gemma - GitLite

KO | EN

gitlite — search

#javascript #python #ai #llm #golang #go #machine-learning #rust #deep-learning #java #awesome #kubernetes #awesome-list #openai

gemma

(★ 5,677)

Gemma open-weight LLM library, from Google DeepMind

파일 탐색기

최종 버전 다운로드 (.zip)

- - pytest_and_autopublish.yml
- extensions.json
- settings.json
- finetuning.ipynb
- lora_finetuning.ipynb
- lora_sampling.ipynb
- multimodal.ipynb
- quantization_aware_training.ipynb
- quantization_sampling.ipynb
- sampling.ipynb
- sharding.ipynb
- tokenizer.ipynb
- tool_use.ipynb
- - favicon.png
  - icon.svg
- checkpoints.md
- conf.py
- dialog.md
- finetuning.md
- index.md
- lora_finetuning.md
- lora_sampling.md
- multimodal.md
- overview.md
- requirements.txt
- research.md
- sampling.md
- sharding.md
- tokenizer.md
- tool_use.md
- classification.py
- dpo.py
- lora.py
- multimodal.py
- npo.py
- seq2seq.py
- sharding.py
- - __init__.py
  - _chat_sampler.py
  - _chat_sampler_test.py
  - _early_stopping.py
  - _early_stopping_test.py
  - _models.py
  - _models_test.py
  - _paths.py
  - _sampler.py
  - _sampler_test.py
  - _transformer.py
  - example.ipynb
- - __init__.py
  - README.md
- - __init__.py
  - _einsum_utils.py
  - _einsum_utils_test.py
  - _interceptors.py
  - _interceptors_test.py
  - _lora.py
  - _lora_test.py
  - _quantization.py
  - _quantization_test.py
  - _quantization_utils.py
  - _tree_utils.py
  - _tree_utils_test.py
  - README.md
- __init__.py
.gitignore
.pylintrc
.readthedocs.yaml
CHANGELOG.md
CONTRIBUTING.md
LICENSE
pyproject.toml
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/google-deepmind/gemma

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd gemma

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

Python 3 pip 명령어를 쓰려면 Python이 필요합니다.

pip install gemma

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install gemma

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Gemma

[![Unittests](https://github.com/google-deepmind/gemma/actions/workflows/pytest_and_autopublish.yml/badge.svg)](https://github.com/google-deepmind/gemma/actions/workflows/pytest_and_autopublish.yml)
[![PyPI version](https://badge.fury.io/py/gemma.svg)](https://badge.fury.io/py/gemma)
[![Documentation Status](https://readthedocs.org/projects/gemma-llm/badge/?version=latest)](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 (Gemma 4)
model = gm.nn.Gemma4_E4B()
params = gm.ckpts.load_params(gm.ckpts.CheckpointPath.GEMMA4_E4B_IT)

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

prompt = """Which of the 2 images do you prefer ?

Image 1: <|image|>
Image 2: <|image|>

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

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

The same `ChatSampler` API works with all Gemma versions (2, 3, 3n, 4).

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)
  * [Gemma 4](https://ai.google.dev/gemma/docs/core/model_card_4)
* 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.*