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: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"