Google announces next-generation open model “Gemma 2”

Built for developers and researchers

Gemma 2 is not only more powerful, it's also designed to be easily integrated into your workflow.

Open and accessible: Like the original Gemma model, Gemma 2 is available under the commercially-friendly Gemma license, allowing developers and researchers to share and commercialize their innovations. Broad framework compatibility: Compatible with leading AI frameworks, including Hugging Face Transformers, JAX, PyTorch, and TensorFlow, via native Keras 3.0, vLLM, Gemma.cpp, Llama.cpp, and Ollama, making it easy to use Gemma 2 with your favorite tools and workflows. Additionally, Gemma is optimized with NVIDIA TensorRT-LLM to run on NVIDIA-accelerated infrastructure or as an NVIDIA NIM inference microservice. Fine-tune with Keras and Hugging Face today. We're actively working to enable additional parameter-efficient fine-tuning options. Easy to deploy: Starting next month, Google Cloud customers will be able to easily deploy and manage Gemma 2 with Vertex AI.

Check out the new Gemma Cookbook, a collection of practical examples and recipes that will guide you in building your own applications and fine-tuning Gemma 2 models for your specific tasks. Learn how to easily use Gemma with the tools of your choice, including common tasks like search extension generation.

Responsible AI Development

We are committed to providing developers and researchers with the resources they need to build and deploy AI responsibly, including through our Responsible Generative AI Toolkit. Our recently open-sourced LLM Comparator helps developers and researchers perform detailed evaluation of language models. Starting today, you can use the accompanying Python library to perform model-versus-data comparative evaluations and visualize the results in the app. Additionally, we are actively working to open-source SynthID, our text watermarking technology for Gemma models.

When training Gemma 2, we followed a robust internal safety process, filtering pre-training data and running rigorous tests and evaluations against a comprehensive set of metrics to identify and mitigate potential biases and risks. We share our results in a number of public benchmarks related to safety and representational harm.

Sources 1/ https://Google.com/ 2/ https://blog.google/technology/developers/google-gemma-2/ The mention sources can contact us to remove/changing this article

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