# gemma

**Repository Path**: oliverxulei/gemma

## Basic Information

- **Project Name**: gemma
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-02-22
- **Last Updated**: 2024-02-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Gemma

[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 an inference implementation and examples, based on the
[Flax](https://github.com/google/flax) and [JAX](https://github.com/google/jax).

### Learn more about Gemma

-   The [Gemma technical report](https://ai.google.dev/gemma/technical-report)
    details the models' capabilities.
-   For tutorials, reference implementations in other ML frameworks, and more,
    visit https://ai.google.dev/gemma.

## Quick start

### Installation

1.  To install Gemma you need to use Python 3.10 or higher.

2.  Install JAX for CPU, GPU or TPU. Follow instructions at
    [the JAX website](https://jax.readthedocs.io/en/latest/installation.html).

3.  Run

```
python -m venv gemma-demo
. gemma-demo/bin/activate
pip install git+https://github.com/google-deepmind/gemma.git
```

### Downloading the models

The model checkpoints are available through Kaggle at
http://kaggle.com/models/google/gemma. Select one of the **Flax** model
variations, click the ⤓ button to download the model archive, then extract the
contents to a local directory. The archive contains both the model weights and
the tokenizer, for example the 2b Flax variation contains:

```
2b/              # Directory containing model weights
tokenizer.model  # Tokenizer
```

### Running the unit tests

To run the unit tests, install the optional `[test]` dependencies (e.g. using
`pip install -e .[test]` from the root of the source tree), then:

```
pytest .
```

Note that the tests in `sampler_test.py` are skipped by default since no
tokenizer is distributed with the Gemma sources. To run these tests, download a
tokenizer following the instructions above, and update the `_VOCAB` constant in
`sampler_test.py` with the path to `tokenizer.model`.

## Examples

To run the example sampling script, pass the paths to the weights directory and
tokenizer:

```
python examples/sampling.py -- \
  --path_checkpoint=/path/to/archive/contents/2b/ \
  --path_tokenizer=/path/to/archive/contents/tokenizer.model
```

There are also several Colab notebook tutorials:

-   [`colabs/sampling_tutorial.ipynb`](https://colab.sandbox.google.com/github/google-deepmind/gemma/blob/main/colabs/sampling_tutorial.ipynb)
    contains a [Colab](http://colab.google) notebook with a sampling example.

-   [`colabs/fine_tuning_tutorial.ipynb`](https://colab.sandbox.google.com/github/google-deepmind/gemma/blob/main/colabs/fine_tuning_tutorial.ipynb)
    contains a [Colab](http://colab.google) with a basic tutorial on how to fine
    tune Gemma for a task, such as English to French translation.

-   [`colabs/gsm8k_eval.ipynb`](https://colab.sandbox.google.com/github/google-deepmind/gemma/blob/main/colabs/gsm8k_eval.ipynb)
    is a [Colab](http://colab.google) with a reference GSM8K eval
    implementation.

To run these notebooks you will need to download a local copy of the weights and
tokenizer (see above), and update the `ckpt_path` and `vocab_path` variables
with the corresponding paths.

## System Requirements

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

## Contributing

We are open to bug reports, pull requests (PR), and other contributions. Please
see [CONTRIBUTING.md](CONTRIBUTING.md) for details on PRs.

## License

Copyright 2024 DeepMind Technologies Limited

This code is licensed under the Apache License, Version 2.0 (the \"License\");
you may not use this file except in compliance with the License. You may obtain
a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an AS IS BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

## Disclaimer

This is not an official Google product.