AX-Qwen3.8-27B-MLX-AXQ-4bit-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 at BF16 in the checkpoint (or a bound sidecar when present).

Checkpoint Tier 1 certified on df-macbookpro-m3 (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 certified for the certificate's authorizing profiles only; outside that scope there is no speedup claim. See the checkpoint Tier 1 certificate and Tier 2 MTP acceleration certificate for the bound evidence and thresholds.

Model details

Property	Value
Base model	Qwen/Qwen3.8-27B
Source revision	1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0
Product family	qwen3.8
Source architecture	Qwen3_5ForConditionalGeneration (dense); text path optimized
Main-model parameters	27.36B logical parameters
Quantizer	AXQuant 1.6.2
Hub budget class	4bit
AXQuant base precision class	5p5bpw
Planned storage-adjusted BPW	5.2337
Measured main-model BPW	5.0667
Measured total BPW, including MTP	5.2338
Safetensors weight size	18.18 GB
Approximate complete download	18.20 GB
Configured maximum context	262,144 tokens; practical limits depend on unified memory
Primary MLX runtime	MLX-LM
AX Engine native execution	Native manifest included; execution still requires a runtime check
MTP present	True
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

python -m pip install -U huggingface_hub
hf download AutomatosX/AX-Qwen3.8-27B-MLX-AXQ-4bit-MTP --local-dir ./AX-Qwen3.8-27B-MLX-AXQ-4bit-MTP

Allow at least 18.20 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-Qwen3.8-27B-MLX-AXQ-4bit-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.

Serve with AX Engine and MTP

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-Qwen3.8-27B-MLX-AXQ-4bit-MTP --port 31418

AX Engine is the authority for the AXQ runtime contract and native MTP sidecar. This development package does not claim runtime speedups until identical-checkpoint benchmarks are published. The artifact records AX Engine version 6.16.1. Native model-manifest.json status: included as model-manifest.json.

Quantization layout

Main-weight precision	Parameters	Share
4bit	24.35B	87.65%
8bit	2.54B	9.15%
bf16	888.07M	3.20%

• Quantization methods: affine, bf16.
• Group sizes used by quantized assignments: 64.
• MTP sidecar: 15 tensors, 424.70M parameters, 0.85 GB, BF16.
• 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	498/498 recorded module conversions succeeded; 0 fallbacks
AX Engine native manifest	included as model-manifest.json
Quality versus BF16 or uniform baselines	Not published; no quality-retention claim
MTP acceptance and speed	certified for the certificate's authorizing profiles only; outside that scope there is no speedup claim
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-m3 (2026-08-14), Hub commit 32f448461caf; 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-macstudio-m2 (see evidence). Text Tier 1 unchanged. Evidence: /Users/akiralam/code/axquant/docs/certifications/evidence/modality-recert-macstudio-m2/results/qwen38-27b-axq4-mtp.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.

• MTP may be ignored outside AX Engine and its speedup is unmeasured for this exact checkpoint.

• 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_mtp_sidecar_manifest.json: MTP tensor provenance.
• 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/Qwen3.8-27B model card for license terms, model limitations, and responsible-use guidance.