Instructions to use AutomatosX/AX-Qwen3-Coder-Next-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-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-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
- Pi
How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-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-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-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-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-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-4bit
Run Hermes
hermes
- OpenClaw new
How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-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-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-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-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-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-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-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
AX Qwen3 Coder Next MLX 4-bit
Parameter count: approximately 79.67B logical parameters (80B total, 3B active per token).
4-bitis the quantization precision, not a 4B model-size claim.
This is a revision-pinned, transparent mirror of
mlx-community/Qwen3-Coder-Next-4bit
at commit 7b9321eabb85ce79625cac3f61ea691e4ea984b5.
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
- Format: MLX Safetensors for Apple Silicon
- Architecture: Qwen3NextForCausalLM, mixture of experts
- Main quantization: 4-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: 9, totaling 44,844,286,500 bytes
- Upstream conversion tool: mlx-lm 0.30.5
Download
hf download AutomatosX/AX-Qwen3-Coder-Next-MLX-4bit \
--local-dir ./AX-Qwen3-Coder-Next-MLX-4bit
Use with MLX-LM
pip install mlx-lm
mlx_lm.generate \
--model AutomatosX/AX-Qwen3-Coder-Next-MLX-4bit \
--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:
ax-engine serve ./AX-Qwen3-Coder-Next-MLX-4bit --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.
The local Hugging Face cache contained an AX-generated model-manifest.json;
it is not present in the pinned upstream repository and was deliberately not
published here.
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.
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Base model
Qwen/Qwen3-Coder-Next