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
```

New tool to benchmark GGUF files

#15

by Nerdsking - opened Jan 2

Discussion

Nerdsking

Jan 2

Hi friends,

I created a tool to benchmark gguf files. You may find it at https://github.com/nerdskingcom/gguf-humaneval-benchmark

Feel free to frop a star if you find it usefull.

Or simply use : "pip install gguf-humaneval-benchmark".

pannaga10

Google org Jan 6

Hi @Nerdsking
This is a fantastic contribution to the community—benchmarking GGUF files with HumanEval is incredibly useful for developers optimizing Gemma's performance. Thank you for your hard work and for making this so accessible via pip.

Nerdsking

Jan 15

Well at least one person said thanks. And I am grateful for that. I hope to find time to enhance this script in the near future as well to share other ideas with the community.

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