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Aug 30, 2026
updated
An AXQuant (AXQ) mixed-precision MLX checkpoint for Apple Silicon, converted directly from the BF16 source model. The language path is quantized while the external assistant MTP drafter and vision tower are preserved at BF16 in the checkpoint (or a bound sidecar 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.
| Property | Value |
|---|---|
| Base model | google/gemma-4-31B-it |
| Source revision | 842da3794eaa0b77d5f08bae87a17459d91ff475 |
| Product family | gemma-4 |
| Source architecture | Gemma4ForConditionalGeneration (dense); text path optimized |
| Main-model parameters | 31.27B logical parameters |
| Quantizer | AXQuant 1.9.0 |
| Hub budget class | 6bit |
| AXQuant base precision class | 6bit |
| Planned storage-adjusted BPW | 6.0000 |
| Measured main-model BPW | 6.0000 |
| Measured total BPW, including MTP | 6.0000 |
| Safetensors weight size | 23.45 GB |
| Approximate complete download | 23.49 GB |
| Configured maximum context | 262,144 tokens; practical limits depend on unified memory |
| Primary MLX runtime | MLX-VLM |
| 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.
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.
python -m pip install -U huggingface_hub
hf download AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-MTP --local-dir ./AX-gemma-4-31b-MLX-AXQ-6bit-MTP
Allow at least 23.49 GB of free disk space. Pin the resulting Hub commit in reproducible
deployments rather than relying indefinitely on main.
python -m pip install -U mlx-vlm
python -m mlx_vlm.generate \
--model AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-MTP \
--image ./image.png \
--prompt "Describe this image." \
--max-tokens 128 \
--temperature 0.0
The protected vision tower and AXQ language decoder are loaded together by MLX-VLM. The artifact
records MLX 0.32.2; runtime QA is reported separately from model-quality claims.
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-gemma-4-31b-MLX-AXQ-6bit-MTP --port 31418
AX Engine is the authority for the AXQ runtime contract and paired assistant-MTP bundle.
This development package does not claim runtime speedups until identical-checkpoint benchmarks are
published. The artifact records AX Engine version not recorded. Native
model-manifest.json status: included as model-manifest.json.
This repository uses an external gemma4_assistant drafter under assistant/. It is not an
embedded-head checkpoint, so do not enable Lightning MTP or import it as a Qwen sidecar.
Download the complete repository, add both ./AX-gemma-4-31b-MLX-AXQ-6bit-MTP and ./AX-gemma-4-31b-MLX-AXQ-6bit-MTP/assistant as local oMLX
models, then configure the target model with VLM MTP enabled and select the assistant model.
The equivalent model-setting fields are:
vlm_mtp_enabled: true
vlm_mtp_draft_model: ./AX-gemma-4-31b-MLX-AXQ-6bit-MTP/assistant
vlm_mtp_draft_block_size: 2
The normalized and indexed vision.safetensors layout is loadable by MLX-VLM 0.6.17 or newer.
For gemma4_unified targets, the same sidecar includes the protected vision_embedder,
embed_vision, and embed_audio modules and the output restores the upstream unified config.
Runtime discovery does not establish identical-output, acceptance-rate, or speed certification
for oMLX. Follow the exact checkpoint revision's Tier 2 status.
| Main-weight precision | Parameters | Share |
|---|---|---|
4bit | 16.07B | 51.38% |
6bit | 13.22B | 42.26% |
8bit | 1.41B | 4.51% |
bf16 | 577.08M | 1.85% |
affine, bf16.32, 64.assistant/ (checksum-bound composite).text-path.convertible.BF16 sidecars, when present, are included in total download size. Their presence does not by itself establish MTP acceleration or vision-language quality.
| Check | Status |
|---|---|
| Planning evidence | architecture_prior |
| Calibration | none; the allocation is based on architecture priors |
| Quantizer execution | 411/411 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 | not measured; no MTP 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 | Not certified; formal AXQuant M0-M8 gates are not closed |
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.
Gemma assistant MTP requires an external-drafter runtime. oMLX VLM MTP discovery does not establish exactness or speed certification.
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.
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.
The checkpoint follows the upstream model license where applicable (often Apache License 2.0). See the google/gemma-4-31B-it model card for license terms, model limitations, and responsible-use guidance.
