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What is Gemma 2?

The Gemma family is composed of advanced and lightweight models that are built upon the same groundbreaking research and technology as the Gemini line. These state-of-the-art models come with powerful security features that foster responsible and trustworthy AI usage, a result of meticulously selected data sets and comprehensive refinements. Remarkably, the Gemma models perform exceptionally well in their varied sizes—2B, 7B, 9B, and 27B—frequently surpassing the capabilities of some larger open models. With the launch of Keras 3.0, users benefit from seamless integration with JAX, TensorFlow, and PyTorch, allowing for adaptable framework choices tailored to specific tasks. Optimized for peak performance and exceptional efficiency, Gemma 2 in particular is designed for swift inference on a wide range of hardware platforms. Moreover, the Gemma family encompasses a variety of models tailored to meet different use cases, ensuring effective adaptation to user needs. These lightweight language models are equipped with a decoder and have undergone training on a broad spectrum of textual data, programming code, and mathematical concepts, which significantly boosts their versatility and utility across numerous applications. This diverse approach not only enhances their performance but also positions them as a valuable resource for developers and researchers alike.

What is ExecuTorch?

ExecuTorch is an innovative open-source framework created for PyTorch, tailored to enable the deployment of artificial intelligence and machine learning models directly on edge devices, which supports various functions including text, vision, speech, recommendation, and multimodal inference without relying on cloud services. This framework simplifies the process of exporting models from PyTorch by eliminating the need for any intermediate conversion formats, thereby preserving ATen operators and utilizing ahead-of-time compilation to optimize performance for specific hardware before deployment. With a modular architecture, developers enjoy the flexibility to choose both compile-time and runtime optimizations, all while working within the familiar PyTorch ecosystem, which incorporates torchao specifically for quantization purposes. The lightweight C++ runtime of ExecuTorch, which is approximately 50 KB in size, ensures its adaptability across an array of platforms, such as smartphones, desktops, embedded systems, microcontrollers, DSPs, and Cortex-M processors. Additionally, it supports a range of operating systems, including Android, iOS, Linux, Windows, macOS, and WebAssembly, and provides native APIs in languages like C++, Swift, Kotlin, and Objective-C. Consequently, ExecuTorch empowers developers with a robust tool for efficiently deploying AI models across a wide variety of devices and applications, making it a crucial asset in the field of edge computing. Its flexible architecture and multi-platform compatibility highlight its potential to support the growing demand for localized AI solutions.

Media

Media

Integrations Supported

C++
Kotlin
PyTorch
C#
Clojure
Database Mart
Gemini Enterprise Agent Platform
Gemma
LM-Kit.NET
MedGemma
MongoDB
Ruby
Rust
SQL
TensorFlow
VESSL AI

Integrations Supported

C++
Kotlin
PyTorch
Instagram
Swift
Voxtral
WhatsApp

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

Supported Platforms

Windows
Mac
On-Prem
Linux

Supported Platforms

Android
iPhone
iPad
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Google

Company Location

United States

Company Website

ai.google.dev/gemma

Company Facts

Organization Name

ExecuTorch

Company Location

United States

Company Website

executorch.ai/

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Inference

Not specified

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