Instructions to use google/gemma-4-31B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-4-31B-it with Transformers:

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

pipe = pipeline("image-text-to-text", model="google/gemma-4-31B-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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("google/gemma-4-31B-it")
model = AutoModelForImageTextToText.from_pretrained("google/gemma-4-31B-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

vLLM

How to use google/gemma-4-31B-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-4-31B-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-4-31B-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-4-31B-it

SGLang

How to use google/gemma-4-31B-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-4-31B-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-4-31B-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-4-31B-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-4-31B-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-4-31B-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-4-31B-it
```

vllm / sglang support?

by mtcl - opened Apr 2

Discussion

mtcl

Apr 2

is there a support for sglang/vllm?

macandchiz

Apr 2

vLLM are working on it as per their Github.

cpatonn

Apr 2

I think the PR is just merged an hour ago.

mtcl

Apr 2

i hope official instructions are updated in the docs soon here.

macandchiz

Apr 3

•

edited Apr 3

Here is a custom vllm image I've built. It works as intended: https://hub.docker.com/r/infantryman77/vllm-gemma4. Tested with Cline and Open-Webui. Not completely production ready for it works.

services:
  vllm:
    image: infantryman77/vllm-gemma4:nightly-20260402
    container_name: gemma4
    command:
      - /models/gemma-4-31B-it-AWQ-8bit
      - --served-model-name
      - gemma4-31b
      - --max-model-len
      - "131072"
      - --tensor-parallel-size
      - "4"
      - --gpu-memory-utilization
      - "0.97"
      - --reasoning-parser
      - gemma4
      - --enable-auto-tool-choice
      - --tool-call-parser
      - gemma4
      - --host
      - 0.0.0.0
      - --limit-mm-per-prompt
      - '{"image":4}'
      - --max-num-batched-tokens
      - "2096"
      - --max-num-seqs
      - "4"
      - --port
      - "8080"
      - --disable-custom-all-reduce
      - --override-generation-config
      - '{"temperature":1.0,"top_p":0.95,"top_k":64}'
    volumes:
      - /home/infantryman/vllm/models:/models
    ports:
      - "8080:8080"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    environment:
      - PYTORCH_ALLOC_CONF=expandable_segments:True
      - LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:/usr/local/cuda/lib64
      - OMP_NUM_THREADS=1
      - PYTHONWARNINGS=ignore::FutureWarning
      - VLLM_WORKER_MULTIPROC_METHOD=spawn
    ipc: host
    restart: unless-stopped

Just-Bax

Apr 3

•

edited Apr 3

Official VLLM doc on how to use gemma 4 models: https://docs.vllm.ai/projects/recipes/en/latest/Google/Gemma4.html

VarunGumma

Apr 15

is SGLang support available?

sonali-kumari11

Google org Apr 16

Hi all,

Yes, both vLLM and SGLang offer official support for Gemma 4. Just make sure you're running the latest versions of these frameworks to handle the new model architecture and tokenizers correctly. For implementation details and setup, you can check out these official resources:

vLLM official guide: https://docs.vllm.ai/projects/recipes/en/latest/Google/Gemma4.html
Gemma 4 Optimized Support & NVFP4 Integration: https://github.com/sgl-project/sglang/issues/22129

kenleo

9 days ago

Official VLLM doc on how to use gemma 4 models: https://docs.vllm.ai/projects/recipes/en/latest/Google/Gemma4.html

It fails on a cluster of two dgx sparks.

kenleo

4 days ago

Official VLLM doc on how to use gemma 4 models: https://docs.vllm.ai/projects/recipes/en/latest/Google/Gemma4.html

It fails on a cluster of two dgx sparks.

It actually works. Check here.

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