Gemma 2

Gemma 2

Google
Mu

Mu

Microsoft
+
+

Related Products

  • LTX
    182 Ratings
    Visit Website
  • Google AI Studio
    41 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    1,161 Ratings
    Visit Website
  • LM-Kit.NET
    29 Ratings
    Visit Website
  • Careerminds
    46 Ratings
    Visit Website
  • Runpod
    230 Ratings
    Visit Website
  • IONOS Cloud GPU Servers
    45,340 Ratings
    Visit Website
  • Sogolytics
    869 Ratings
    Visit Website
  • Innoslate
    98 Ratings
    Visit Website
  • RaimaDB
    12 Ratings
    Visit Website

About

A family of state-of-the-art, light-open models created from the same research and technology that were used to create Gemini models. These models incorporate comprehensive security measures and help ensure responsible and reliable AI solutions through selected data sets and rigorous adjustments. Gemma models achieve exceptional comparative results in their 2B, 7B, 9B, and 27B sizes, even outperforming some larger open models. With Keras 3.0, enjoy seamless compatibility with JAX, TensorFlow, and PyTorch, allowing you to effortlessly choose and change frameworks based on task. Redesigned to deliver outstanding performance and unmatched efficiency, Gemma 2 is optimized for incredibly fast inference on various hardware. The Gemma family of models offers different models that are optimized for specific use cases and adapt to your needs. Gemma models are large text-to-text lightweight language models with a decoder, trained in a huge set of text data, code, and mathematical content.

About

Mu is a 330-million-parameter encoder–decoder language model designed to power the agent in Windows settings by mapping natural-language queries to Settings function calls, running fully on-device via NPUs at over 100 tokens per second while maintaining high accuracy. Drawing on Phi Silica optimizations, Mu’s encoder–decoder architecture reuses a fixed-length latent representation to cut computation and memory overhead, yielding 47 percent lower first-token latency and 4.7× higher decoding speed on Qualcomm Hexagon NPUs compared to similar decoder-only models. Hardware-aware tuning, including a 2/3–1/3 encoder–decoder parameter split, weight sharing between input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, enables fast inference at over 200 tokens per second on devices like Surface Laptop 7 and sub-500 ms response times for settings queries.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Not Supported
Linux Not Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Developers and teams looking for a solution offering LLMs to improve their AI development operations

Audience

Developers seeking a solution to navigate and configure system settings through natural language

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Supported
Live Online Supported
In Person Supported

Company Information

Google
United States
ai.google.dev/gemma

Company Information

Microsoft
Founded: 1975
United States
blogs.windows.com/windowsexperience/2025/06/23/introducing-mu-language-model-and-how-it-enabled-the-agent-in-windows-settings/

Alternatives

Alternatives

CodeT5

CodeT5

Salesforce
GLM-OCR

GLM-OCR

Z.ai
Gemma 3

Gemma 3

Google
Gemma

Gemma

Google
Gemma 4

Gemma 4

Google
Falcon-7B

Falcon-7B

Technology Innovation Institute (TII)

Categories

Categories

AI Models Supported

Integrations

AiAssistWorks Supported
C++ Supported
Clojure Supported
Database Mart Supported
F# Supported
Gemini Enterprise Agent Platform Supported
Google Cloud Platform Supported
Google Colab Supported
HTML Supported
Hugging Face Supported
Java Supported
MedGemma Supported
MongoDB Supported
Nebius Token Factory Supported
Ollama Supported
Python Supported
Rust Supported
SQL Supported
TypeScript Supported
Visual Basic Supported

Integrations

AiAssistWorks Not Supported
C++ Not Supported
Clojure Not Supported
Database Mart Not Supported
F# Not Supported
Gemini Enterprise Agent Platform Not Supported
Google Cloud Platform Not Supported
Google Colab Not Supported
HTML Not Supported
Hugging Face Not Supported
Java Not Supported
MedGemma Not Supported
MongoDB Not Supported
Nebius Token Factory Not Supported
Ollama Not Supported
Python Not Supported
Rust Not Supported
SQL Not Supported
TypeScript Not Supported
Visual Basic Not Supported
Claim Gemma 2 and update features and information
Claim Gemma 2 and update features and information
Claim Mu and update features and information
Claim Mu and update features and information