---
base_model:
- google/siglip-so400m-patch14-384
language:
- en
- zh
license: apache-2.0
pipeline_tag: image-feature-extraction
---

# Oryx-ViT

## Model Summary

The Oryx-ViT model is trained on 200M data and can seamlessly and efficiently process visual inputs with arbitrary spatial sizes and temporal lengths.  It is described in the paper [Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution](https://arxiv.org/abs/2409.12961).

- **Repository:** https://github.com/Oryx-mllm/Oryx
- **Project Page:** https://oryx-mllm.github.io
- **Languages:** English, Chinese


### Model Architecture

- **Architecture:** SigLip
- **Data:** a mixture of 200M data, 2 epoch
- **Precision:** BFloat16

#### Hardware & Software

- **Hardware:** 64 * NVIDIA Tesla A100
- **Orchestration:** HuggingFace Trainer
- **Code:** Pytorch

## Citation

```bibtex
@article{liu2024oryx,
title={Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution},
author={Liu, Zuyan and Dong, Yuhao and Liu, Ziwei and Hu, Winston and Lu, Jiwen and Rao, Yongming},
journal={arXiv preprint arXiv:2409.12961},
year={2024}
}
```