Instructions to use 2imi9/gemma-4-E4B-it-NVFP4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 2imi9/gemma-4-E4B-it-NVFP4A16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="2imi9/gemma-4-E4B-it-NVFP4A16")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("2imi9/gemma-4-E4B-it-NVFP4A16")
model = AutoModelForImageTextToText.from_pretrained("2imi9/gemma-4-E4B-it-NVFP4A16")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 2imi9/gemma-4-E4B-it-NVFP4A16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "2imi9/gemma-4-E4B-it-NVFP4A16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "2imi9/gemma-4-E4B-it-NVFP4A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/2imi9/gemma-4-E4B-it-NVFP4A16

SGLang

How to use 2imi9/gemma-4-E4B-it-NVFP4A16 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 "2imi9/gemma-4-E4B-it-NVFP4A16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "2imi9/gemma-4-E4B-it-NVFP4A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "2imi9/gemma-4-E4B-it-NVFP4A16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "2imi9/gemma-4-E4B-it-NVFP4A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use 2imi9/gemma-4-E4B-it-NVFP4A16 with Docker Model Runner:
```
docker model run hf.co/2imi9/gemma-4-E4B-it-NVFP4A16
```

gemma-4-E4B-it-NVFP4A16

Model Overview

Base Model: google/gemma-4-E4B-it
Quantization: NVFP4A16 (4-bit NV floating point weights, 16-bit activations)
Quantization Tool: llm-compressor

Description

This model is a weight-only NVFP4A16 quantized version of Google's Gemma 4 E4B instruction-tuned model. Weights are quantized to FP4 with per-group (group size 16) quantization using the NVIDIA FP4 format, while activations remain in FP16.

The following modules are kept in their original precision:

lm_head
Vision encoder (vision_tower)
Audio encoder (audio_tower)
Vision embedding projection (embed_vision)
Audio embedding projection (embed_audio)

How It Was Made

from transformers import AutoModelForImageTextToText, AutoProcessor
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

MODEL_ID = "google/gemma-4-E4B-it"
model = AutoModelForImageTextToText.from_pretrained(MODEL_ID, dtype="auto")

recipe = QuantizationModifier(
    targets="Linear",
    scheme="NVFP4A16",
    ignore=[
        "lm_head",
        "re:.*vision_tower.*",
        "re:.*audio_tower.*",
        "re:.*embed_vision.*",
        "re:.*embed_audio.*",
    ],
)

oneshot(model=model, recipe=recipe)
model.save_pretrained("gemma-4-E4B-it-NVFP4A16", save_compressed=True)