant.source-plan-binding.v1",
	"source_model": {
	"architecture": "Qwen3VLForConditionalGeneration",
	"format": "mlx",
	"local_path": null,
	"model_id": "Qwen/Qwen3-VL-4B-Instruct",
	"revision": "ebb281ec70b05090aa6165b016eac8ec08e71b17"
	}
	}