mlx-community/gemma-4-26B-A4B-it-qat-OptiQ-4bit

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A 4-bit mixed-precision MLX quant produced by mlx-optiq, built on Google's quantization-aware-trained (QAT) Gemma-4 base. This is the sparse-MoE member of the family: 26B total parameters with 128 routed experts (about 4B active per token). OptiQ's sensitivity-guided per-layer bit allocation is applied on top of weights already trained to survive low-bit quantization, and it still beats a uniform 4-bit quant of the same QAT base on the Capability Score.

This is a quant of google/gemma-4-26B-A4B-it-qat-q4_0-unquantized. Per-layer bit-widths come from a KL-divergence sensitivity pass on a six-domain calibration mix (prose, reasoning, code, agent, tool-call, constraint-bearing instructions). On this MoE the sensitivity pass identifies the routed experts as where precision matters most, so the allocation puts 8-bit on the experts that need it (42 of 90 expert tensors) plus the sensitive attention and router layers, and keeps the robust ones at 4-bit.

Quantization details

Property	Value
Base	google/gemma-4-26B-A4B-it-qat-q4_0-unquantized (QAT, MoE)
Architecture	gemma4 · 128 experts, ~4B active per token
Predominant precision	4-bit
Components at 8-bit (sensitive)	275
Components at 4-bit (robust)	50
Total quantized components	325
Expert tensors at 8-bit	42 / 90
Achieved bits-per-weight	6.01
Group size	64
Reference for sensitivity	uniform 4-bit (streamed)
Calibration mix	six-domain mix
Vision	bf16 sidecar (optiq_vision.safetensors), image+text via optiq
Speculative drafter	google/gemma-4-26B-A4B-it-qat-q4_0-unquantized-assistant via optiq serve --drafter

Capability Score

Six-metric mean (MMLU, GSM8K, IFEval, BFCL, HumanEval, HashHop), scored against a uniform 4-bit quant of the same QAT base. That comparison isolates what the mixed-precision allocation adds, holding the base fixed.

Benchmark	Uniform-4 (QAT base)	This model (OptiQ, QAT base)	Delta
MMLU (5-shot, 1000)	64.3%	65.9%	+1.6
GSM8K (1000)	89.2%	90.3%	+1.1
IFEval (full, strict)	73.6%	74.1%	+0.5
BFCL-V3 simple (200)	74.5%	73.5%	-1.0
HumanEval (pass@1, 164)	90.2%	89.0%	-1.2
HashHop (long-context)	35.0%	35.0%	0.0
Capability Score (mean)	71.13	71.32	+0.19

The gain concentrates in the reasoning-heavy benchmarks (MMLU +1.6, GSM8K +1.1, IFEval +0.5), which is where the routed experts carry the most signal. On this sparse-MoE base, where most of the parameters live in 128 experts, the precision has to reach the experts to move capability, and the per-layer allocation puts it there. The mixed quant is 6.01 bits-per-weight (about 19 GB on disk) versus 4.0 bits-per-weight (about 14.5 GB) for uniform 4-bit.

Usage

This is a Gemma-4 MoE (model_type: gemma4, gemma4_text experts), so it needs mlx-lm from main and import optiq (the MoE text tower is not in the 0.31.3 PyPI release; the main build also reports 0.31.3, so install from git, not a version pin):

pip install -U mlx-optiq "mlx-lm @ git+https://github.com/ml-explore/mlx-lm.git"

import optiq  # registers the OptiQ model paths
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/gemma-4-26B-A4B-it-qat-OptiQ-4bit")
print(generate(model, tokenizer, "Explain mixed-precision quantization.", max_tokens=256))

Image+text input and the speculative drafter run through mlx-optiq:

pip install mlx-optiq
optiq serve --model mlx-community/gemma-4-26B-A4B-it-qat-OptiQ-4bit \
            --drafter google/gemma-4-26B-A4B-it-qat-q4_0-unquantized-assistant

The language and image+text paths both run through optiq. The bf16 vision tower rides in optiq_vision.safetensors, which mlx-lm ignores (it globs model*.safetensors), so both paths work from one artifact.