An affordable, efficient AI solution for diverse text and image tasks.
MAI-DS-R1 is a DeepSeek-R1 reasoning model that has been post-trained by the Microsoft AI team to fill in information gaps in the previous version of the model and improve its harm protections while maintaining R1 reasoning capabilities.
Microsoft Research's EvoDiff is a diffusion modeling framework capable of generating highfidelity, diverse, and novel proteins with the option of conditioning according to sequence constraints. Because it operates in the universal protein design space, EvoDiff can unconditionally sample divers
State-of-the-art open-weight reasoning model.
Lightweight math reasoning model optimized for multi-step problem solving
3.8B parameters Small Language Model outperforming larger models in reasoning, math, coding, and function-calling
First small multimodal model to have 3 modality inputs (text, audio, image), excelling in quality and efficiency
Phi-4 14B, a highly capable model for low latency scenarios.
Adapted AI model for financial reports analysis based on Phi-4
Adapted AI model for supply chain trade regulations based on Phi-4
Muse is a World and Human Action Model (WHAM), a generative model of gameplay (visuals and/or controller actions).
A 7B parameters model, proves better quality than Phi-3-mini, with a focus on high-quality, reasoning-dense data.
Azure AI Language Azure AI Language is a cloudbased service designed to help you easily get insights from unstructured text data. It uses a combination of SLMs and LLMs, including taskoptimized decoder models and encoder models, for Language AI solutions. It provides premium quality at an affor
Model Summary Phi3 Vision is a lightweight, stateoftheart open multimodal model built upon datasets which include synthetic data and filtered publicly available websites with a focus on very highquality, reasoning dense data both on text and vision. The model belongs to the Phi3 model
The TamGen is a 100 millionparameter model that can generate compounds based on the input protein information. TamGen is pretrained on 10 million compounds from PubChem and finetuned on CrossDocked and PDB datasets. We evaluate TamGen on existing benchmarks and achieve top performance. Furthermor
Orca 2 is a finetuned version of LLAMA2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 pap
A 14B parameters model, proves better quality than Phi-3-mini, with a focus on high-quality, reasoning-dense data.
State-of-the-art open-weight reasoning model.
Azure AI Translator Azure AI Translator, a part of the Azure AI services, is a cloudbased neural machine translation service that enables businesses to translate text and documents across multiple languages in real time and in batches. The service also offers customization options, enabling busi
This model is an optimized version of Phi4reasoning to enable local inference on CPUs. This model uses RTN quantization. Model Description Developed by: Microsoft Model type: ONNX License: MIT Model Description: This is a conversion of the Phi4reasoning for local infer
Learn more: \[original model announcement\] DeepSeekR1DistilledNPUOptimized is a downloadable package of DeepSeekR1DistilledQwen1.5B that is specifically optimized for the Neural Processing Unit (NPU). NPU optimized models let develo
Biomedical image analysis is fundamental for biomedical discovery in cell biology, pathology, radiology, and many other biomedical domains. 3D medical images such as CT and MRI play unique roles in clinical practices. MedImageParse 3D is a foundation model for imaging parsing that can jointly co
Biomolecular Emulator (BioEmu) is a deep learning model that, given a protein sequence, can sample thousands of statistically independent structures from the protein structure ensemble per hour on a single graphics processing unit. By leveraging novel training methods and vast data of protein st
Refresh of Phi-3-vision model.
