Instructions to use google/gemma-4-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-4-31B 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")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("google/gemma-4-31B") model = AutoModelForImageTextToText.from_pretrained("google/gemma-4-31B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use google/gemma-4-31B 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" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/gemma-4-31B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/google/gemma-4-31B
- SGLang
How to use google/gemma-4-31B 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" \ --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/gemma-4-31B", "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/gemma-4-31B" \ --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/gemma-4-31B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use google/gemma-4-31B with Docker Model Runner:
docker model run hf.co/google/gemma-4-31B
Update python libs requirements
#4
by 1MrazorT1 - opened
Running the first example script in this readme crashes with error:
Traceback (most recent call last):
File "/home/ubuntu/test_gemma/script.py", line 6, in <module>
processor = AutoProcessor.from_pretrained(MODEL_ID)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/test_gemma/.venv/lib/python3.12/site-packages/transformers/models/auto/processing_auto.py", line 429, in from_pretrained
return processor_class.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/test_gemma/.venv/lib/python3.12/site-packages/transformers/utils/import_utils.py", line 2035, in __getattribute__
requires_backends(cls, cls._backends)
File "/home/ubuntu/test_gemma/.venv/lib/python3.12/site-packages/transformers/utils/import_utils.py", line 2021, in requires_backends
raise ImportError("".join(failed))
ImportError:
Gemma4Processor requires the PIL library but it was not found in your environment. You can install it with pip:
`pip install pillow`. Please note that you may need to restart your runtime after installation.
After installing pillow, it crashes with the second error:
ImportError:
Gemma4VideoProcessor requires the Torchvision library but it was not found in your environment. Check out the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
Please note that you may need to restart your runtime after installation.