Instructions to use google/gemma-3-4b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-3-4b-it with Transformers:

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

pipe = pipeline("image-text-to-text", model="google/gemma-3-4b-it")
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("google/gemma-3-4b-it")
model = AutoModelForMultimodalLM.from_pretrained("google/gemma-3-4b-it")
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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use google/gemma-3-4b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-3-4b-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-3-4b-it",
		"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/google/gemma-3-4b-it

SGLang

How to use google/gemma-3-4b-it 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 "google/gemma-3-4b-it" \
    --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": "google/gemma-3-4b-it",
		"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 "google/gemma-3-4b-it" \
        --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": "google/gemma-3-4b-it",
		"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 google/gemma-3-4b-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-3-4b-it
```

Add model-index with comprehensive benchmark evaluations

#78

by davidlms - opened Dec 7, 2025

base: refs/heads/main

←

from: refs/pr/78

Discussion Files changed

+162

-0

davidlms

Dec 7, 2025

Added structured evaluation results from README benchmark tables covering 4 categories:

1. Reasoning & Factuality (11 benchmarks):

HellaSwag: 77.2, BoolQ: 72.3, PIQA: 79.6, SocialIQA: 51.9
TriviaQA: 65.8, Natural Questions: 20.0
ARC-c: 56.2, ARC-e: 82.4, WinoGrande: 64.7
BIG-Bench Hard: 50.9, DROP: 60.1

2. STEM & Code (8 benchmarks):

MMLU: 59.6, MMLU Pro COT: 29.2, AGIEval: 42.1
MATH: 24.2, GSM8K: 38.4, GPQA: 15.0
MBPP: 46.0, HumanEval: 36.0

3. Multilingual (7 benchmarks):

MGSM: 34.7, Global-MMLU-Lite: 57.0
WMT24++ (ChrF): 48.4, FloRes: 39.2, XQuAD: 68.0
ECLeKTic: 11.0, IndicGenBench: 57.2

4. Multimodal (15 benchmarks):

COCOcap: 102.0, DocVQA: 72.8, InfoVQA: 44.1, MMMU: 39.2
TextVQA: 58.9, RealWorldQA: 45.5, ReMI: 27.3
AI2D: 63.2, ChartQA: 63.6, VQAv2: 63.9
BLINK: 38.0, OKVQA: 51.0, TallyQA: 42.5
SpatialSense VQA: 50.9, CountBenchQA: 26.1

Total: 41 benchmarks across reasoning, STEM, code, multilingual, and multimodal capabilities.

This enables the model to appear in leaderboards and makes it easier to compare with other models.

Note: Existing PRs (#57, #49, #34) modify README text content. This PR adds structured metadata to the YAML frontmatter and should not conflict.

Add model-index with comprehensive benchmark evaluations8ed7826f

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