Phi-Ground-Any
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Phi-mini-MoE-instruct
MicrosoftPhi-mini-MoE is a lightweight Mixture of Experts (MoE) model with 7.6B total parameters and 2.4B activated parameters. It is compressed and distilled from the base model shared by Phi-3.5-MoE and GRIN-MoE using the SlimMoE approach, then post-trained via supervised fine-tuning and direct preference optimization for instruction following and safety. The model is trained on Phi-3 synthetic data and filtered public documents, with a focus on high-quality, reasoning-dense content. It is part of the SlimMoE series, which includes a smaller variant, Phi-tiny-MoE, with 3.8B total and 1.1B activated parameters. The model is intended for commercial and research use in English. The model provides uses for general purpose AI systems and applications which require memory/compute constrained environments and latency bound scenarios. Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.
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View details ↗Phi-tiny-MoE-instruct
MicrosoftPhi-tiny-MoE is a lightweight Mixture of Experts (MoE) model with 3.8B total parameters and 1.1B activated parameters. It is compressed and distilled from the base model shared by Phi-3.5-MoE and GRIN-MoE using the SlimMoE approach, then post-trained via supervised fine-tuning and direct preference optimization for instruction following and safety. The model is trained on Phi-3 synthetic data and filtered public documents, with a focus on high-quality, reasoning-dense content. It is part of the SlimMoE series, which includes a larger variant, Phi-mini-MoE, with 7.6B total and 2.4B activated parameters. References: 📖 SlimMoE Paper 📖 Phi-3 Technical Report 📖 GRIN-MoE The model is intended for commercial and research use in English. The model provides uses for general purpose AI systems and applications which require memory/compute constrained environments and latency bound scenarios.
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View details ↗Promptist
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View details ↗prophetnet-large-uncased
MicrosoftPretrained weights for ProphetNet. ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction. ProphetNet is able to predict more future tokens with a n-stream decoder. The original implementation is Fairseq version at github repo. This pre-trained model can be fine-tuned on *sequence-to-sequence* tasks. The model could *e.g.* be trained on headline generation as follows:
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View details ↗prophetnet-large-uncased-cnndm
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View details ↗prophetnet-large-uncased-squad-qg
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View details ↗psirnet
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View details ↗rad-dino
MicrosoftRAD-DINO is a vision transformer model trained to encode chest X-rays using the self-supervised learning method DINOv2. RAD-DINO is described in detail in Exploring Scalable Medical Image Encoders Beyond Text Supervision (F. Pérez-García, H. Sharma, S. Bond-Taylor, et al., 2025). - **Developed by:** Microsoft Health Futures - **Model type:** Vision transformer - **License:** MIT - **Finetuned from model:** `dinov2-base`
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View details ↗rad-dino-maira-2
MicrosoftRAD-DINO-MAIRA-2 is a vision transformer model trained to encode chest X-rays using the self-supervised learning method DINOv2. RAD-DINO-MAIRA-2 is a variant of RAD-DINO, which is described in detail in RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision (F. Pérez-García, H. Sharma, S. Bond-Taylor, et al., 2024). RAD-DINO-MAIRA-2 is the version of RAD-DINO used in MAIRA-2: Grounded Radiology Report Generation (S. Bannur, K. Bouzid, et al., 2024). Relative to RAD-DINO, it was trained on more data. RAD-DINO-MAIRA-2 is shared for research purposes only. It is **not meant to be used for clinical practice**.
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View details ↗radedit
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View details ↗reacc-py-retriever
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View details ↗Reducio-VAE
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View details ↗renderformer-v1.1-swin-large
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View details ↗renderformer-v1-base
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View details ↗resnet-101
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View details ↗resnet-152
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View details ↗resnet-18
MicrosoftResNet model trained on imagenet-1k. It was introduced in the paper Deep Residual Learning for Image Recognition and first released in this repository. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. ResNet introduced residual connections, they allow to train networks with an unseen number of layers (up to 1000). ResNet won the 2015 ILSVRC & COCO competition, one important milestone in deep computer vision.
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View details ↗resnet-26
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View details ↗resnet-34
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View details ↗resnet-50
MicrosoftResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
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View details ↗rho-base
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View details ↗rho-fr3-duo
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View details ↗rho-libero
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