⚠️ Existing MLX quantized Gemma 4 models (mlx-community, unsloth) produce garbage output due to quantizing PLE (Per-Layer Embedding) layers. This repo provides working quantized weights. See Why below.

Other Precisions

4bit ← you are here
8bit
bf16

All Gemma 4 MLX Models

Model	Params	Precision	Size	Audio
gemma-4-e2b-it-MLX-4bit	2.3B	4bit	7.1 GB	✅
gemma-4-e2b-it-MLX-8bit	2.3B	8bit	8.5 GB	✅
gemma-4-e2b-it-MLX-bf16	2.3B	bf16	9.6 GB	✅
gemma-4-e4b-it-MLX-4bit	4.5B	4bit	10.3 GB	✅
gemma-4-e4b-it-MLX-8bit	4.5B	8bit	12.3 GB	✅
gemma-4-e4b-it-MLX-bf16	4.5B	bf16	16.0 GB	✅
gemma-4-26b-a4b-it-MLX-4bit	26B MoE	4bit	16.4 GB	—
gemma-4-26b-a4b-it-MLX-8bit	26B MoE	8bit	28.6 GB	—
gemma-4-26b-a4b-it-MLX-bf16	26B MoE	bf16	51.6 GB	—
gemma-4-31b-it-MLX-4bit	31B dense	4bit	20.4 GB	—
gemma-4-31b-it-MLX-8bit	31B dense	8bit	35.1 GB	—
gemma-4-31b-it-MLX-bf16	31B dense	bf16	62.5 GB	—

Quantization Details

Bits: 4
Group size: 64
Mode: affine
Strategy: PLE-safe — only large nn.Linear and SwitchLinear (MoE) layers are quantized. All PLE/ScaledLinear/vision/audio layers stay in bf16.

Why PLE-Safe?

Gemma 4 uses a novel PLE (Per-Layer Embeddings) architecture with ScaledLinear layers that multiply outputs by a learned scalar. Standard quantization introduces rounding error in these layers, and the scalar amplifies it — producing ionoxffionoxff... garbage.

Our fix: Only quantize the large decoder nn.Linear and SwitchLinear (MoE expert) layers. Everything else stays bf16:

Quantized (4bit)	Kept in bf16
Attention projections (q/k/v/o_proj)	ScaledEmbedding (embed_tokens)
MLP layers (gate/up/down_proj)	ScaledLinear (PLE pathway)
MoE expert layers (SwitchLinear)	Per-layer embeddings (per_layer_*)
	Vision encoder
	All norms and scalars

Usage

Prerequisite: Apply the ScaledLinear fix to mlx-vlm (required until PR merged upstream):

bash

pip install mlx-vlm

# Apply fix
git clone https://github.com/FakeRocket543/mlx-gemma4.git
cp mlx-gemma4/mlx_vlm_patches/models/gemma4/language.py \
   $(python -c "import mlx_vlm; print(mlx_vlm.__path__[0])")/models/gemma4/

Important: You must manually apply the chat template. mlx_vlm.generate() does not do this automatically for Gemma 4.

Vision

python

from mlx_vlm import load, generate

model, processor = load("FakeRockert543/gemma-4-31b-it-MLX-4bit")
tokenizer = processor.tokenizer

messages = [{"role": "user", "content": [
    {"type": "image", "url": "photo.jpg"},
    {"type": "text", "text": "Describe this image in detail."},
]}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
out = generate(model, processor, prompt, ["photo.jpg"],
    max_tokens=200, repetition_penalty=1.2, temperature=0.7)
print(out.text)

Text

python

messages = [{"role": "user", "content": "What is the capital of France?"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
out = generate(model, processor, prompt, max_tokens=100, temperature=0.0)
print(out.text)

Bugs Fixed in mlx-vlm

#	Bug	Impact	Fix
1	`ScaledLinear` inherits `nn.Module` not `nn.Linear`	`nn.quantize()` can't find these layers	Change to `ScaledLinear(nn.Linear)`
2	Standard quantization quantizes PLE layers	Garbage output on 4-bit/8-bit	PLE-safe `class_predicate` skipping PLE/vision/audio
3	`processor.save_pretrained()` strips `feature_extractor`	Audio silently dropped	Copy `processor_config.json` from source
4	`SwitchLinear` (MoE) not quantized	26B-A4B: 49 GB instead of 16 GB	Check `hasattr(module, 'to_quantized')`

Fixed source files are included in the GitHub repo.

Convert From Source

bash

git clone https://github.com/FakeRocket543/mlx-gemma4.git
cd mlx-gemma4
python convert_gemma4.py 31B 4

Validation

All 12 variants validated on 10 images + 3 chat prompts. Full results: GitHub.

License

Model weights: Google Gemma License. Scripts: MIT.