8 commits
0
stars
8
commits
1
linked in READMEs
Aug 30, 2026
updated
An AXQuant (AXQ) mixed-precision MLX checkpoint for Apple Silicon, converted directly from the BF16 source model. The language path is quantized while the external assistant MTP drafter and vision tower are preserved at BF16 in the checkpoint (or a bound sidecar 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.
| Property | Value |
|---|---|
| Base model | google/gemma-4-31B-it |
| Source revision | 842da3794eaa0b77d5f08bae87a17459d91ff475 |
| Product family | gemma-4 |
| Source architecture | Gemma4ForConditionalGeneration (dense); text path optimized |
| Main-model parameters | 31.27B logical parameters |
| Quantizer | AXQuant 1.9.0 |
| Hub budget class | 6bit |
| AXQuant base precision class | 6bit |
| Planned storage-adjusted BPW | 6.0000 |
| Measured main-model BPW | 6.0000 |
| Measured total BPW, including MTP | 6.0000 |
| Safetensors weight size | 23.45 GB |
| Approximate complete download | 23.49 GB |
| Configured maximum context | 262,144 tokens; practical limits depend on unified memory |
| Primary MLX runtime | MLX-VLM |
| 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.
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.
python -m pip install -U huggingface_hub
hf download AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-MTP --local-dir ./AX-gemma-4-31b-MLX-AXQ-6bit-MTP
Allow at least 23.49 GB of free disk space. Pin the resulting Hub commit in reproducible
deployments rather than relying indefinitely on main.
python -m pip install -U mlx-vlm
python -m mlx_vlm.generate \
--model AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-MTP \
--image ./image.png \
--prompt "Describe this image." \
--max-tokens 128 \
--temperature 0.0
The protected vision tower and AXQ language decoder are loaded together by MLX-VLM. The artifact
records MLX 0.32.2; runtime QA is reported separately from model-quality claims.
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-gemma-4-31b-MLX-AXQ-6bit-MTP --port 31418
AX Engine is the authority for the AXQ runtime contract and paired assistant-MTP bundle.
This development package does not claim runtime speedups until identical-checkpoint benchmarks are
published. The artifact records AX Engine version not recorded. Native
model-manifest.json status: included as model-manifest.json.
This repository uses an external gemma4_assistant drafter under assistant/. It is not an
embedded-head checkpoint, so do not enable Lightning MTP or import it as a Qwen sidecar.
Download the complete repository, add both ./AX-gemma-4-31b-MLX-AXQ-6bit-MTP and ./AX-gemma-4-31b-MLX-AXQ-6bit-MTP/assistant as local oMLX
models, then configure the target model with VLM MTP enabled and select the assistant model.
The equivalent model-setting fields are:
vlm_mtp_enabled: true
vlm_mtp_draft_model: ./AX-gemma-4-31b-MLX-AXQ-6bit-MTP/assistant
vlm_mtp_draft_block_size: 2
The normalized and indexed vision.safetensors layout is loadable by MLX-VLM 0.6.17 or newer.
For gemma4_unified targets, the same sidecar includes the protected vision_embedder,
embed_vision, and embed_audio modules and the output restores the upstream unified config.
Runtime discovery does not establish identical-output, acceptance-rate, or speed certification
for oMLX. Follow the exact checkpoint revision's Tier 2 status.
| Main-weight precision | Parameters | Share |
|---|---|---|
4bit | 16.07B | 51.38% |
6bit | 13.22B | 42.26% |
8bit | 1.41B | 4.51% |
bf16 | 577.08M | 1.85% |
affine, bf16.32, 64.assistant/ (checksum-bound composite).text-path.convertible.BF16 sidecars, when present, are included in total download size. Their presence does not by itself establish MTP acceleration or vision-language quality.
| Check | Status |
|---|---|
| Planning evidence | architecture_prior |
| Calibration | none; the allocation is based on architecture priors |
| Quantizer execution | 411/411 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 | not measured; no MTP 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 | Not certified; formal AXQuant M0-M8 gates are not closed |
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.
Gemma assistant MTP requires an external-drafter runtime. oMLX VLM MTP discovery does not establish exactness or speed certification.
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.
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.
The checkpoint follows the upstream model license where applicable (often Apache License 2.0). See the google/gemma-4-31B-it model card for license terms, model limitations, and responsible-use guidance.
8 commits