Instructions to use AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-MXFP4 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-MXFP4 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-MXFP4")

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-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-Coder-Next-MLX-AXQ-MXFP4"

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-MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-MXFP4 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-MXFP4"

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-MXFP4"
# 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-MXFP4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use AutomatosX/AX-Qwen3-Coder-Next-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-Coder-Next-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-Coder-Next-MLX-AXQ-MXFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AutomatosX/AX-Qwen3-Coder-Next-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-Coder-Next-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-Coder-Next-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-Coder-Next-MLX-AXQ-MXFP4 / README.md

AutomatosX

Bind Coder-Next MXFP4 card to Hub commit b7ceed00

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	---
	license: apache-2.0
	library_name: mlx
	base_model: Qwen/Qwen3-Coder-Next
	base_model_relation: quantized
	pipeline_tag: text-generation
	tags:
	- mlx
	- apple-silicon
	- quantized
	- mixed-precision
	- axquant
	- axq
	- development
	- qwen3-next
	- MXFP4
	- MXFP4
	---

	# AX-Qwen3-Coder-Next-MLX-AXQ-MXFP4 — 4.32 BPW measured main

	An AXQuant (AXQ) mixed-precision MLX checkpoint for Apple Silicon, converted directly from
	the BF16 source model. The language path is quantized under AXQuant protection floors (embeddings, norms, and other protected tensors remain higher precision).

	> Checkpoint Tier 1 certified on `df-macstudio-m2` (2026-08-18) for this exact
	> revision — measured size against a matched uniform baseline, quality retention, and
	> conversion integrity. Tier 1 is a checkpoint claim, not a speed claim: MTP
	> acceleration is not certified; no MTP speedup claim for this checkpoint.
	> See the [checkpoint Tier 1 certificate](https://github.com/defai-digital/axquant/blob/main/docs/certifications/qwen3-coder-next-axq-mxfp4-tier1.md) for the bound evidence and thresholds.


	## Model details

	\| Property \| Value \|
	\| --- \| --- \|
	\| Base model \| [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next/tree/a7fbcb5c0e12d62a448eaa0e260346bf5dcc0feb) \|
	\| Source revision \| `a7fbcb5c0e12d62a448eaa0e260346bf5dcc0feb` \|
	\| Product family \| `qwen3-next` \|
	\| Source architecture \| `Qwen3NextForCausalLM` (mixture of experts (MoE)); text path optimized \|
	\| Main-model parameters \| 79.67B logical parameters \|
	\| Quantizer \| AXQuant `1.9.0` \|
	\| Hub budget class \| `MXFP4` \|
	\| AXQuant base precision class \| `5p6bpw` \|
	\| Planned storage-adjusted BPW \| 5.0590 \|
	\| Measured main-model BPW \| 4.3154 \|
	\| Measured total BPW \| 4.3154 \|
	\| Safetensors weight size \| 42.98 GB \|
	\| Approximate complete download \| 43.08 GB \|
	\| Configured maximum context \| 262,144 tokens; practical limits depend on unified memory \|
	\| Primary MLX runtime \| MLX-LM \|
	\| AX Engine native execution \| Not established; no validated native manifest is included \|
	\| MTP present \| `False` \|
	\| Vision present \| `False` \|
	\| Audio present \| `False` \|

	This repository contains MLX Safetensors. It does not contain PyTorch or GGUF weights.

	## Choosing an AXQ pack

	AXQ names describe a storage-budget product class, not one uniform precision applied to every
	tensor. Protected tensors remain at higher precision, so the exact measured BPW is authoritative.
	In particular, a `6bit`-named mixed plan may retain `4bit` as its base precision while selecting
	6-bit, 8-bit, or BF16 for other tensors to meet an approximately 6-BPW total budget. Protection
	floors can also raise a `4bit`-named pack close to (or above) a `6bit` budget on small or heavily
	protected models. When that collapse happens, AutomatosX does not publish a separate
	misleading `4bit` sibling for that base.


	\| Sibling \| Intended trade-off \|
	\| --- \| --- \|
	\| [4bit sibling](https://huggingface.co/AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-4bit) \| Lower-storage AXQ budget; check its exact BPW \|
	\| [6bit sibling](https://huggingface.co/AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-6bit) \| Higher average precision near the 6-BPW budget \|

	See the [AutomatosX collections](https://huggingface.co/AutomatosX/collections)
	for the family catalog, or the [complete index](https://huggingface.co/collections/AutomatosX/automatosx-mlx-model-catalog).

	## Download

	```bash
	python -m pip install -U huggingface_hub
	hf download AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-MXFP4 --local-dir ./AX-Qwen3-Coder-Next-MLX-AXQ-MXFP4
	```

	Allow at least 43.08 GB of free disk space. Pin the resulting Hub commit in reproducible
	deployments rather than relying indefinitely on `main`.

