Update README.md

README.md

@@ -1,3 +1,52 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- DongkiKim/Mol-LLaMA-Instruct
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+tags:
+- biology
+- chemistry
+- medical
+---
+
+# Mol-Llama-3-8B-Instruct
+[[Project Page](https://mol-llama.github.io/)] [[Paper](https://arxiv.org/abs/2502.13449)] [[GitHub](https://github.com/DongkiKim95/Mol-LLaMA)]
+
+This repo contains the weights of Mol-LLaMA including the LoRA weights and projectors, based on [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
+
+## Architecture
+![image.png](architecture.png)
+1) Molecular encoders: Pretrained 2D encoder ([MoleculeSTM](https://huggingface.co/chao1224/MoleculeSTM)) and 3D encoder ([Uni-Mol](https://huggingface.co/dptech/Uni-Mol-Models))
+2) Blending Module: Combining complementary information from 2D and 3D encoders via cross-attention
+3) Q-Former: Embed molecular representations into query tokens based on [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased)
+4) LoRA: Adapters for fine-tuning LLMs
+
+
+## Training Dataset
+
+Mol-LLaMA is trained on [Mol-LLaMA-Instruct](https://huggingface.co/datasets/DongkiKim/Mol-LLaMA-Instruct), to learn the fundamental characteristics of molecules with the reasoning ability and explanbility.
+
+## How to Use
+
+Please check out [the exemplar code for inference](https://github.com/DongkiKim95/Mol-LLaMA/blob/master/playground.py) in the Github repo.
+
+## Citation
+
+If you find our model useful, please consider citing our work.
+```
+@misc{kim2025molllama,
+    title={Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model},
+    author={Dongki Kim and Wonbin Lee and Sung Ju Hwang},
+    year={2025},
+    eprint={2502.13449},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+## Acknowledgements
+
+We appreciate [LLaMA](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), [3D-MoLM](https://huggingface.co/Sihangli/3D-MoLM), [MoleculeSTM](https://huggingface.co/chao1224/MoleculeSTM), [Uni-Mol](https://huggingface.co/dptech/Uni-Mol-Models) and [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased) for their open-source contributions.