Field	Value
Source model	`google/gemma-4-E4B-it`
Format	MLX safetensors
Weight dtype	`bfloat16`
Tensor check	665 tensors, all `mlx.core.bfloat16`
Local conversion tool	`mlx-lm`
License	Apache 2.0 / Gemma license terms from upstream

Conversion command:

bash

mlx_lm.convert \
  --hf-path google/gemma-4-E4B-it \
  --mlx-path gemma-4-e4b-it-MLX-bf16 \
  --dtype bfloat16

Why BF16?

Gemma-4 is distributed natively in bfloat16. Keeping BF16 preserves the upstream numerical format while avoiding the quality/runtime tradeoffs of weight quantization.

Use with MLX

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("majentik/gemma-4-e4b-it-MLX-bf16")

messages = [{"role": "user", "content": "Explain Singapore's MRT system in one paragraph."}]
prompt = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_dict=False,
)

response = generate(model, tokenizer, prompt=prompt, max_tokens=256, verbose=True)
print(response)

Relationship to quantized variants

Use this repo when you want the unquantized BF16 reference decoder. For smaller/faster variants, use the existing quantized MLX repos under majentik, such as:

Notes

This is a format conversion of the upstream Gemma-4 E4B instruct model, not a fine-tune.
The weights remain unquantized BF16.
For licensing and acceptable use, follow the upstream Gemma terms linked above.