Instructions to use yasu-oh/gemma-4-E2B-it-qat-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yasu-oh/gemma-4-E2B-it-qat-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="yasu-oh/gemma-4-E2B-it-qat-NVFP4")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("yasu-oh/gemma-4-E2B-it-qat-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("yasu-oh/gemma-4-E2B-it-qat-NVFP4", device_map="auto")
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 Settings

vLLM

How to use yasu-oh/gemma-4-E2B-it-qat-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yasu-oh/gemma-4-E2B-it-qat-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yasu-oh/gemma-4-E2B-it-qat-NVFP4",
		"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/yasu-oh/gemma-4-E2B-it-qat-NVFP4

SGLang

How to use yasu-oh/gemma-4-E2B-it-qat-NVFP4 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 "yasu-oh/gemma-4-E2B-it-qat-NVFP4" \
    --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": "yasu-oh/gemma-4-E2B-it-qat-NVFP4",
		"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 "yasu-oh/gemma-4-E2B-it-qat-NVFP4" \
        --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": "yasu-oh/gemma-4-E2B-it-qat-NVFP4",
		"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 yasu-oh/gemma-4-E2B-it-qat-NVFP4 with Docker Model Runner:
```
docker model run hf.co/yasu-oh/gemma-4-E2B-it-qat-NVFP4
```

gemma-4-E2B-it-qat-NVFP4 / README.md

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metadata

library_name: transformers
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: image-text-to-text
base_model:
  - google/gemma-4-E2B-it-qat-q4_0-unquantized
datasets:
  - CohereLabs/aya_dataset
  - interstellarninja/hermes_reasoning_tool_use
tags:
  - quantization
  - nvfp4

gemma-4-E2B-it-qat-NVFP4

An NVFP4-quantized version of google/gemma-4-E2B-it-qat-q4_0-unquantized.

This model was independently quantized to NVFP4 W4A4 using LLM Compressor and saved in the compressed-tensors format.

Model Details

Base model: google/gemma-4-E2B-it-qat-q4_0-unquantized
Architecture: Gemma4ForConditionalGeneration
Quantization: NVFP4 W4A4
Serialization format: nvfp4-pack-quantized
Compression backend: compressed-tensors
Calibration samples: 1,024
Maximum calibration sequence length: 8,192 tokens

Calibration Data

The following datasets were used for calibration:

The calibration dataset consists of:

512 general multilingual instruction samples from Aya
512 reasoning and tool-use conversation samples from Hermes

Quantization Configuration

default_stage:
  default_modifiers:
    GPTQModifier:
      config_groups:
        group_0:
          targets: [Linear]
          weights:
            num_bits: 4
            type: float
            symmetric: true
            group_size: 16
            strategy: tensor_group
            block_structure: null
            dynamic: false
            actorder: static
            scale_dtype: torch.float8_e4m3fn
            zp_dtype: null
            observer: imatrix_mse
            observer_kwargs: {strict: true}
          input_activations:
            num_bits: 4
            type: float
            symmetric: true
            group_size: 16
            strategy: tensor_group
            block_structure: null
            dynamic: local
            actorder: null
            scale_dtype: torch.float8_e4m3fn
            zp_dtype: null
            observer: static_minmax
            observer_kwargs: {}
          output_activations: null
          format: null
      targets: [Linear]
      ignore: [lm_head, 're:.*embed.*', 're:.*vision.*', 're:.*audio.*', 're:.*router.*', 're:.*per_layer.*']
      kv_cache_scheme:
        num_bits: 8
        type: float
        symmetric: true
        group_size: null
        strategy: tensor
        block_structure: null
        dynamic: false
        actorder: null
        scale_dtype: null
        zp_dtype: null
        observer: static_minmax
        observer_kwargs: {}
      bypass_divisibility_checks: false
      requires_calibration_data: true
      block_size: 128
      dampening_frac: 0.01
      actorder: static
      offload_hessians: false

Reproducibility

The quantization code and calibration preprocessing are available at:

gemma4-nvfp4-quantization

The quantization run used:

transformers:        5.12.1
llmcompressor:       0.12.1.dev92+g8cec0acc1
compressed-tensors:  0.17.2a20260729