A quantized build of google/gemma-4-31B-it produced by baa.ai. Retains the full vision tower, unlike other pre-quantized MLX variants of this model.

Property	Value
Size on disk	19.49 GB
Format	MLX
Base model	google/gemma-4-31B-it
Vision tower	Retained

Usage

python

from mlx_vlm import load, generate

model, processor = load("baa-ai/Gemma-4-31B-it-RAM-3bit-MLX")
prompt = processor.tokenizer.apply_chat_template(
    [{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}],
    add_generation_prompt=True, tokenize=False,
)
result = generate(model, processor, prompt, max_tokens=512, verbose=True)
print(result.text)

Benchmark results

Measured on vanilla MMLU (500 questions) and MathVision MCQ (20 questions).
Unsloth Gemma 4 MLX variants strip the vision tower — they cannot process images.

This model vs Unsloth Gemma-4-31B 3bit (500-question MMLU):

RAM: 89.2% · Unsloth: 75.6% · Gap: +13.6 pp
Size: Unsloth 19.52 GB → RAM 19.49 GB (30 MB smaller, vision retained)
MathVision: RAM 50.0% · Unsloth: N/A (no vision)

Full results table — all 31B variants

80-question run

Bits	Unsloth MMLU	RAM MMLU	GAP	RAM Vision
8bit	70.7%	90.0%	+19.3	55.0%
4bit	73.8%	86.2%	+12.4	60.0%
3bit	72.5%	86.2%	+13.7	50.0%

500-question run

Bits	Unsloth MMLU	RAM MMLU	GAP
8bit	71.8%	90.4%	+18.6
4bit	71.8%	89.0%	+17.2
3bit	75.6%	89.2%	+13.6

License

Inherited from the upstream Gemma license.

baa.ai

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