Microsoft Research

Turing-NLG (Natural Language Generation) was one of the largest language models at its release, with 17 billion parameters. Developed by Microsoft, it demonstrated strong performance on various language generation tasks and pushed the boundaries of model scale.

Swin Transformer is a hierarchical vision transformer that uses shifted windows for computing self-attention. Developed by Microsoft Research, it achieves excellent performance on image classification, object detection, and semantic segmentation while being efficient.

Phi-3 is Microsoft's family of small language models that achieve performance comparable to much larger models. The Phi-3-mini (3.8B parameters) matches the performance of models like GPT-3.5, demonstrating that smaller, well-trained models can be highly capable.