Instructions to use AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use AutomatosX/AX-Qwen3-Coder-Next-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-Coder-Next-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-Coder-Next-MLX-AXQ-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AutomatosX/AX-Qwen3-Coder-Next-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-Coder-Next-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-Coder-Next-MLX-AXQ-4bit

Run Hermes

hermes

OpenClaw new

How to use AutomatosX/AX-Qwen3-Coder-Next-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-Coder-Next-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-Coder-Next-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"

MLX LM

How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

AX-Qwen3-Coder-Next-MLX-AXQ-4bit / axquant_runtime.json

AutomatosX

Add development AXQ pack AX-Qwen3-Coder-Next-MLX-AXQ-4bit

edb8457 verified about 19 hours ago

Raw

History Blame Contribute Delete

1.64 kB

	{
	"ax_engine": {
	"decode_kernel": null,
	"fused_mtp": null,
	"kernel_evidence": "unmeasured",
	"model_manifest": "model-manifest.json",
	"preferred_group_size": 32
	},
	"compatible_runtimes": [
	{
	"compatibility_level": "B",
	"manifest": "config.json",
	"mtp_support": "runtime-dependent",
	"name": "mlx-lm",
	"notes": [
	"Standard backbone inference is the compatibility target.",
	"AXQuant MTP metadata may be ignored by MLX-LM."
	],
	"standard_inference": true,
	"standard_mlx_weights": true,
	"support_level": "standard-inference"
	}
	],
	"created_at": "2026-08-02T21:26:21.520101Z",
	"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": "model-manifest.json",
	"mtp_support": "native",
	"name": "ax-engine",
	"notes": [
	"Runtime claims require a passing AX Engine doctor and benchmark report."
	],
	"standard_inference": true,
	"standard_mlx_weights": true,
	"support_level": "optimized"
	},
	"schema_version": "axquant.runtime.v1"
	}