Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
This dataset is the Version 2.0 of the FStar Data Set. This dataset’s primary objective is to train and evaluate Proof-oriented Programming with AI (PoPAI, in short). Given a specification of a program and proof…
This dataset contains programs and proofs in F* proof-oriented programming language. The data, proposed in Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming, is an archive of source code, build artifacts, and metadata assembled from eight…
This is a codebase to perform privacy-preserving in-context learning with differentially private few-shot generation.
This repository contains the code for the Eureka ML Insights, a framework for standardizing evaluations of large foundation models, beyond single-score reporting and rankings. The framework is designed to help researchers and practitioners run reproducible evaluations…
A self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superior models. rStar decouples reasoning into a self-play mutual generation-discrimination process.
Vector Post-Training Quantization (VPTQ) is a novel Post-Training Quantization method that leverages Vector Quantization to high accuracy on LLMs at an extremely low bit-width (
EASIER is a domain specific language embedded in PyTorch to automatically scale physical simulations up and out. It just-in-time (JIT) distributes tensor dataflows that describe physical simulations to any number of workers and compiles them…
Developed by Microsoft Research, BitNet b1.58 2B4T is the first open-source, native 1-bit large language model (LLM) in which every parameter is ternary (i.e., -1, 0, 1), at a 2-billion parameter scale. Trained on a…
