Gemma
Gemma is a family of lightweight, state-of-the-art open source language models from Google, built using the same research and technology as Gemini models, designed for responsible AI development.
https://ai.google.dev/gemma
Product Information
Updated:Apr 16, 2025
Gemma Monthly Traffic Trends
Gemma experienced a 10.8% decline in traffic with 3.86M visits in July. The lack of significant product updates coupled with the introduction of Google's free AI coding assistant and Veo 2 on YouTube Shorts may have impacted user engagement.
What is Gemma
Gemma is an open source AI model family developed by Google, offering lightweight yet powerful language models in sizes ranging from 2B to 27B parameters. Built on the same foundation as Google's Gemini models, Gemma aims to democratize access to advanced AI capabilities while promoting responsible development. The Gemma family includes text generation models, as well as specialized variants for tasks like code generation (CodeGemma) and vision-language processing (PaliGemma). Gemma models are designed to be efficient, allowing them to run on a wide range of hardware from laptops to cloud infrastructure.
Key Features of Gemma
Gemma is a family of lightweight, open-source AI language models developed by Google, built from the same technology as Gemini models. It offers state-of-the-art performance in smaller sizes (2B, 7B, 9B, 27B parameters), incorporates safety measures, and is designed for responsible AI development. Gemma is framework-flexible, optimized for Google Cloud, and can run on various hardware from laptops to cloud infrastructure.
Lightweight and efficient: Gemma models achieve exceptional benchmark results at smaller sizes, even outperforming some larger open models, allowing for deployment on laptops and mobile devices.
Framework flexibility: Compatible with JAX, TensorFlow, and PyTorch through Keras 3.0, enabling developers to easily switch frameworks based on their needs.
Responsible AI design: Incorporates comprehensive safety measures through curated datasets and rigorous tuning to ensure responsible and trustworthy AI solutions.
Google Cloud optimization: Offers deep customization options and deployment on flexible, cost-efficient AI-optimized infrastructure through Vertex AI and Google Kubernetes Engine.
Use Cases of Gemma
Natural language processing tasks: Gemma can be used for various text generation tasks including question answering, summarization, and reasoning.
Code generation and completion: CodeGemma variant brings powerful code completion and generation capabilities suitable for local computers.
Vision-language tasks: PaliGemma variant is designed for a wide range of vision-language tasks, combining text and image processing capabilities.
AI safety and content moderation: ShieldGemma offers safety content classifier models to filter input and outputs of AI models, enhancing user safety.
Pros
Open-source and commercially friendly licensing
Exceptional performance for its size
Designed with responsible AI principles
Versatile deployment options from edge devices to cloud
Cons
Not as powerful as larger closed-source models like GPT-4 or Gemini Ultra
Requires technical expertise to implement and fine-tune effectively
How to Use Gemma
Request access to Gemma: Before using Gemma for the first time, you must request access through Kaggle. You'll need to use a Kaggle account to accept the Gemma use policy and license terms.
Choose a Gemma model: Select from Gemma 2B, 7B, 9B or 27B models depending on your needs and hardware capabilities. Smaller models can run on laptops while larger ones are better for desktops or servers.
Set up your development environment: Gemma works with popular frameworks like JAX, PyTorch, and TensorFlow via Keras 3.0. You can use tools like Google Colab, Kaggle notebooks, or set up a local environment.
Download the model: Download the Gemma model weights from Kaggle, Hugging Face, or the Vertex AI Model Garden.
Load the model: Use the appropriate framework (e.g. Keras, PyTorch) to load the Gemma model into your environment.
Format your input: Gemma uses specific formatting for inputs. Use the provided chat templates to properly format your prompts.
Generate text: Use the model's generate method to create text outputs based on your input prompts.
Fine-tune (optional): If desired, you can fine-tune Gemma on your own data using techniques like LoRA (Low-Rank Adaptation) to specialize it for specific tasks.
Deploy (optional): For production use, you can deploy Gemma models on Google Cloud services like Vertex AI or Google Kubernetes Engine (GKE) for scalable inference.
Gemma FAQs
Gemma is a family of lightweight, open-source AI models developed by Google DeepMind. It is built from the same research and technology used to create Google's Gemini models, but designed to be more compact and efficient for developers to use.
Official Posts
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Analytics of Gemma Website
Gemma Traffic & Rankings
5.4M
Monthly Visits
-
Global Rank
-
Category Rank
Traffic Trends: May 2024-Mar 2025
Gemma User Insights
00:02:44
Avg. Visit Duration
2.76
Pages Per Visit
53.62%
User Bounce Rate
Top Regions of Gemma
US: 14.66%
IN: 11.03%
CN: 8.85%
RU: 5.15%
VN: 3.99%
Others: 56.32%
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