Instructions to use google/functiongemma-270m-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/functiongemma-270m-it with Transformers:

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

pipe = pipeline("text-generation", model="google/functiongemma-270m-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/functiongemma-270m-it")
model = AutoModelForCausalLM.from_pretrained("google/functiongemma-270m-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use google/functiongemma-270m-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/functiongemma-270m-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/functiongemma-270m-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/google/functiongemma-270m-it

SGLang

How to use google/functiongemma-270m-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/functiongemma-270m-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/functiongemma-270m-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/functiongemma-270m-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/functiongemma-270m-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use google/functiongemma-270m-it with Docker Model Runner:
```
docker model run hf.co/google/functiongemma-270m-it
```

Installation Video and Testing - Step by Step

by fahdmirzac - opened Dec 18, 2025

Discussion

fahdmirzac

Dec 18, 2025

Hi,
Kudos on producing such a sublime model. I did a local installation and testing video:

https://youtu.be/iTJD8MxR7nQ?si=v8ZxZ4hwZdMEVkBV

Thanks and regards,
Fahd

pannaga10

Google org Dec 22, 2025

Hi @fahdmirzac
Thank you for sharing this; it's incredibly helpful for the community and beginners alike, and we truly appreciate you taking the time to create this video!

likewendy

Dec 27, 2025

Visual support?

pannaga10

Google org Dec 30, 2025

Hi @likewendy FunctionGemma is a specialized version of our Gemma 3 270M model tuned for function calling . It is designed as a strong base for further training into custom,fast, private, local agents that translate natural language into executable API actions. For visual/multimodal support you can take a look at other models like T5 Gemma 2 .

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