Instructions to use AutomatosX/AX-Qwen3-Coder-Next-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-6bit 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-6bit")

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-6bit 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-6bit"

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-6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-6bit 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-6bit"

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-6bit

Run Hermes

hermes

OpenClaw new

How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-6bit 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-6bit"

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-6bit" \
  --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-6bit 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-6bit"

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-6bit"
# 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-6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

AX-Qwen3-Coder-Next-MLX-6bit / README.md

AutomatosX

Correct logical model size information

e9519fc verified 10 days ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3-Coder-Next
	pipeline_tag: text-generation
	library_name: mlx
	tags:
	- mlx
	- apple-silicon
	- qwen3-next
	- coding
	- mixture-of-experts
	- quantized
	- 6-bit
	- mirror
	- automatosx
	- ax-engine
	---

	# AX Qwen3 Coder Next MLX 6-bit

	> Parameter count: approximately 79.67B logical parameters (80B total,
	> 3B active per token). `6-bit` is the quantization precision, not a 6B
	> model-size claim.

	This is a revision-pinned, transparent mirror of
	[mlx-community/Qwen3-Coder-Next-6bit](https://huggingface.co/mlx-community/Qwen3-Coder-Next-6bit)
	at commit `9d12cc36cc6c386ffd04f7c8f0de6ccb29c5927e`.

	The model weights, index, configuration, tokenizer, chat template, generation
	configuration, and tool-parser files are byte-identical to that upstream
	revision. AutomatosX did not fine-tune, merge, re-quantize, or otherwise alter
	the model artifacts. We add only mirror documentation, a copy of the declared
	Apache 2.0 license, and machine-readable provenance.

	## Model details

	- Base model: [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next)
	- Format: MLX Safetensors for Apple Silicon
	- Architecture: Qwen3NextForCausalLM, mixture of experts
	- Main quantization: 6-bit affine, group size 64
	- Quantization exceptions: router and shared-expert gate tensors remain 8-bit,
	as defined by the unchanged upstream config
	- Layers: 48
	- Experts: 512 total, 10 selected per token
	- Configured context limit: 262,144 tokens
	- Weight shards: 13, totaling 64,749,929,465 bytes
	- Upstream conversion tool: mlx-lm 0.30.5

	## Download

	~~~bash
	hf download AutomatosX/AX-Qwen3-Coder-Next-MLX-6bit \
	--local-dir ./AX-Qwen3-Coder-Next-MLX-6bit
	~~~

	## Use with MLX-LM

	~~~bash
	pip install mlx-lm
	mlx_lm.generate \
	--model AutomatosX/AX-Qwen3-Coder-Next-MLX-6bit \
	--prompt "Write a Python function that merges two sorted lists."
	~~~

	Applications should apply the included chat template for conversational or
	tool-using prompts.

	## Serve with AX Engine

	You can also serve the downloaded model through the OpenAI-compatible API in
	[AX Engine](https://github.com/defai-digital/ax-engine):

	~~~bash
	ax-engine serve ./AX-Qwen3-Coder-Next-MLX-6bit --port 31418
	~~~

	## Mirror policy and provenance

	This release is meant to group a required upstream artifact under the
	AutomatosX catalog, not to claim a new conversion. `UPSTREAM_README.md`
	preserves the original upstream model card. `ax_provenance.json` pins the
	source commit and records SHA-256 values and sizes for every mirrored artifact.

	This is a standard direct-decoding model. It does not contain an MTP head and
	no MTP conversion was applied.

	## License

	The upstream model metadata declares Apache License 2.0. See `LICENSE`, the
	upstream model card, and the base-model card for limitations and responsible-use
	guidance.