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

Property	Value
Base model	Qwen/Qwen3.6-27B
Source revision	`6a9e13bd6fc8f0983b9b99948120bc37f49c13e9`
Product family	`qwen3.6`
Source architecture	`Qwen3_5ForConditionalGeneration` (dense); text path optimized
Main-model parameters	27.36B logical parameters
Quantizer	AXQuant `1.2.0`
Hub budget class	`4bit`
AXQuant base precision class	`5p6bpw`
Planned storage-adjusted BPW	5.3355
Measured main-model BPW	5.4183
Measured total BPW	5.3355
Safetensors weight size	18.53 GB
Approximate complete download	18.55 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	`True`
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	Lower-storage AXQ budget; check its exact BPW
6bit sibling	Higher average precision near the 6-BPW budget

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

Download

bash

python -m pip install -U huggingface_hub
hf download AutomatosX/AX-Qwen3.6-27B-MLX-AXQ-4bit --local-dir ./AX-Qwen3.6-27B-MLX-AXQ-4bit

Allow at least 18.55 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-Qwen3.6-27B-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.

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`	24.35B	87.65%
`8bit`	1.27B	4.58%
`bf16`	2.16B	7.77%

Quantization methods: affine, bf16.
Group sizes used by quantized assignments: 32, 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

Check	Status
Planning evidence	`architecture_prior`
Calibration	none; the allocation is based on architecture priors
Quantizer execution	497/497 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 (No MTP weights; checkpoint Tier 1 is non-MTP direct-decode only.)
AX Engine kernel evidence	`unmeasured`
Vision-language quality	Not evaluated or claimed; vision tensors are preserved at BF16
Speech-recognition quality	Not applicable
Long-context quality	262,144-token capacity is config metadata, not a validated claim
Release certification	Checkpoint Tier 1 certified on `df-macbookpro-m5` (2026-08-14), Hub commit `c71547e6ef92`; 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	`present-not-certified`	`true`	vision present sidecar=['vision.safetensors'] keys=['model.visual']; mlx-vlm smoke failed on df-macbookpro-m3 (Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in _run_code
File "/Users/akiralam/code/axquant/.venv/lib/python3.12/site-packages/mlx_vlm/generate/main.py). Text Tier 1 unchanged. Evidence: /Users/akiralam/code/axquant/docs/certifications/evidence/modality-recert-capability-gated/results/qwen36-27b-axq4-nomtp-tier1.json
Audio	`not-applicable`	`false`	audio not supported (no tower config and no sidecar weights)

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.
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 compatibility metadata; runtime checks remain separate evidence.
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. 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.