Update README.md

README.md

@@ -1,30 +1,45 @@
----
-license: llama2
----
-
-# v-MLLM Model Card
-
-## Model details
-
-**Model type:**
-v-MLLM is an open-source MLLM trained on Visual-Modality Instruction (VIM) corpus, it can robustly follow the text-modality instructions and visual-modality instructions.
-
-**Model date:**
-v-MLLM-7B was trained on January 2024.
-
-**Github for more information:**
-https://github.com/VIM-Bench/VIM_TOOL
-
-## License
-v-MLLM is licensed under the LLAMA 2 Community License, 
-Copyright (c) Meta Platforms, Inc. All Rights Reserved.
-
-## Intended use
-**Primary intended uses:**
-The primary use of v-MLLM is research on multimodal large language models.
-
-**Primary intended users:**
-The primary intended users of the model are researchers in computer vision, natural language processing, machine learning, and artificial intelligence.
-
-## Training dataset
-- 846k VIM corpus based on LVIS-Instruct4V corpus.
+---
+license: llama2
+---
+
+# v-MLLM Model Card
+
+## Model details
+
+**Model type:**
+v-MLLM is an open-source MLLM trained on Visual-Modality Instruction (VIM) corpus, it can robustly follow the text-modality instructions and visual-modality instructions.
+
+**Model date:**
+v-MLLM-7B was trained on January 2024.
+
+**Github for more information:**
+https://github.com/VIM-Bench/VIM_TOOL
+
+## License
+v-MLLM is licensed under the LLAMA 2 Community License, 
+Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+
+## Intended use
+**Primary intended uses:**
+The primary use of v-MLLM is research on multimodal large language models.
+
+**Primary intended users:**
+The primary intended users of the model are researchers in computer vision, natural language processing, machine learning, and artificial intelligence.
+
+## Training dataset
+- 846k VIM corpus based on LVIS-Instruct4V corpus.
+
+# Citation
+
+Please kindly cite our paper if you find our resources useful:
+
+```
+@misc{lu2023vim,
+      title={VIM: Probing Multimodal Large Language Models for Visual Embedded Instruction Following}, 
+      author={Yujie Lu and Xiujun Li and William Yang Wang and Yejin Choi},
+      year={2023},
+      eprint={2311.17647},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```