- 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
- 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 AI | The best AI multimodal model on a single GPU
Simply take a photo of your meal, and Gemma 3 AI instantly analyzes what's on your plate Whether you're tracking calories, managing dietary restrictions, or just curious about your food choices, we provide clear insights in seconds
- Gemma 3n Powers Real-World Impact at the Edge
The Gemma 3n Impact Challenge reveals the model's profound potential for on-device, multimodal AI solutions addressing real-world problems
- 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
- Introducing Gemma 3 270M: The compact model for hyper-efficient AI
Explore Gemma 3 270M, a compact, energy-efficient AI model for task-specific fine-tuning, offering strong instruction-following and production-ready quantization
- Get started with Gemma models - Google AI for Developers
The Gemma family of open models includes a range of model sizes, capabilities, and task-specialized variations to help you build custom generative solutions These are the main paths you can follow when using Gemma models in an application:
|