Improve model card: add paper/code links and update metadata

Hi! I'm Niels from the Hugging Face community science team. This PR improves your model card by:
- Linking the model to the original research paper.
- Adding a link to the GitHub repository.
- Updating the license to MIT as indicated in your repository.
- Removing `library_name: transformers` from the metadata, as this model uses a custom architecture that is not natively part of the `transformers` library. This ensures the Hub doesn't display incorrect automated usage snippets.

README.md

@@ -1,26 +1,38 @@
 ---
-license: cc-by-4.0
-library_name: transformers
+license: mit
 pipeline_tag: feature-extraction
 tags:
 - chemistry
 - multimodal
 ---
 
-# Example code
+# GLACIER: Graph-Language Alignment for Chemical Inference and Exploration using Representations
 
-``` Python
+GLACIER is a multimodal student-teacher foundation model designed for molecular property prediction. It integrates molecular graphs, SMILES strings, and physicochemical descriptors to learn rich molecular embeddings.
 
+- **Paper:** [GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction](https://huggingface.co/papers/2606.11382)
+- **Repository:** [https://github.com/eemokey/glacier](https://github.com/eemokey/glacier)
+
+## Sample Usage
+
+Since this model uses a custom architecture, you need to download the repository files to load the model.
+
+```python
 import torch 
 from huggingface_hub import snapshot_download
 import sys
+
+# Download the repository to access custom model code
 repo_dir = snapshot_download(repo_id="glacier-hf/GLACIER-100k-MiniMol")
 sys.path.append(repo_dir)
+
 from data.dataloader import SmilesMoleculeDataset, build_dataloader
 from glacier_student import Glacier
 
+# Load the pretrained GLACIER model
 model = Glacier.from_pretrained("glacier-hf/GLACIER-100k-MiniMol")
 
+# Prepare input data
 dataset = SmilesMoleculeDataset(smiles=["Cn1c(=O)c2c(ncn2C)n(C)c1=O"])
 dataloader = build_dataloader(dataset, batch_size=1)
 
@@ -28,24 +40,25 @@ model.eval()
 batch = next(iter(dataloader))
 with torch.no_grad():
     embedding = model(batch)
-embedding
-
+print(embedding)
 ```
 
+## GLACIER Model Files
+- `dataloader`: customized dataloader for multimodal learning
+- `encoders`: graph, text, and tabular encoders 
+- `fusion`: Finsler geometry-aware fusion method
+- `glacier_student`: GLACIER model backbone and contrastive loss
+- `utils`: miscellaneous helper functions 
 
-# GLACIER Model Files
-
-- dataloader: customized dataloader for multimodal learning
-- encoders: graph, text, and tabular encoders 
-- fusion: fusion method
-- glacier_student: GLACIER model backbone, contrastive loss
-- utils: miscellaneous helper functions 
-
-
-# Citation
-
-Emily Nguyen, Yongchan Hong, Harsh Toshniwal, Yan Liu, and Andreas
-Luttens. 2026. GLACIER: A Multimodal Student-Teacher Foundation Model
-for Molecular Property Prediction. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD ’26), August 09–13, 2026, Jeju Island, Republic of Korea. ACM, New York, NY, USA,
-17 pages. https://doi.org/10.1145/3770855.3819032
+## Citation
 
+```bibtex
+@inproceedings{nguyen2026glacier,
+  title={GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction},
+  author={Emily Nguyen and Yongchan Hong and Harsh Toshniwal and Yan Liu and Andreas Luttens},
+  booktitle={Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD ’26)},
+  year={2026},
+  publisher={ACM},
+  doi={10.1145/3770855.3819032}
+}
+```