A new mixture of experts model
This model is an optimized version of Phi4reasoning to enable local inference on GPUs. This model uses RTN quantization. Model Description Developed by: Microsoft Model type: ONNX License: MIT Model Description: This is a conversion of the Phi4reasoning for local infer
Lightweight math reasoning model optimized for multi-step problem solving
LLaVAMed v1.5, using mistralai/Mistral7BInstructv0.2 as LLM for a better commercial license Large Language and Vision Assistant for bioMedicine (i.e., “LLaVAMed”) is a large language and vision model trained using a curriculum lear
Description The adapted AI model for financial reports analysis (preview) is a state\of\the\art small language model (SLM) based on the Phi\3\small\128k architecture, designed specifically for analyzing financial reports. It has been fine\tuned on a few hundred million tokens derived fro
Description Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles[^1],[^2],[^3]. Previous models often rely predominantly on tilelevel predictions, which can overlook critical slidelevel context and spatial dependen
Orca 2 is a finetuned version of LLAMA2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 pap
Biomedical image analysis is fundamental for biomedical discovery in cell biology, pathology, radiology, and many other biomedical domains. MedImageParse is a biomedical foundation model for imaging parsing that can jointly conduct segmentation, detection, and recognition across 9 imaging modalities
The Swin Transformer V2 model is a type of Vision Transformer, pretrained on ImageNet21k with a resolution of 192x192, is introduced in the <a href="https://arxiv.org/abs/2111.09883" target="blank"researchpaper</a titled "Swin Transformer V2: Scaling Up Capacity and Resolution" authored by Liu
BiomedCLIP is a biomedical visionlanguage foundation model that is pretrained on PMC15M, a dataset of 15 million figurecaption pairs extracted from biomedical research articles in PubMed Central, using contrastive learning. It uses PubMedBERT as the text encoder and Vision Transformer as the imag
Same Phi-3-medium model, but with a larger context size for RAG or few shot prompting.
This model is an optimized version of Phi4reasoning to enable local inference on CUDA GPUs. This model uses RTN quantization. Model Description Developed by: Microsoft Model type: ONNX License: MIT Model Description: This is a conversion of the Phi4reasoning for local
Overview The CXRReportGen model utilizes a multimodal architecture, integrating a BiomedCLIP image encoder with a Phi3Mini text encoder to help an application interpret complex medical imaging studies of chest Xrays. CXRReportGen follows the same framework as [MAIRA2](https://www.microsoft
Model Description Model card for RADDINO Model description RADDINO is a vision transformer model trained to encode chest Xrays using the selfsupervised learning method DINOv2. RADDINO is described in detail in [RADDINO: Exploring Scalab
Microsoft Phi2 The phi2 is a language model with 2.7 billion parameters. The phi2 model was trained using the same data sources as phi1, augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed a
Azure AI Content Understanding Introduction Azure AI Content Understanding empowers you to transform unstructured multimodal data—such as text, images, audio, and video—into structured, actionable insights. By streamlining content processing with advanced AI techniques like schema extraction
Same Phi-3-mini model, but with a larger context size for RAG or few shot prompting.
Azure AI Vision Introduction The Azure AI Vision service gives you access to advanced algorithms that process images and videos and return insights based on the visual features and content you are interested in. Azure AI Vision can power a diverse set of scenarios, including digital asset man
Same Phi-3-small model, but with a larger context size for RAG or few shot prompting.
Most medical imaging AI today is narrowly built to detect a small set of individual findings on a single modality like chest Xrays. This training approach is data and computationally inefficient, requiring ~612 months per finding1, and often fails to generalize in real world environments. By furt
This model is an optimized version of Phi4minireasoning to enable local inference on QNN NPUs. This model uses QuaRot and GPTQ quantization. Model Description Developed by: Microsoft Model type: ONNX License: MIT Model Description: This is a conversion of the Phi4mini
Aurora is a machine learning model that can predict general environmental variables.
Tiniest member of the Phi-3 family. Optimized for both quality and low latency.
Azure AI Speech Introduction The Speech service provides speech to text and text to speech capabilities with a Speech resource. You can transcribe speech to text with high accuracy, produce naturalsounding text to speech voices, translate spoken audio, and use speaker recognition during conv
MatterSim is a largescale pretrained deep learning model for efficient materials emulations and property predictions. MatterSim is a deep learning model for general materials design tasks. It supports efficient atomistic simulations at firstprinciples level and accurate prediction of broad materi
Description The adapted AI model for supply chain trade regulations analysis (preview) is a 3\.8B parameter, lightweight, state\of\the\art open model, trained using synthetic supply chain domain\specific datasets, focused on trade regulations. The model is fine\tuned on the base model, P
Refresh of Phi-3-mini model.