update model card

README.md

@@ -1,3 +1,36 @@
 ---
-license: mit
+base_model:
+- PrincetonPLI/Eagle-X2-Llama3-8B
+library_name: transformers
+license: cc-by-nc-sa-4.0
+pipeline_tag: image-text-to-text
 ---
+
+# Model Card for Eagle-X2-Llama3-8B-ConsecutiveTableReadout-Mix-160k
+This model follows the adapter-based VLM structure from [LLaVA](https://github.com/haotian-liu/LLaVA) and [Eagle](https://github.com/NVlabs/EAGLE). This model uses [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as the base LLM and CLIP-448 (based on [CLIP-336](openai/clip-vit-large-patch14-336)) and [ConvNeXt](https://github.com/facebookresearch/ConvNeXt) as the visual encoders.
+
+## Training Details
+We trained [Eagle-X2-Llama3-8B](https://huggingface.co/PrincetonPLI/Eagle-X2-Llama3-8B) on 160k examples of **Mix** supervision on Consecutive Table Readout.
+
+## Citation
+Paper: [Generalizing from SIMPLE to HARD Visual Reasoning](https://arxiv.org/abs/2501.02669)
+```
+@misc{park2025generalizingsimplehardvisual,
+      title={Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?}, 
+      author={Simon Park and Abhishek Panigrahi and Yun Cheng and Dingli Yu and Anirudh Goyal and Sanjeev Arora},
+      year={2025},
+      eprint={2501.02669},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2501.02669}, 
+}
+```
+
+## Contact
+Simon Park, Princeton University
+
+Abhishek Panigrahi, Princeton University
+
+Yun Cheng, Princeton University
+
+{juhyunp, ap34, yc6206} 'at' princeton 'dot' edu