mlx-community/gemma-4-e4b-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. OptiQ's sensitivity-guided per-layer bit allocation is applied on top of weights that were trained to survive low-bit quantization, and it still beats a uniform 4-bit quant of the same QAT base by +1.19 Capability Score points.
This is a quant of google/gemma-4-E4B-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). Sensitive layers go to 8-bit, robust ones stay at 4-bit.
Quantization details
| Property | Value |
|---|---|
| Base | google/gemma-4-E4B-it-qat-q4_0-unquantized (QAT) |
| Predominant precision | 4-bit |
| Components at 8-bit (sensitive) | 221 |
| Components at 4-bit (robust) | 122 |
| Total quantized components | 343 |
| Achieved bits-per-weight | 5.17 |
| Group size | 64 |
| Reference for sensitivity | uniform-4-bit baseline (bf16 does not fit in RAM at this size) |
| Calibration mix | six-domain mix |
| Vision | bf16 sidecar (optiq_vision.safetensors), image+text via optiq |
| Speculative drafter | google/gemma-4-E4B-it-qat-q4_0-unquantized-assistant via optiq serve --drafter |
Capability Score
Six-metric mean (MMLU, GSM8K, IFEval, BFCL, HumanEval, HashHop), scored against the published uniform 4-bit quant of the same QAT base (mlx-community/gemma-4-E4B-it-qat-4bit). That comparison isolates what the mixed-precision allocation adds, holding the base fixed.
| Benchmark | This model (OptiQ, QAT base) | Uniform-4 (QAT base) | Delta |
|---|---|---|---|
| MMLU (5-shot, 1000) | 57.7% | 57.4% | +0.3 |
| GSM8K (1000) | 80.0% | 79.5% | +0.5 |
| IFEval (full, strict) | 69.1% | 67.8% | +1.3 |
| BFCL-V3 simple (200) | 87.5% | 87.5% | +0.0 |
| HumanEval (pass@1, 164) | 81.7% | 78.7% | +3.0 |
| HashHop (long-context) | 36.0% | 34.0% | +2.0 |
| Capability Score (mean) | 68.67 | 67.48 | +1.19 |
The mixed-precision allocation adds +1.19 points over uniform 4-bit on the QAT base, with the largest gains on HumanEval and the long-context HashHop task. The mixed quant is 5.17 bits-per-weight (about 7.0 GB on disk) versus 4.0 bits-per-weight (about 6.3 GB) for uniform 4-bit: the gain comes from spending the extra budget on the layers that need it.
Usage
mlx-lm loads it directly for text:
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/gemma-4-e4b-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-e4b-it-qat-OptiQ-4bit \
--drafter google/gemma-4-E4B-it-qat-q4_0-unquantized-assistant
The same repo loads text-only under stock mlx-lm and image+text under 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.
Quantize your own
This quant was produced by mlx-optiq. Point it at any Hugging Face model to get the same sensitivity-aware mixed precision:
pip install mlx-optiq
optiq convert <hf-model-id> --target-bpw 5.0 --candidate-bits 4,8
optiq lab # or open the full local workbench: chat, compare, quantize, fine-tune
License
Gemma Terms of Use. Built on google/gemma-4-E4B-it-qat-q4_0-unquantized.