Text Generation
MLX
Safetensors
qwen3_next
apple-silicon
quantized
mixed-precision
axquant
axq
development
qwen3-next
4bit
4-bit precision
conversational
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
- MLX
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
- Pi
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"} ] }'
File size: 7,322 Bytes
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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
- 4bit
- 4-bit
---
# AX-Qwen3-Coder-Next-MLX-AXQ-4bit
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).
> **Development evidence — not a certified AXQuant release.** This package has conversion and
> artifact-integrity records, but it does not publish measured quality, long-context, kernel-speed,
> or MTP-speed evidence. Do not interpret the AXQ product label as a benchmark claim.
## Model details
| Property | Value |
| --- | --- |
| Base model | [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next) |
| Source revision | `unrecorded` |
| Product family | `qwen3-next` |
| Source architecture | `Qwen3NextForCausalLM` (mixture of experts (MoE)); text path optimized |
| Main-model parameters | 79.67B logical parameters |
| Quantizer | AXQuant `1.0.1` |
| Hub budget class | `4bit` |
| AXQuant base precision class | `4bit` |
| Planned storage-adjusted BPW | 15.7300 |
| Measured main-model BPW | 15.7300 |
| Measured total BPW | **15.7300** |
| Safetensors weight size | 156.66 GB |
| Approximate complete download | 156.67 GB |
| Configured maximum context | 262,144 tokens; practical limits depend on unified memory |
| Primary runtime | AX Engine, compatibility level A |
| Compatible runtime | MLX-LM standard text inference, compatibility level B |
| MTP present | `False` |
| Vision sidecar 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.
| 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 a 6-BPW budget |
See the [AutomatosX MLX model catalog](https://huggingface.co/collections/AutomatosX/automatosx-mlx-model-catalog)
for related MLX and OptiQ alternatives.
## Download
```bash
python -m pip install -U huggingface_hub
hf download AutomatosX/AX-Qwen3-Coder-Next-MLX-AXQ-4bit --local-dir ./AX-Qwen3-Coder-Next-MLX-AXQ-4bit
```
Allow at least 156.67 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-4bit \
--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.
## Serve with AX Engine
After installing [AX Engine](https://github.com/defai-digital/ax-engine), download the complete
repository and serve the local directory:
```bash
ax-engine serve ./AX-Qwen3-Coder-Next-MLX-AXQ-4bit --port 31418
```
AX Engine is the authority for the AXQ runtime contract.
This development package does not claim runtime speedups until identical-checkpoint benchmarks are
published. The artifact records AX Engine version `not recorded`. Native
`model-manifest.json` status: not included.
## Quantization layout
| Main-weight precision | Parameters | Share |
| --- | ---: | ---: |
| `4bit` | 1.69B | 2.12% |
| `6bit` | 397,312 | 0.00% |
| `8bit` | 361.50M | 0.45% |
| `bf16` | 77.62B | 97.42% |
- 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 | 397/397 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 measured; no MTP speedup claim |
| AX Engine kernel evidence | `unmeasured` |
| Vision-language quality | Not applicable (no vision sidecar in this package) |
| Long-context quality | 262,144-token capacity is config metadata, not a validated claim |
| Release certification | **Not certified**; formal AXQuant M0-M8 gates are not closed |
## 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.
- 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): AX Engine and MLX-LM compatibility contract.
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. Parallel OptiQ repositories use 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) for license terms, model
limitations, and responsible-use guidance.
|