Update README.md

README.md

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 🤗 **LLM-Drop** hosts research artifacts for efficient foundation models, with a focus on large language models and unified multimodal models.
 
-Our work studies how modern foundation models can be made more efficient while preserving their core capabilities. This page collects model weights, code links, and related resources from our research projects.
+Our work studies how modern foundation models can be made more efficient while preserving their core capabilities. This page collects model weights, code links, project pages, and related resources from our research projects.
 
 ## 📌 Projects
 
@@ -28,6 +28,7 @@ TMLR 2026
 
 **Demystifying When Pruning Works via Representation Hierarchies**
 
+- 🌐 Project Page: https://case-lab-umd.github.io/Pruning-on-Representations/
 - 📄 Paper: https://arxiv.org/abs/2603.24652
 - 💻 Code: https://github.com/CASE-Lab-UMD/Pruning-on-Representations
 
@@ -35,8 +36,12 @@ TMLR 2026
 
 **Understanding and Harnessing Sparsity in Unified Multimodal Models**
 
+- 🌐 Project Page: https://shwai-he.github.io/SparseUnifiedModel/
 - 📄 Paper: https://huggingface.co/papers/2512.02351
 - 💻 Code: https://github.com/Shwai-He/SparseUnifiedModel
+- 🤗 Models:
+  - https://huggingface.co/LLM-Drop/BAGEL-MoE-7B-GEN-16to8
+  - https://huggingface.co/LLM-Drop/BAGEL-MoE-7B-GEN-32to16
 
 ## 📬 Contact
 
@@ -44,4 +49,3 @@ For questions or collaborations, please contact:
 
 - Shwai He: shwaihe@umd.edu
 - Guoheng Sun: ghsun@umd.edu
-