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Introducing Mu language model and how it enabled the agent in Windows Settings
- The Mu language model has been introduced to power the agent in Windows Settings, providing high-performance natural language input processing efficiently on NPUs.
- Mu, a 330M encoder-decoder language model, offers efficiency benefits by reusing latent representations, resulting in lower latency and higher throughput on specialized hardware.
- Mu's design optimization for NPUs includes weight sharing, hardware-aware operations, and efficient transformer upgrades, enhancing performance on edge devices.
- The Mu model underwent training using advanced techniques like warmup-stable-decay schedules and Muon optimizer, achieving strong accuracy and faster inference within edge-device constraints.
- Mu showed remarkable performance on tasks like SQUAD, CodeXGlue, and Windows Settings agent, demonstrating efficiency despite its micro-size compared to Phi models.
- Model quantization techniques, such as Post-Training Quantization, enabled Mu to run efficiently on Copilot+ PCs' NPUs while maintaining accuracy and reducing memory footprint.
- Mu was finely tuned for the Windows Settings agent, meeting quality objectives with response times under 500 milliseconds and integrating seamlessly into the user interface for system setting changes.
- The agent in Settings leverages Mu's capabilities to handle a wide range of user queries effectively, focusing on multi-word inputs to provide high precision actionable responses.
- The challenge of managing extensive Windows settings was addressed by refining training data to prioritize common settings and handling ambiguous user queries effectively.
- The model's performance in the Windows Settings agent scenario was optimized through fine-tuning with synthetic approaches, metadata tuning, noise injection, and smart sampling.
- The Windows Insiders program welcomes feedback on the agent in Settings as refinements and enhancements continue to improve user experience.
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