base on Gemma open-weight LLM library, from Google DeepMind # 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: <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: * [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.* ", Assign "at most 3 tags" to the expected json: {"id":"8020","tags":[]} "only from the tags list I provide: []" returns me the "expected json"