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README.md

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 # Doc / guide: https://huggingface.co/docs/hub/model-cards
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-This is the official checkpoint for MonoGPT, as featured in the paper [Dual Modalities of Text: Visual and Textual Generative Pre-training](https://arxiv.org/abs/2404.10710). For more details on how to use it, please visit the [GitHub page](https://github.com/ernie-research/pixelgpt).
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+ <a href="https://2024.emnlp.org/" target="_blank"> <img alt="EMNLP 2024" src="https://img.shields.io/badge/Proceedings-EMNLP2024-red" /> </a>
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+This repository contains the official checkpoint for PixelGPT, as presented in the paper [Autoregressive Pre-Training on Pixels and Texts (EMNLP 2024)](https://arxiv.org/pdf/2404.10710). For detailed instructions on how to use the model, please visit our [GitHub page](https://github.com/ernie-research/pixelgpt/).
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 ## Model Description
-[More Information Needed]
+MonoGPT is an autoregressive language model pre-trained on the dual modality of both pixels and texts without relying on the pixel-text paired data. By processing documents as visual data (pixels), the model learns to predict both the next token and the next image patch in a sequence, enabling it to handle visually complex tasks in different modalities. 
 
 ## Citation
 ```
-@article{chai2024dual,
-  title={Dual Modalities of Text: Visual and Textual Generative Pre-training},
-  author={Chai, Yekun and Liu, Qingyi and Xiao, Jingwu and Wang, Shuohuan and Sun, Yu and Wu, Hua},
-  journal={arXiv preprint arXiv:2404.10710},
-  year={2024}
+@misc{chai2024autoregressivepretrainingpixelstexts,
+  title = {Autoregressive Pre-Training on Pixels and Texts},
+  author = {Chai, Yekun and Liu, Qingyi and Xiao, Jingwu and Wang, Shuohuan and Sun, Yu and Wu, Hua},
+  year = {2024},
+  eprint = {2404.10710},
+  archiveprefix = {arXiv},
+  primaryclass = {cs.CL},
+  url = {https://arxiv.org/abs/2404.10710},
 }
 ```