AX-Qwen3.6-27B-MLX-AXQ-6bit

An AXQuant (AXQ) mixed-precision MLX checkpoint for Apple Silicon, converted directly from the BF16 source model. The language path is quantized while the vision tower are preserved at BF16 in the checkpoint (or a bound sidecar when present).

Checkpoint Tier 1 certified on df-macbookpro-m5 (2026-08-14) 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 for the bound evidence and thresholds.

Model details

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.

See the AutomatosX collections for the family catalog, or the complete index.

Download

python -m pip install -U huggingface_hub
hf download AutomatosX/AX-Qwen3.6-27B-MLX-AXQ-6bit --local-dir ./AX-Qwen3.6-27B-MLX-AXQ-6bit

Allow at least 19.88 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-Qwen3.6-27B-MLX-AXQ-6bit \
  --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, download the complete repository (see AXQuant for conversion, certificates, and model-card tooling) and serve the local directory:

ax-engine serve ./AX-Qwen3.6-27B-MLX-AXQ-6bit --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 6.13.5. Native model-manifest.json status: included as model-manifest.json.

Quantization layout

• Quantization methods: affine, bf16, dwq.
• Group sizes used by quantized assignments: 64.
• MTP sidecar: not included.
• Vision sidecar: 333 tensors, 460.73M parameters, 0.92 GB, BF16.
• Vision weights: protected BF16 sidecar.
• 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

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.

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.

• Vision weights are 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.

• 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 compatibility metadata; runtime checks remain separate evidence.
• axquant_vision_sidecar_manifest.json: protected vision tensor provenance.
• model-manifest.json: AX Engine native tensor manifest.

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/Qwen3.6-27B model card for license terms, model limitations, and responsible-use guidance.