Update README.md

README.md

@@ -32,11 +32,11 @@ pretty_name: MOAT
 
 **MOAT** (**M**ultimodal model **O**f **A**ll **T**rades) is a challenging benchmark for large multimodal models (LMMs). It consists of vision language (VL) tasks that require the LMM to integrate several VL capabilities and engage in human-like generalist visual problem solving. Moreover, many tasks in **MOAT** focus on LMMs' capability to ground complex text and visual instructions, which is crucial for the application of LMMs in-the-wild. Developing on the VL capability taxonomies proposed in previous benchmark papers, we define 10 fundamental VL capabilities in **MOAT**. 
 
-Please check out our [GitHub repo](https://cambrian-yzt.github.io/MOAT/) for further information.
+Please check out our [GitHub repo](https://github.com/Cambrian-yzt/MOAT) for further information.
 
 ## Usage
 
-Please check out our [GitHub repo](https://cambrian-yzt.github.io/MOAT/) for detail usage.
+Please check out our [GitHub repo](https://github.com/Cambrian-yzt/MOAT) for detail usage.
 
 **Run Your Own Evaluation**
 
@@ -47,7 +47,7 @@ from datasets import load_dataset
 dataset = load_dataset("waltsun/MOAT", split='test')
 ```
 
-As some questions are formatted as interleaved text and image(s), we recommend referring to the `./inference/eval_API.py` file in our [GitHub repo](https://cambrian-yzt.github.io/MOAT/) for the correct way to query the LMM.
+As some questions are formatted as interleaved text and image(s), we recommend referring to the `./inference/eval_API.py` file in our [GitHub repo](https://github.com/Cambrian-yzt/MOAT) for the correct way to query the LMM.
 
 **Column Description**