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What is Towhee?

Leverage our Python API to build an initial version of your pipeline, while Towhee optimizes it for scenarios suited for production. Whether you are working with images, text, or 3D molecular structures, Towhee is designed to facilitate data transformation across nearly 20 varieties of unstructured data modalities. Our offerings include thorough end-to-end optimizations for your pipeline, which cover aspects such as data encoding and decoding, as well as model inference, potentially speeding up your pipeline performance by as much as tenfold. Towhee offers smooth integration with your chosen libraries, tools, and frameworks, making the development process more efficient. It also boasts a pythonic method-chaining API that enables you to easily create custom data processing pipelines. With support for schemas, handling unstructured data becomes as simple as managing tabular data. This adaptability empowers developers to concentrate on innovation, free from the burdens of intricate data processing challenges. In a world where data complexity is ever-increasing, Towhee stands out as a reliable partner for developers.

What is Mu?

On June 23, 2025, Microsoft introduced Mu, a cutting-edge language model boasting 330 million parameters and designed to significantly improve the agent experience in Windows environments by seamlessly converting natural language questions into functional calls for Settings, with all operations executed on-device via NPUs at an impressive speed exceeding 100 tokens per second while maintaining high accuracy. Utilizing Phi Silica optimizations, Mu's encoder-decoder architecture employs a fixed-length latent representation that notably minimizes computational requirements and memory consumption, achieving a 47 percent decrease in first-token latency and delivering a decoding speed that is 4.7 times faster on Qualcomm Hexagon NPUs in comparison to traditional decoder-only models. Furthermore, the model is enhanced by hardware-aware tuning methodologies, which incorporate a strategic 2/3–1/3 division of encoder and decoder parameters, shared weights for both input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, facilitating rapid inference rates that surpass 200 tokens per second on devices like the Surface Laptop 7, along with response times for settings-related queries that are under 500 ms. This impressive blend of features and optimizations establishes Mu as a revolutionary development in the realm of on-device language processing capabilities, setting new standards for speed and efficiency. As a result, users can expect a more intuitive and responsive experience when interacting with their Windows settings through natural language.

Media

Media

Integrations Supported

Python

Integrations Supported

Python

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Towhee

Company Website

towhee.io

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

blogs.windows.com/windowsexperience/2025/06/23/introducing-mu-language-model-and-how-it-enabled-the-agent-in-windows-settings/

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Categories and Features

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