	## Run with MLX-LM

	```bash
	python -m pip install -U mlx-lm
	mlx_lm.generate \
	--model AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-MXFP4 \
	--prompt "Explain mixed-precision quantization in three sentences." \
	--max-tokens 128 \
	--temp 0.0
	```

	MLX-LM compatibility covers standard text/backbone inference. It may ignore AXQuant runtime
	metadata and optional sidecars (`vision.safetensors`, `mtp.safetensors`); this command therefore
	does not establish MTP acceleration or vision-language quality. The artifact records MLX
	`0.32.0` and MLX-LM `0.31.3` from conversion.

	## AX Engine status

	This package does not include a validated native `model-manifest.json`, so AX Engine execution
	is not established by this release. The AX Engine fields in `axquant_runtime.json` describe the
	intended compatibility contract, not observed runtime evidence. Use the architecture-specific MLX
	runtime path above. The artifact records AX Engine version
	`not recorded`, but version discovery alone is not a runtime check.

	## Quantization layout

	\| Main-weight precision \| Parameters \| Share \|
	\| --- \| ---: \| ---: \|
	\| `4bit` \| 79.00B \| 99.15% \|
	\| `8bit` \| 361.59M \| 0.45% \|
	\| `bf16` \| 312.56M \| 0.39% \|

	- Quantization methods: `affine, bf16`.
	- Group sizes used by quantized assignments: `32, 64`.
	- MTP sidecar: not included.
	- Vision sidecar: not included.
	- Optimization scope: `text-path`.
	- Support tier: `convertible`.

	BF16 sidecars, when present, are included in total download size. Their presence does not by itself
	establish MTP acceleration or vision-language quality.

	## Evidence and validation status

	\| Check \| Status \|
	\| --- \| --- \|
	\| Planning evidence \| `architecture_prior` \|
	\| Calibration \| none; the allocation is based on architecture priors \|
	\| Quantizer execution \| 74125/74125 recorded module conversions succeeded; 0 fallbacks \|
	\| AX Engine native manifest \| not included \|
	\| Quality versus BF16 or uniform baselines \| Not published; no quality-retention claim \|
	\| MTP acceptance and speed \| not certified; no MTP speedup claim for this checkpoint (Qwen3-Coder-Next source declares no MTP; certification is non-MTP direct-decode checkpoint Tier 1 only (qwen3-next-direct track).) \|
	\| AX Engine kernel evidence \| `unmeasured` \|
	\| Vision-language quality \| Not applicable (vision disabled for this pack) \|
	\| Speech-recognition quality \| Not applicable (audio disabled for this pack) \|
	\| Long-context quality \| 262,144-token capacity is config metadata, not a validated claim \|
	\| Release certification \| Checkpoint Tier 1 certified on `df-macstudio-m2` (2026-08-18), Hub commit `b7ceed0036af`; the formal AXQuant M0-M8 release campaign is a separate process and is not implied \|

	## Modalities (capability-gated)

	Text checkpoint Tier 1 does not imply vision or audio quality. `Vision present=true` on a pack is not a quality pass.

	\| Modality \| Claim \| Supported \| Reason \|
	\| --- \| --- \| --- \| --- \|
	\| Vision \| `not-applicable` \| `false` \| vision not supported on this pack \|
	\| Audio \| `not-applicable` \| `false` \| audio not supported on this pack \|

	## Intended use and limitations

	- Intended for local development and evaluation on Apple Silicon with MLX-compatible runtimes.
	- No minimum unified-memory figure is claimed; loadability depends on model size, context length,
	KV-cache policy, runtime buffers, and other processes using unified memory.
	- Architecture-prior allocation is not measured sensitivity. It must not be presented as measured
	model quality.
	- The configured context window can require substantially more memory as the KV cache grows.
	- AX Engine execution is not established because this package has no validated native manifest.

	- Upstream capabilities, limitations, biases, and responsible-use guidance still apply.

	## Provenance and audit files

	- [`axquant_manifest.json`](axquant_manifest.json): package identity, byte accounting, runtime
	contract, software versions, and file checksums.
	- [`axquant_plan.json`](axquant_plan.json): per-tensor precision decisions and planning evidence.
	- [`axquant_quantizer_execution.json`](axquant_quantizer_execution.json): conversion coverage and
	fallback records.
	- [`axquant_runtime.json`](axquant_runtime.json): declared AX Engine and MLX compatibility metadata; runtime checks remain separate evidence.

	All published provenance uses repository-relative paths. Local source paths are stripped before
	publication. The checkpoint was converted from BF16 rather than re-quantized from an OptiQ
	artifact. If an OptiQ repository is published separately, it uses a different quantizer and
	should not be assumed to have identical BPW or quality.

	## License

	The checkpoint follows the upstream model license where applicable (often Apache License 2.0). See
	the [Qwen/Qwen3-Coder-Next model card](https://huggingface.co/Qwen/Qwen3-Coder-Next/tree/a7fbcb5c0e12d62a448eaa0e260346bf5dcc0feb) for license terms, model
	limitations, and responsible-use guidance.