diff --git a/.gitignore b/.gitignore
index e65bc18..8c788e3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -120,4 +120,5 @@ test/
 *.tfevents.*
 *.sto
 *.a3m
-nogit/
+/nogit/
+/out/
diff --git a/README.md b/README.md
index 288e9f0..ad84b79 100644
--- a/README.md
+++ b/README.md
@@ -145,28 +145,26 @@ The output directory of running Uni-Fold contain the predicted structures in `*.
 
 ## Training Uni-Fold
 
-Training Uni-Fold relies on pre-calculated features of proteins. We provide a demo dataset in the [example data](example_data) folder. A larger dataset will be released soon.
-
 ### Demo case
 
 To start with, we provide a demo script to train the monomer/multimer system of Uni-Fold:
 
 ```bash
-bash train_monomer_demo.sh .
+bash train_monomer_demo.sh ./out
 ```
 
 and
 
 ```bash
-bash train_multimer_demo.sh .
+bash train_multimer_demo.sh ./out
 ```
 
-This command starts a training process on the [demo data](example_data) included in this repository. Note that this demo script only tests the correctness of package installation and does not reflect any true performances.
+These two commands start training processes on the [demo data](example_data) included in this repository. Note that this demo script only tests the correctness of package installation and does not reflect any true performances.
 
 
 ### Full training dataset download
 
-We thank [ModelScope](https://modelscope.cn/home) and [Volcengine](https://www.volcengine.com) for providing free hosts of the full training dataset. The downloaded dataset could be directly used to train Uni-Fold from-scratch.
+Training Uni-Fold relies on pre-calculated features of proteins. We thank [ModelScope](https://modelscope.cn/home) and [Volcengine](https://www.volcengine.com) for providing free hosts of the full training dataset. The downloaded dataset could be directly used to train Uni-Fold from-scratch. Notably, the full dataset is approximately 2TB in size, so make sure the local storage is capable (>3TB recommended).
 
 #### Download from ModelScope
 
@@ -175,7 +173,7 @@ Download the dataset from [modelscope](https://modelscope.cn/datasets/DPTech/Uni
 1. install modelscope
 
 ```bash
-pip3 install https://databot-algo.oss-cn-zhangjiakou.aliyuncs.com/maas/modelscope-1.0.0-py3-none-any.whl 
+pip3 install modelscope
 ```
 
 2. download the dataset with python
@@ -192,7 +190,8 @@ ds = MsDataset.load(dataset_name='Uni-Fold-Data', namespace='DPTech', split='tra
 # The data will be located at ${your_data_path}/modelscope/hub/datasets/downloads/DPTech/Uni-Fold-Data/master/*
 ```
 
-#### Download from Volcengine
+
+#### Download from Volcengine (recommended)
 
 1. install rclone