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What is PaliGemma 2?

PaliGemma 2 marks a significant advancement in tunable vision-language models, building on the strengths of the original Gemma 2 by incorporating visual processing capabilities and streamlining the fine-tuning process to achieve exceptional performance. This innovative model allows users to visualize, interpret, and interact with visual information, paving the way for a multitude of creative applications. Available in multiple sizes (3B, 10B, 28B parameters) and resolutions (224px, 448px, 896px), it provides flexible performance suitable for a variety of scenarios. PaliGemma 2 stands out for its ability to generate detailed and contextually relevant captions for images, going beyond mere object identification to describe actions, emotions, and the overarching story conveyed by the visuals. Our findings highlight its advanced capabilities in diverse tasks such as recognizing chemical equations, analyzing music scores, executing spatial reasoning, and producing reports on chest X-rays, as detailed in the accompanying technical documentation. Transitioning to PaliGemma 2 is designed to be a simple process for existing users, ensuring a smooth upgrade while enhancing their operational capabilities. The model's adaptability and comprehensive features position it as an essential resource for researchers and professionals across different disciplines, ultimately driving innovation and efficiency in their work. As such, PaliGemma 2 represents not just an upgrade, but a transformative tool for advancing visual comprehension and interaction.

What is Gemma?

Gemma encompasses a series of innovative, lightweight open models inspired by the foundational research and technology that drive the Gemini models. Developed by Google DeepMind in collaboration with various teams at Google, the term "gemma" derives from Latin, meaning "precious stone." Alongside the release of our model weights, we are also providing resources designed to foster developer creativity, promote collaboration, and uphold ethical standards in the use of Gemma models. Sharing essential technical and infrastructural components with Gemini, our leading AI model available today, the 2B and 7B versions of Gemma demonstrate exceptional performance in their weight classes relative to other open models. Notably, these models are capable of running seamlessly on a developer's laptop or desktop, showcasing their adaptability. Moreover, Gemma has proven to not only surpass much larger models on key performance benchmarks but also adhere to our rigorous standards for producing safe and responsible outputs, thereby serving as an invaluable tool for developers seeking to leverage advanced AI capabilities. As such, Gemma represents a significant advancement in accessible AI technology.

Media

Media

Integrations Supported

PyTorch
Gemma

Integrations Supported

PyTorch
Axolotl
C#
C++
F#
Gemini Robotics
Gemma 2
Google AI Studio
Java
Kerlig
NativeMind
Nutanix Enterprise AI
PaliGemma 2
Python
TypeScript
Undrstnd
Unmixr
Visual Basic
oMLX

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
On-Prem
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Google

Date Founded

1994

Company Location

United States

Company Website

developers.googleblog.com/en/introducing-paligemma-2-powerful-vision-language-models-simple-fine-tuning/

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

ai.google.dev/gemma

Categories and Features

AI Models

Not specified

AI Vision Models

Not specified

Computer Vision

Not specified

Categories and Features

AI Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

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