mlx-community/gemma-4-12B-it-OptiQ-4bit

Built with mlx-optiq, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. Try the Lab · All OptiQ quants · Docs

A 4-bit mixed-precision MLX quant produced by mlx-optiq, the sensitivity-aware quantization toolkit for Apple Silicon. Scores +6.40 over stock uniform 4-bit on the six-metric Capability Score, the second-largest mixed-precision gain in the Gemma-4 lineup.

A 4-bit mixed-precision MLX quant of google/gemma-4-12B-it, the unified (text+vision+audio) Gemma-4. This artifact is the text-inference path: the language tower is quantized and the vision/audio towers are dropped during conversion. 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

We follow the same naming convention llama.cpp uses for Q4_K_M and similar mixed-precision quants: the "4-bit" label is for the predominant precision, not the weighted average. The mixed allocation is what lets this build beat stock uniform 4-bit on the Capability Score at a comparable disk size.

Usage

This is the unified Gemma-4 12B (model_type: gemma4_unified), so it needs mlx-lm from main and import optiq to register the model type. The unified text tower is not in the 0.31.3 PyPI release of mlx-lm, and the main build also reports version 0.31.3, so install from git rather than pinning a version:

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

import optiq  # registers the gemma4_unified model type
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/gemma-4-12B-it-OptiQ-4bit")
response = generate(
    model, tokenizer,
    prompt="Explain quantum computing in simple terms.",
    max_tokens=200,
)

Gemma-4-12B is a reasoning model with a thinking channel. For direct (non-thinking) answers on tasks like extraction or classification, pass chat_template_kwargs={"enable_thinking": False} when applying the chat template.

For more (mixed-precision KV-cache serving, sensitivity-aware LoRA fine-tuning, OpenAI + Anthropic-compatible inference server, hot-swap mounted adapters, sandboxed Python execution for agent workflows), install mlx-optiq:

pip install mlx-optiq

See the Gemma-4 family guide on mlx-optiq.com for sampling defaults, training recipes, and family-specific caveats.

Benchmarks

Six-metric Capability Score (mean of MMLU + GSM8K + IFEval + BFCL + HumanEval + HashHop). Apples-to-apples comparison against stock uniform 4-bit, both at the full sample counts:

Every metric gets one equal vote. Disk size is reported next to the score as an honest second axis instead of being folded into the score. BFCL-V3 is within its ±6.3pp confidence interval (a tie); the other five benchmarks all improve, with the largest gains on long-context retrieval and code. MMLU is scored cloze-style (answer-letter log-likelihood), which underscores reasoning models that prefer to think before answering; the strong GSM8K and HumanEval results are the better quality signal. See the eval-framework writeup for the full methodology.

Links

• Project website: mlx-optiq.com
• Gemma-4 family guide: mlx-optiq.com/docs/gemma-4
• PyPI: pypi.org/project/mlx-optiq
• Calibration mix: mlx-optiq.com/blog/calibration-mix
• Eval framework: mlx-optiq.com/blog/eval-framework
• Base model: google/gemma-4-12B-it

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   # full local workbench: chat, compare, quantize, fine-tune

License

Gemma license (inherits from base model). See https://ai.google.dev/gemma/terms for the terms of use.