chatbench-llama3-8b
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chatbench-mistral-7b
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View details ↗ClimaX
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View details ↗cocolm-base
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View details ↗cocolm-large
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View details ↗codebert-base
MicrosoftPretrained weights for CodeBERT: A Pre-Trained Model for Programming and Natural Languages. The model is trained on bi-modal data (documents & code) of CodeSearchNet This model is initialized with Roberta-base and trained with MLM+RTD objective (cf. the paper).
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View details ↗codebert-base-mlm
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View details ↗codeexecutor
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View details ↗CodeGPT-small-java
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View details ↗CodeGPT-small-java-adaptedGPT2
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View details ↗CodeGPT-small-py
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View details ↗CodeGPT-small-py-adaptedGPT2
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View details ↗codereviewer
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View details ↗CogACT-v2-RoboDojo
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View details ↗colipri
MicrosoftCOLIPRI is a 3D vision–language transformer model trained to encode chest CT scans and radiological reports. COLIPRI was trained using tens of thousands of chest CT scans and reports, without any annotations, using multiple objectives to learn strong joint representations of 3D images and text. The procedure is described in detail in Comprehensive language-image pre-training for 3D medical image understanding (Wald et al., ECCV 2026). The encoders be plugged to other models, or used independently or jointly for many downstream tasks, such as:
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View details ↗conditional-detr-resnet-50
MicrosoftConditional DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Conditional DETR for Fast Training Convergence by Meng et al. and first released in this repository. The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. You can use the raw model for object detection. See the model hub to look for all available Conditional DETR models.
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View details ↗cvt-13
MicrosoftCvT-13 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. Disclaimer: The team releasing CvT did not write a model card for this model so this model card has been written by the Hugging Face team. Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
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View details ↗cvt-13-384
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View details ↗cvt-13-384-22k
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View details ↗cvt-21
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View details ↗cvt-21-384
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View details ↗cvt-21-384-22k
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View details ↗cvt-w24-384-22k
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View details ↗Dayhoff-170m-GR
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