Gemma#

Gemma is a family of open-weights Large Language Model (LLM) by Google DeepMind, based on Gemini research and technology.

This repository contains the implementation of the gemma PyPI package. A JAX library to use and fine-tune Gemma.

For examples and use cases, see our documentation. Please report issues and feedback in our GitHub.

Installation#

Install JAX for CPU, GPU or TPU. Follow the instructions on the JAX website.
Run
```
pip install gemma
```

Examples#

Here is a minimal example to have a multi-turn, multi-modal conversation with Gemma:

from gemma import gm

# Model and parameters
model = gm.nn.Gemma3_4B()
params = gm.ckpts.load_params(gm.ckpts.CheckpointPath.GEMMA3_4B_IT)

# Example of multi-turn conversation
sampler = gm.text.ChatSampler(
    model=model,
    params=params,
    multi_turn=True,
)

prompt = """Which of the two images do you prefer?

Image 1: <start_of_image>
Image 2: <start_of_image>

Write your answer as a poem."""
out0 = sampler.chat(prompt, images=[image1, image2])

out1 = sampler.chat('What about the other image ?')

Our documentation contains various Colabs and tutorials, including:

Additionally, our examples/ folder contain additional scripts to fine-tune and sample with Gemma.

Learn more about Gemma#

To use this library: Gemma documentation
Technical reports for metrics and model capabilities:
- Gemma 1
- Gemma 2
- Gemma 3
Other Gemma implementations and doc on the Gemma ecosystem

Downloading the models#

To download the model weights. See our documentation.

System Requirements#

Gemma can run on a CPU, GPU and TPU. For GPU, we recommend 8GB+ RAM on GPU for The 2B checkpoint and 24GB+ RAM on GPU are used for the 7B checkpoint.

Contributing#

We welcome contributions! Please read our Contributing Guidelines before submitting a pull request.

This is not an official Google product.

Gemma

Contents