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- Gemma 3 model overview - Google AI for Developers
Gemma is a family of generative artificial intelligence (AI) models and you can use them in a wide variety of generation tasks, including question answering, summarization, and reasoning
- GitHub - google-deepmind gemma: Gemma open-weight LLM library, from . . .
Gemma is a family of open-weights Large Language Model (LLM) by Google DeepMind, based on Gemini research and technology This repository contains the implementation of the gemma PyPI package
- Gemma (language model) - Wikipedia
Gemma is a series of open-source large language models developed by Google DeepMind It is based on similar technologies as Gemini The first version was released in February 2024, followed by Gemma 2 in June 2024 and Gemma 3 in March 2025
- Welcome Gemma 3: Googles all new multimodal, multilingual, long . . .
Today Google releases Gemma 3, a new iteration of their Gemma family of models The models range from 1B to 27B parameters, have a context window up to 128k tokens, can accept images and text, and support 140+ languages Try out Gemma 3 now 👉🏻 Gemma 3 Space All the models are on the Hub and tightly integrated with the Hugging Face ecosystem
- Gemma 3 | Powerful Lightweight AI Model | Try Free
Integrate Gemma 3 seamlessly with popular ML frameworks including PyTorch, TensorFlow, and JAX Enjoy optimized memory usage and computational efficiency, allowing you to run more complex workloads on existing hardware
- Gemma 3n - Google DeepMind
Gemma 3n was created in close collaboration with leading mobile hardware manufacturers It shares architecture with the next generation of Gemini Nano to empower a new wave of intelligent, on-device applications Engineered for speed and quality, with a significantly reduced memory footprint
- Hands-on with Gemma 3 on Google Cloud | Google Cloud Blog
Deploy Gemma 3, Google's open model, to production on Google Cloud Explore two hands-on paths: serverless Cloud Run for simplicity or GKE for robust orchestration
- Gemma 3n model overview | Google AI for Developers
This reduced parameter operation can be achieved using the flexible parameter technology built into Gemma 3n models to help them run efficiently on lower resource devices The parameters in Gemma 3n models are divided into 4 main groups: text, visual, audio, and per-layer embedding (PLE) parameters
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