Instructions to use google/functiongemma-270m-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/functiongemma-270m-it", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/functiongemma-270m-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/functiongemma-270m-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/functiongemma-270m-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use google/functiongemma-270m-it with Docker Model Runner:
docker model run hf.co/google/functiongemma-270m-it
Android + OpenCL implementation — runs on-device on non-flagship phones (Adreno 6xx)
Hi! I wanted to share an Android + OpenCL implementation of FunctionGemma-270M-IT, in case anyone wants to run it on a phone:
- Try it: Edgi on Google Play — runs fully on-device, no cloud.
- Open source: the app is built on top of the open-source adreno-llms inference engine — https://github.com/a8nova/adreno-llms — pure C++/OpenCL with hand-written kernels tuned for Adreno.
It's tuned and tested on Adreno 6xx GPUs — the GPU class in mid-range and older Android phones (verified on a 2020 Motorola Razr / Adreno 620) — and should run on most arm64 Android phones with OpenCL, though the optimizations are Adreno-specific. On the Adreno 620 it decodes at ~8.9 tok/s in fp16, and greedy output is bit-identical to the PyTorch reference.
Hope it's useful — happy to answer questions!