Text Generation
MLX
Safetensors
qwen3_next
apple-silicon
qwen3-next
coding
mixture-of-experts
quantized
4-bit precision
mirror
automatosx
ax-engine
conversational
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"} ] }'
File size: 3,025 Bytes
c341ba5 5827785 c341ba5 1083e87 c341ba5 5827785 c341ba5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | ---
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
- 4-bit
- mirror
- automatosx
- ax-engine
---
# AX Qwen3 Coder Next MLX 4-bit
> **Parameter count:** approximately 79.67B logical parameters (80B total,
> 3B active per token). `4-bit` is the quantization precision, not a 4B
> model-size claim.
This is a revision-pinned, transparent mirror of
[mlx-community/Qwen3-Coder-Next-4bit](https://huggingface.co/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](https://huggingface.co/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
~~~bash
hf download AutomatosX/AX-Qwen3-Coder-Next-MLX-4bit \
--local-dir ./AX-Qwen3-Coder-Next-MLX-4bit
~~~
## Use with MLX-LM
~~~bash
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](https://github.com/defai-digital/ax-engine):
~~~bash
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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