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What is SmolLM2?

SmolLM2 features a sophisticated range of compact language models designed for effective on-device operations. This assortment includes models with various parameter counts, such as a substantial 1.7 billion, alongside more efficient iterations at 360 million and 135 million parameters, which guarantees optimal functionality on devices with limited resources. The models are particularly adept at text generation and have been fine-tuned for scenarios that demand quick responses and low latency, ensuring they deliver exceptional results in diverse applications, including content creation, programming assistance, and understanding natural language. The adaptability of SmolLM2 makes it a prime choice for developers who wish to embed powerful AI functionalities into mobile devices, edge computing platforms, and other environments where resource availability is restricted. Its thoughtful design exemplifies a dedication to achieving a balance between high performance and user accessibility, thus broadening the reach of advanced AI technologies. Furthermore, the ongoing development of such models signals a promising future for AI integration in everyday technology.

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

Hugging Face
Locally AI
Mirai
Private Mind
Runpod

Integrations Supported

Hugging Face
Locally AI
Mirai
Private Mind
Runpod

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Open source
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

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

Hugging Face

Date Founded

2016

Company Location

United States

Company Website

huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9

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/

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