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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 (with NPU and GPU support coming next). The first release of bitnet.cpp is to support inference on CPUs. bitnet.cpp achieves speedups of 1.37x to 5.07x on ARM CPUs, with larger models experiencing greater perf

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Garnet is a new remote cache-store from Microsoft Research, that offers several unique benefits: Garnet adopts the popular RESP wire protocol as a starting point, which makes it possible to use Garnet from unmodified Redis clients available in most programming languages of today, such as StackExchange.Redis in C#. Garnet offers much better throughput and scalability with many client connections an

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The Artificial Intelligence Controller Interface (AICI) lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Controllers are flexible programs capable of implementing constrained decoding, dynamic editing of prompts and generated text, and coordinating execution across multiple, parallel generations. Controllers incorporate custom logic during t

📅 2024-07-06: We have a New Release for v1.0.0!. You can check out our documentation. We welcome your contributions and feedback! 📅 2024-06-28: We are thrilled to announce that our official introduction video is now available on YouTube! 📅 2024-06-25: New Release for v0.2.1! We are excited to announce the release of version 0.2.1! This update includes several new features and improvements: Host

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"Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine" (H. Nori, Y. T. Lee, S. Zhang, D. Carignan, R. Edgar, N. Fusi, N. King, J. Larson, Y. Li, W. Liu, R. Luo, S. M. McKinney, R. O. Ness, H. Poon, T. Qin, N. Usuyama, C. White, E. Horvitz 2023) @article{nori2023can, title={Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medici

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📚 Cite paper. 🔥 Mar 26: Andrew Ng gave a shoutout to AutoGen in What's next for AI agentic workflows at Sequoia Capital's AI Ascent. 🔥 Mar 3: What's new in AutoGen? 📰Blog; 📺Youtube. 🔥 Mar 1: the first AutoGen multi-agent experiment on the challenging GAIA benchmark achieved the No. 1 accuracy in all the three levels. 🎉 Jan 30: AutoGen is highlighted by Peter Lee in Microsoft Research Forum

This repo is a collection of Rust crates that enable developers to develop Windows Drivers in Rust. It is the intention to support both WDM and WDF driver development models. This repo contains the following crates: wdk-build: A library to configure a Cargo build script for binding generation and downstream linking of the WDK (Windows Driver Kit). While this crate is written to be flexible with di

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#54546 explored one approach of enabling dual ESM/CJS emit for packages with tsc alone. The idea was that instead of determining the module format of .ts files by looking for package.json files in its ancestor directories, we would look for them starting at the directory where that file’s .js output would be emitted (a subdirectory of outDir). That way, two tsconfig files could point to two differ

This repository contains a set of design patterns illustrating how to effectively build Agentic solutions powered by LLMs (Large Language Models) in Azure. Agentic AI systems are designed to autonomously pursue complex goals and workflows with limited direct human supervision. These systems act as independent agents, making decisions and performing tasks autonomously. The main capabilities of Agen

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TypeChat is a library that makes it easy to build natural language interfaces using types. Building natural language interfaces has traditionally been difficult. These apps often relied on complex decision trees to determine intent and collect the required inputs to take action. Large language models (LLMs) have made this easier by enabling us to take natural language input from a user and match t

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