Update README.md

README.md

@@ -10,6 +10,9 @@ metrics:
 - accuracy
 library_name: transformers
 ---
+
+[Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions](https://arxiv.org/abs/2412.08737)
+
 # Model Card for Euclid-convnext-large (Version on 12/05/2024)
 
 A multimodal large language models specifically trained for strong low-level geometric perception. 
@@ -23,11 +26,11 @@ Euclid is trained on 1.6M synthetic geometry images with high-fidelity question-
 It combines a ConvNeXt visual encoder with a Qwen-2.5 language model, connected through a 2-layer MLP multimodal connector.
 
 
-### Model Sources [optional]
+### Model Sources
 
 - **Repository:** https://github.com/euclid-multimodal/Euclid
-- **Paper:** [Paper Link]
-- **Demo:** [Demo Link if available]
+- **Paper:** https://arxiv.org/abs/2412.08737
+- **Demo:** https://euclid-multimodal.github.io/
 
 ## Uses
 
@@ -83,10 +86,9 @@ Performance on Geoperception benchmark tasks:
 
 If you find Euclid useful for your research and applications, please cite using this BibTeX:
 ```bibtex
-@misc{euclid,
-    title={Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions},
-    url={https://euclid-llm.github.io/},
-    author={Zhang, Jiarui and Liu, Ollie and Yu, Tianyu and Hu, Jinyi and Neiswanger, Willie},
-    month={December},
-    year={2024}
+@article{zhang2024euclid,
+  title={Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions},
+  author={Zhang, Jiarui and Liu, Ollie and Yu, Tianyu and Hu, Jinyi and Neiswanger, Willie},
+  journal={arXiv preprint arXiv:2412.08737},
+  year={2024}
 }