AXQuant

AXQuant is the offline precision and evidence control plane for Apple Silicon. It allocates per-tensor precision under explicit policy, materializes checkpoints through public MLX backends, and binds source identity, plan, output bytes, runtime evidence, and — when certified — permitted claims into a checksum-verifiable artifact graph. AX Engine is the primary inference runtime; performance claims require bound measurements.

In short: MLX encodes, AX Engine runs, and AXQuant decides the precision mix — then proves what the result may claim. It does not train the source model or add new capabilities.

Status: toolkit 2.1.0, packaging classifier Alpha. The certified PyPI pin stays axquant[mlx]==1.8.1. The 2.0 line opens the experimental native CUDA NVFP4 backend — RTN NVFP4 W4A16 exports plus an opt-in calibrated W4A4 path — alongside the MLX planning line and its joint weight/KV diagnostics (diagnose-joint, plan-joint) as development evidence. CUDA runtime and quality certification remain open. All per-pack certificate records were withdrawn on 2026-10-03 pending re-certification, so there is currently no certified pack revision on the live catalog; the certification index is the source of truth for what is certified today.

Version 2.1 adds explicit oMLX/MTPLX runtime exports and manifest-bound compatibility checks for future MLX conversions. Unknown peer profiles fail closed; static format checks do not establish generation or certification. Install this development toolkit with python -m pip install 'axquant[mlx]==2.1.0' when using these commands.

Install from PyPI, then convert. You do not need to clone this repository.

Who it is for

• You ship or evaluate quantized models on Apple Silicon and want mixed precision with protection floors, not blind uniform quantization.
• You need reproducible artifacts: pinned source, exact plan, measured BPW, checksums, and runtime metadata in every output.
• You publish packs and want fail-closed gates between a development checkpoint and a public quality or speed claim.

Not for: training or fine-tuning, non-Apple platforms, GGUF workflows, or any model family below the convertible support tier.

How it compares

Design beliefs

1. Precision allocation, not codec invention. AXQuant chooses which tensors deserve which precision; public MLX backends do the encoding.
2. Floors are constraints, not suggestions. Sensitive components are never sacrificed to hit a pretty BPW number; infeasible budgets adjust or refuse, with a record.
3. Prior, measurement, and certification are different things. Architecture priors suit fast development; calibrated measurement supports allocation; only complete quality and hardware evidence supports a public claim.
4. Success must be complete. Every module a plan claims to quantize must actually be processed by the backend — no silent fallback.
5. Claims follow artifact identity. Quality, size, and speed claims belong to one source revision, one plan, one set of bytes, and one runtime and profile — never to a family by association.
6. Certification aims at reproducibility, not universality. A single factory host class keeps comparisons honest; checkpoint (Tier 1) and MTP acceleration (Tier 2) claims stay separate.

Install

Apple Silicon Mac and Python 3.11+. Conversion needs the MLX extra; that stack only runs on arm64 macOS.

Always install into a virtual environment. A bare system-wide install fails with externally-managed-environment; do not pass --break-system-packages. Copy the block as a whole (plain ASCII quotes; single-quote the extra so zsh does not treat [mlx] as a glob):

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install 'axquant[mlx]==1.8.1'
axquant --help

Wheels and checksums also ship on GitHub Releases.

Convert

Point quantize at a local BF16 Safetensors directory. --target-bpw defaults to 4.8. The result is a development checkpoint — good for trying the model locally, not a public quality or speed claim.

axquant quantize /path/to/model-bf16

# Choose the bit budget and output folder
axquant quantize /path/to/model-bf16 --target-bpw 4.8 --output ./AXQuant-output

# Hugging Face id (downloads only when you pass --allow-download)
axquant quantize Qwen/Qwen3.6-27B --allow-download --revision COMMIT_SHA

# Confirm the family is convertible before spending the convert
axquant inspect --model /path/to/model-bf16 --output inventory.json

Load the output with MLX-LM:

python -m pip install -U mlx-lm
mlx_lm.generate --model ./AXQuant-output --prompt "Hello" --max-tokens 64 --temp 0.0

Prefer a ready-made pack? The live catalog is on AutomatosX on Hugging Face — packs, model cards, and certification status. This README deliberately does not mirror the pack list.

Conversion needs enough unified memory for the source model. Not every Hugging Face model is convertible: run axquant support-matrix and axquant support-policy for the registry-derived tier (certified / convertible / inspect-only) before beginning work.

How it works

BF16 Safetensors checkpoint (pin a revision for measured/release evidence)
                         |
                         v
        inspect -> plan -> convert -> runtime-check / validate
           |                |
           |                +-- mixed-precision MLX checkpoint
           |                    + plan, manifest, checksums, provenance
           |                    + AX Engine / MLX runtime metadata
           +-- inventory with protection boundaries

Most users only need Install and Convert. The staged journey (inspect → plan → convert → validate → publish) is for measured releases and public claims; see the certification operator guide.

Input and output

Input: an unquantized Safetensors checkpoint of a family at the convertible tier or above, converted through the promoted public backend (MLX-LM for text, MLX-VLM / MLX-Audio with BF16-protected modality towers for vision and audio). A pinned source revision is mandatory for measured sensitivity and release evidence; an unpinned local source is permitted only for development.

Output: a portable MLX model directory with mixed-precision weights, the exact quantization plan, a checksum-bound artifact manifest recording authoritative parameters and measured BPW, runtime metadata for the supported runtimes, and byte-preserved MTP / modality sidecars unless an explicit validated transform is selected. The language-model output stays usable as a standard MLX checkpoint; AX Engine reads the extra metadata.

CLI workflow

Run axquant COMMAND --help for the full options of any command. The main entry points are quantize (one-command development conversion), inspect, analyze, plan, convert, and verify-cert (offline certificate check). The complete command table lives in the CLI reference.

Certification

AXQuant separates checkpoint claims from acceleration claims:

• Tier 1 (checkpoint): size, matched quality, conversion integrity, and standard-runtime compatibility for one exact revision.
• Tier 2 (MTP, scoped): greedy exactness plus token-weighted ≥1.20× and prompt-median ≥1.10× decode speedup on named authorizing workloads — measured evidence for that pack, engine build, and host only.

Conversion, Tier 1, and Tier 2 evidence are all produced on one factory host class: Mac Studio, M2 Ultra, 192 GB unified memory. Records name the hardware spec, never a machine identity.

The rules are frozen in the Certification Spec v1.0; the verdicts live in docs/certifications/, including the full list of every record. A certificate never promotes a family by association.

Documentation

Index: docs/README.md.

Product requirements, the architecture decision register, technical specifications, and the independent-implementation policy are maintained internally and are not published in this repository.

Development

CI splits surfaces on purpose: Ubuntu runs the non-MLX gate, macOS runs the MLX suite. Prefer the local CI mirror:

./scripts/ci-local.sh

.venv/bin/pytest
.venv/bin/ruff check .
.venv/bin/ruff format --check .
.venv/bin/mypy src

Tests use small synthetic Safetensors fixtures and do not require real model weights. See CONTRIBUTING.md.

Contributing

Contributions are warmly welcome: bug fixes, documentation, tests, usability improvements, runtime compatibility, architecture adapters, or reproducible quantization research. Fork the repository and send a pull request; for a substantial design change or new model family, open a GitHub issue first.

License

AXQuant is released under the MIT License. Dependencies, model checkpoints, calibration datasets, and external tools retain their own licenses.