Microsoft
Open source projects and samples from Microsoft
Microsoft GitHub Findings
Employee Self-Service Agent Developer Kit
Customize your Employee Self-Service (ESS) agent using GitHub Copilot
A monorepo of solutions, samples, and tooling for the Microsoft Employee Self-Service (ESS) agent built on Microsoft Copilot Studio.
This repo is intended as an example or learning tool. It is not a Microsoft product or a supported service. See SUPPORT.md for the support model and SECURITY.md for reporting security issues.
Skill Recorder
Desktop app that records your on-screen work session and uses the GitHub Copilot CLI to reconstruct it as an intent + ordered steps, then builds a reusable Skill or Automation for Microsoft Scout, Microsoft Copilot Cowork, or Copilot Studio.https://github.com/microsoft/skill-recorder
bitnet.cpp
Official inference framework for 1-bit LLMs
bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels that support fast and lossless inference of 1.58-bit models on CPU and GPU (NPU support coming next).
Try it out via this online demo, or build and run it on your own CPU or GPU.
bitnet.cpp achieves speedups of 1.37x to 5.07x on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by 55.4% to 70.0%, further boosting overall efficiency. On x86 CPUs, speedups range from 2.37x to 6.17x with energy reductions between 71.9% to 82.2%. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. Please refer to the technical report for more details.
Try it out via this online demo, or build and run it on your own CPU or GPU.
bitnet.cpp achieves speedups of 1.37x to 5.07x on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by 55.4% to 70.0%, further boosting overall efficiency. On x86 CPUs, speedups range from 2.37x to 6.17x with energy reductions between 71.9% to 82.2%. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. Please refer to the technical report for more details.
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