Gemma

Gemma Open Models (Google Gemma) Overview

Gemma is a collection of lightweight, state-of-the-art open models built from the same research and technology that powers Google’s Gemini 2.0, designed to run via multiple frameworks and deployment targets. It emphasizes multilingual capabilities, large context handling, and multimodal understanding (text, image, and video) to enable advanced, globally accessible AI applications.

Key capabilities include a 128K-token context window for processing extensive information, multilingual support across 140+ languages, and the ability to analyze words and images to power interactive and intelligent applications. Gemma models are designed for research, experimentation, and production when paired with the Gemmaverse ecosystem and deployment options.

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How to Use Gemma

• Access through Google AI Studio, Colab, Hugging Face, Keras, Ollama, and Python libraries.
• Fine-tune and deploy using LoRA and model-parallelism, on TPU backends or locally, depending on the chosen workflow.
• Explore a growing catalog of specialized models for vision, language, and multimodal tasks (e.g., ShieldGemma, PaliGemma, DataGemma, Gemma Scope).

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Gemmaverse and Ecosystem

Gemmaverse provides a community-driven ecosystem of Gemma models and tools ready to power and inspire innovation. It supports multiple deployment targets and simplifies experimentation, research, and deployment at scale.

• Gemma models span research benchmarks and practical deployments across multilingual, multimodal, and reasoning tasks.
• Visualize benchmarks and compare model performance across various datasets and evaluation suites.
• A growing family of companion data, safety, and multimodal integration tools (e.g., safety labeling and data connectors).

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Deployment Targets

• Mobile: On-device deployment with Google AI Edge for low-latency, offline functionality (e.g., mobile apps, IoT, embedded systems).
• Web: Integrations for rich web experiences and interactive AI features.
• Cloud: Scalable cloud deployments to handle large workloads.
• Hybrid/Experimental: Local inference via Ollama or Python libraries for experimentation and research.

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Notable Models and Roles

• Gemma 3: 128K context, multilingual support, multimodal capabilities. Benchmarked across diverse tasks with extensive context handling.
• DataGemma: Connects LLMs with real-world data from Google Data Commons.
• ShieldGemma 2: Image safety-focused model with labeled safety categories.
• PaliGemma 2: Visual-capable extension for vision-language tasks.
• Gemma Scope: Provides transparency into model decision-making for research.

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How to Access and Deploy

• Hugging Face: Use Gemma models with the Transformers ecosystem.
• Keras (JAX backend): Finetune Gemma with LoRA and model-parallelism on TPUs.
• Ollama: Run local inferences with Gemma models.
• Gemma Python Library: Programmatic access and tooling for chat and fine-tuning.
• Google AI Studio / Colab: Try, test, and prototype Gemma models in a collaborative environment.

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Safety and Responsible AI

• Gemma provides a foundation for research and production with an emphasis on responsible AI practices. Visit the Gemma documentation for safety guidelines, usage policies, and best practices when integrating with real-world systems.

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Core Features

• 128K-token context window for long-context understanding
• Multilingual support across 140+ languages
• Text, image, and video understanding (multimodal)
• Lightweight open-model family suitable for research and deployment
• Multiple deployment targets: mobile, web, cloud, and local
• Integration with Hugging Face, Keras, Ollama, Colab, and Google AI Studio
• Specialized model variants for safety, data integration, and analytics