Instructions to use mlx-community/gemma-3n-E4B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/gemma-3n-E4B-bf16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="mlx-community/gemma-3n-E4B-bf16")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("mlx-community/gemma-3n-E4B-bf16")
model = AutoModelForMultimodalLM.from_pretrained("mlx-community/gemma-3n-E4B-bf16", device_map="auto")

MLX

How to use mlx-community/gemma-3n-E4B-bf16 with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("mlx-community/gemma-3n-E4B-bf16")
config = load_config("mlx-community/gemma-3n-E4B-bf16")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use mlx-community/gemma-3n-E4B-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/gemma-3n-E4B-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/gemma-3n-E4B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mlx-community/gemma-3n-E4B-bf16

SGLang

How to use mlx-community/gemma-3n-E4B-bf16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mlx-community/gemma-3n-E4B-bf16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/gemma-3n-E4B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mlx-community/gemma-3n-E4B-bf16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/gemma-3n-E4B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mlx-community/gemma-3n-E4B-bf16 with Docker Model Runner:
```
docker model run hf.co/mlx-community/gemma-3n-E4B-bf16
```
Atomic Chat

gemma-3n-E4B-bf16

File size: 1,092 Bytes

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  "data_format": "channels_first",
  "default_to_square": false,
  "device": null,
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  "dither": 0.0,
  "do_center_crop": null,
  "do_convert_rgb": null,
  "do_normalize": false,
  "do_rescale": true,
  "do_resize": true,
  "feature_size": 128,
  "fft_length": 1024,
  "fft_overdrive": true,
  "frame_length": 512,
  "hop_length": 160,
  "image_mean": [
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  "image_processor_type": "SiglipImageProcessorFast",
  "image_seq_length": 256,
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  "input_data_format": null,
  "input_scale_factor": 1.0,
  "max_frequency": 7600.0,
  "mel_floor": 1e-05,
  "min_frequency": 125.0,
  "padding_side": "right",
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  "per_bin_mean": null,
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  "preemphasis": 0.97,
  "preemphasis_htk_flavor": true,
  "processor_class": "Gemma3nProcessor",
  "resample": 2,
  "rescale_factor": 0.00392156862745098,
  "return_attention_mask": true,
  "return_tensors": null,
  "sampling_rate": 16000,
  "size": {
    "height": 768,
    "width": 768
  }
}