Instructions to use AutomatosX/AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AutomatosX/AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP 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-Qwen3.5-9B-MLX-AXQ-6bit-MTP")

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-Qwen3.5-9B-MLX-AXQ-6bit-MTP 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-Qwen3.5-9B-MLX-AXQ-6bit-MTP"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-Qwen3.5-9B-MLX-AXQ-6bit-MTP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use AutomatosX/AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP 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-Qwen3.5-9B-MLX-AXQ-6bit-MTP"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "AutomatosX/AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP"
# 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-Qwen3.5-9B-MLX-AXQ-6bit-MTP",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use AutomatosX/AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP 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-Qwen3.5-9B-MLX-AXQ-6bit-MTP"

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-Qwen3.5-9B-MLX-AXQ-6bit-MTP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AutomatosX/AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP 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-Qwen3.5-9B-MLX-AXQ-6bit-MTP"

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-Qwen3.5-9B-MLX-AXQ-6bit-MTP" \
  --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"

Why only 6bit? Protection floors (MTP + vision BF16, etc.) force both the 4.8 and ~6.0 BPW budgets to **6.97 BPW** with identical weights. There is no smaller AXQ-4bit sibling for Qwen3.5-9B — use this pack only.

AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP

An AXQuant (AXQ) mixed-precision MLX checkpoint for Apple Silicon, converted directly from the BF16 source model. The language path is quantized while the multi-token-prediction (MTP) head and vision tower are preserved as BF16 sidecars when present.

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.

Stable-name v2. main serves the audited v2 artifact for backward compatibility. The same revision is tagged v2; the replaced artifact remains recoverable at legacy-pre-v2.

Model details

Property	Value
Base model	Qwen/Qwen3.5-9B
Source revision	`c202236235762e1c871ad0ccb60c8ee5ba337b9a`
Product family	`qwen3.5`
Source architecture	`Qwen3_5ForConditionalGeneration` (dense); text path optimized
Main-model parameters	9.41B logical parameters
Quantizer	AXQuant `1.2.0`
Hub budget class	`6bit`
Artifact edition	`v2`
AXQuant base precision class	`7p0bpw`
Planned storage-adjusted BPW	6.9700
Measured main-model BPW	6.7367
Measured total BPW, including MTP	6.9701
Safetensors weight size	8.41 GB
Approximate complete download	8.43 GB
Configured maximum context	262,144 tokens; practical limits depend on unified memory
MLX-LM compatibility	Standard text inference, compatibility level B
AX Engine native execution	Not established; no validated native manifest is included
MTP present	`True`
Vision sidecar present	`True`

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

Why there is no AXQ-4bit pack

On this base, AXQuant protection floors (BF16 MTP sidecar, BF16 vision tower, and other protected tensors) raise both the low-memory (~4.8 BPW) and 6 BPW planning budgets to the same effective target of about 6.97 BPW.

The former …-AXQ-4bit-MTP sibling therefore contained the same mixed plan and the same weight files as this 6bit pack (~8.4 GB download). Publishing both would look like a size trade-off when none exists. AutomatosX keeps only this repository.


Measured main-model BPW	~6.74
Measured total BPW (incl. MTP)	~6.97
Why not 4bit	Floor-collapsed; identical to this pack

Choosing an AXQ pack

AXQ 4bit / 6bit names are storage-budget product classes, not a promise that every tensor uses that width. On this base there is no distinct 4bit Hub pack — see Why there is no AXQ-4bit pack above.

Sibling	Intended trade-off
(none published)	This 6bit pack is the only public AXQ checkpoint for this base.

See the AutomatosX MLX model catalog for related MLX and OptiQ alternatives.

Download

python -m pip install -U huggingface_hub
hf download AutomatosX/AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP --local-dir ./AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP

Allow at least 8.43 GB of free disk space. Pin the resulting Hub commit in reproducible deployments rather than relying indefinitely on main.

Run with MLX-LM

python -m pip install -U mlx-lm
mlx_lm.generate \
  --model AutomatosX/AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP \
  --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 MLX-LM path above for standard text/backbone inference. The artifact records AX Engine version not recorded, but version discovery alone is not a runtime check.

Use the packaged Qwen MTP head with oMLX or MTPLX

Download the complete repository to a writable local directory. In oMLX 0.6.3rc2 or newer, add that directory, open Model Settings, choose Import MTP side-car, and then enable Lightning MTP. The import changes only the local copy so the sidecar tensors become visible through the checkpoint index. This is a text-path compatibility result; it does not certify VLM loading or vision quality.

MTPLX can consume the packaged sidecar directly:

mtplx quickstart \
  --model ./AX-Qwen3.5-9B-MLX-AXQ-6bit-MTP \
  --profile stable \
  --depth 1 \
  --reasoning off

mtplx_runtime.json declares the canonical qwen3-next-mtp execution contract. This enables strict runtime discovery; it does not establish oMLX or MTPLX exactness or speed certification.

Quantization layout

Main-weight precision	Parameters	Share
`4bit`	6.92B	71.67%
`8bit`	1.02B	10.54%
`bf16`	1.72B	17.79%

Quantization methods: affine, bf16.
Group sizes used by quantized assignments: 32, 64.
MTP sidecar: 15 tensors, 243.29M parameters, 0.49 GB, BF16.
Vision sidecar: 333 tensors, 456.01M parameters, 0.91 GB, BF16.
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	249/249 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 evaluated or claimed; vision tensors are preserved at BF16
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.
MTP requires a sidecar-aware runtime. oMLX/MTPLX discovery compatibility does not establish exactness or speed certification for those runtimes.
Vision weights are byte-preserved at BF16, but this release does not claim validated VLM 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: package identity, byte accounting, runtime contract, software versions, and file checksums.
axquant_plan.json: per-tensor precision decisions and planning evidence.
axquant_quantizer_execution.json: conversion coverage and fallback records.
axquant_runtime.json: declared AX Engine and MLX-LM compatibility metadata; runtime checks remain separate evidence.
axquant_mtp_sidecar_manifest.json: MTP tensor provenance.
axquant_vision_sidecar_manifest.json: protected vision tensor provenance.

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/Qwen3.5-9B model card for license terms, model limitations, and responsible-use guidance.