https://github.com/google-deepmind/gemma
Gemma open-weight LLM library, from Google DeepMind
https://github.com/google-deepmind/gemma
Last synced: 11 days ago
JSON representation
Gemma open-weight LLM library, from Google DeepMind
- Host: GitHub
- URL: https://github.com/google-deepmind/gemma
- Owner: google-deepmind
- License: apache-2.0
- Created: 2024-02-20T18:39:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-23T15:41:44.000Z (11 days ago)
- Last Synced: 2025-04-23T17:09:56.071Z (11 days ago)
- Language: Jupyter Notebook
- Homepage: https://gemma-llm.readthedocs.io
- Size: 739 KB
- Stars: 3,201
- Watchers: 37
- Forks: 434
- Open Issues: 74
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-repositories - google-deepmind/gemma - Open weights LLM from Google DeepMind. (Python)
- AiTreasureBox - google-deepmind/gemma - 04-30_3219_2](https://img.shields.io/github/stars/google-deepmind/gemma.svg)|Open weights LLM from Google DeepMind.| (Repos)
- awesome-llm - google-deepmind/gemma -  - Open weights LLM from Google DeepMind. (Materials / GitHub repositories)
README
# Gemma
[](https://github.com/google-deepmind/gemma/actions/workflows/pytest_and_autopublish.yml)
[](https://badge.fury.io/py/gemma)
[](https://gemma-llm.readthedocs.io/en/latest/?badge=latest)
[Gemma](https://ai.google.dev/gemma) is a family of open-weights Large Language
Model (LLM) by [Google DeepMind](https://deepmind.google/), based on Gemini
research and technology.
This repository contains the implementation of the
[`gemma`](https://pypi.org/project/gemma/) PyPI package. A
[JAX](https://github.com/jax-ml/jax) library to use and fine-tune Gemma.
For examples and use cases, see our
[documentation](https://gemma-llm.readthedocs.io/). Please
report issues and feedback in
[our GitHub](https://github.com/google-deepmind/gemma/issues).
### Installation
1. Install JAX for CPU, GPU or TPU. Follow the instructions on
[the JAX website](https://jax.readthedocs.io/en/latest/installation.html).
1. Run
```sh
pip install gemma
```
### Examples
Here is a minimal example to have a multi-turn, multi-modal conversation with
Gemma:
```python
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:
Image 2:
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:
* [Sampling](https://gemma-llm.readthedocs.io/en/latest/colab_sampling.html)
* [Multi-modal](https://gemma-llm.readthedocs.io/en/latest/colab_multimodal.html)
* [Fine-tuning](https://gemma-llm.readthedocs.io/en/latest/colab_finetuning.html)
* [LoRA](https://gemma-llm.readthedocs.io/en/latest/colab_lora_sampling.html)
* ...
Additionally, our
[examples/](https://github.com/google-deepmind/gemma/tree/main/examples) folder
contain additional scripts to fine-tune and sample with Gemma.
### Learn more about Gemma
* To use this library: [Gemma documentation](https://gemma-llm.readthedocs.io/)
* Technical reports for metrics and model capabilities:
* [Gemma 1](https://goo.gle/GemmaReport)
* [Gemma 2](https://goo.gle/gemma2report)
* [Gemma 3](https://storage.googleapis.com/deepmind-media/gemma/Gemma3Report.pdf)
* Other Gemma implementations and doc on the
[Gemma ecosystem](https://ai.google.dev/gemma/docs)
### Downloading the models
To download the model weights. See
[our documentation](https://gemma-llm.readthedocs.io/en/latest/checkpoints.html).
### 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](./CONTRIBUTING.md) before submitting a pull request.
*This is not an official Google product.*