diff --git a/README.md b/README.md
index 88f33bb..0d31c4f 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 <div align="center">
 <h1><img src="./assets/fire.svg" width=30, height=30> 
-𝐓𝐞𝐱𝐓𝐞𝐥𝐥𝐞𝐫 <img src="./assets/fire.svg" width=30, height=30> </h1>
+𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛 <img src="./assets/fire.svg" width=30, height=30> </h1>
 
 <p align="center">
 English | <a href="./assests/README_zh.md">中文版本</a>
@@ -23,7 +23,7 @@ python=3.10
 
 pytorch
 
-> Note: CUDA version >= 12.0 have been fully tested.
+> Note: Only CUDA version >= 12.0 have been fully tested, so we recommend using CUDA version>=12.0
 
 ## Getting Started
 
@@ -33,7 +33,7 @@ pytorch
     git clone https://github.com/OleehyO/TexTeller
     ```
 
-2. After [pytorch installation](https://pytorch.org/get-started), install the required packages:
+2. After [pytorch installation](https://pytorch.org/get-started/locally/#start-locally), install the required packages:
 
     ```bash
     pip install -r requirements.txt
@@ -47,7 +47,7 @@ pytorch
     #+e.g. python inference.py -img "./img.jpg" -cuda
     ```
 
-    > chekpoints will be downloaded in your first run.
+    > Checkpoints will be downloaded in your first run.
 
 ## Web Demo
 
@@ -57,9 +57,9 @@ You can also run the web demo by navigating to the `TexTeller/src` directory and
 ./start_web.sh
 ```
 
-Then go to `http://localhost:8000` in your browser to run TexTeller in the web.
+Then go to `http://localhost:8501` in your browser to run TexTeller in the web.
 
-> You can change the default settings in start_web.sh, such as inference with GPU(e.g. `USE_CUDA=True`) or increase the number of beams(e.g. `NUM_BEAM=3`) for higher accuracy.
+> You can change the default settings in `start_web.sh`, such as inference with GPU(e.g. `USE_CUDA=True`) or increase the number of beams(e.g. `NUM_BEAM=3`) for higher accuracy.
 
 ## API
 
@@ -82,13 +82,13 @@ You can pass the following arguments to the `serve.py` script to get custom infe
 | `--ncpu_per_replica` | Number of CPU cores to use per replica, *default is 1*. |
 | `--ngpu_per_replica` | Number of GPUs to use per replica, *default is 1*. You can set this to 0~1 to run multiple replicas on a single GPU(if --num_replicas 2, --ngpu_per_replica 0.7, then 2 gpus are required) |
 
-> Client demo can be found in `TexTeller/client/demo.py`.
+> Client demo can be found in `TexTeller/client/demo.py`, you can refer to `demo.py` to send requests to the server.
 
 ## Training
 
 ### Dataset
 
-We provide a dataset example in `TexTeller/src/models/ocr_model/train/dataset`, and you can place your own images in the `images` directory and annotate the corresponding formula for each image in `formulas.jsonl`
+We provide a dataset example in `TexTeller/src/models/ocr_model/train/dataset`, and you can place your own images in the `images` directory and annotate the corresponding formula for each image in `formulas.jsonl`.
 
 After the dataset is ready, you should **change the `DIR_URL` variable** in `.../dataset/loader.py` to the path of your dataset.
 
@@ -96,7 +96,7 @@ After the dataset is ready, you should **change the `DIR_URL` variable** in `...
 
 If you are using a different dataset, you may need to retrain the tokenizer to match your specific vocabulary. After setting up the dataset, you can do this by:
 
-1. Change the line `new_tokenizer.save_pretrained('./your_dir_name')` in `TexTeller/src/models/ocr_model/tokenizer/train.py` to your desired output directory name.`
+1. Change the line `new_tokenizer.save_pretrained('./your_dir_name')` in `TexTeller/src/models/ocr_model/tokenizer/train.py` to your desired output directory name.
     > To use a different vocabulary size, you should modify the `VOCAB_SIZE` parameter in the `TexTeller/src/models/globals.py`.
 
 2. Running the following command **under `TexTeller/src` directory**:
@@ -114,6 +114,7 @@ python -m models.ocr_model.train.train
 ```
 
 You can set your own tokenizer and checkpoint path(or fine-tune the default model checkpoint if you don't use your own tokenizer while keeping the same model architecture) in `TexTeller/src/models/ocr_model/train/train.py`.
+> Please refer to `train.py` for more details.
 
 Model architecture and training hyperparameters can be adjusted in `TexTeller/src/globals.py` and `TexTeller/src/models/ocr_model/train/train_args.py`.
 
diff --git a/assets/README_zh.md b/assets/README_zh.md
new file mode 100644
index 0000000..d8a9e59
--- /dev/null
+++ b/assets/README_zh.md
@@ -0,0 +1,162 @@
+<div align="center">
+<h1><img src="./fire.svg" width=30, height=30> 
+𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛 <img src="./fire.svg" width=30, height=30> </h1>
+
+<p align="center">
+<a href="../README.md">English</a> | 中文版本
+</p>
+
+<p align="center">
+  <img src="./web_demo.gif" alt="TexTeller_demo" width=800>
+</p>
+
+</div>
+
+TexTeller是一个基于ViT的端到端公式识别模型，可以把图片转换为对应的latex公式
+
+TexTeller用了550K的图片-公式对进行训练(数据集可以在[这里](https://huggingface.co/datasets/OleehyO/latex-formulas)获取)，相比于[LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)(使用了一个100K的数据集)，TexTeller具有更强的泛化能力以及更高的精确度，可以覆盖你大部分的使用场景。
+
+> 我们马上就会发布一个使用5.5M数据集进行训练的TexTeller checkpoint
+
+## 前置条件
+
+python=3.10
+
+pytorch
+
+> 注意: 只有CUDA版本>= 12.0被完全测试过，所以最好使用>= 12.0的CUDA版本
+
+
+## Getting Started
+
+1. 克隆本仓库:
+
+    ```bash
+    git clone https://github.com/OleehyO/TexTeller
+    ```
+
+2. [安装pytorch](https://pytorch.org/get-started/locally/#start-locally)后，再安装本项目的依赖包:
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3. 进入`TexTeller/src`目录，在终端运行以下命令进行推理:
+
+    ```bash
+    python inference.py -img "/path/to/image.{jpg,png}" 
+    # use -cuda option to enable GPU inference
+    #+e.g. python inference.py -img "./img.jpg" -cuda
+    ```
+
+    > 第一次运行时会在hugging face上下载所需要的checkpoints
+
+## FAQ：无法连接到Hugging Face
+
+默认情况下，会在Hugging Face中下载模型权重，**如果你的远端服务器无法连接到Hugging Face**，你可以通过以下命令进行加载：
+
+1. 安装huggingface hub包
+
+    ```bash
+    pip install -U "huggingface_hub[cli]"
+    ```
+
+2. 在能连接Hugging Face的机器上下载模型权重:
+
+    ```bash
+    huggingface-cli download OleehyO/TexTeller --include "*.json" "*.bin" "*.txt" --repo-type model --local-dir "your/dir/path"
+    ```
+
+3. 把包含权重的目录上传远端服务器，然后把`TexTeller/src/models/ocr_model/model/TexTeller.py`中的`REPO_NAME = 'OleehyO/TexTeller'`修改为`REPO_NAME = 'your/dir/path'`
+
+如果你还想在训练模型时开启evaluate，你需要提前下载metric脚本并上传远端服务器：
+
+1. 在能连接Hugging Face的机器上下载metric脚本
+
+    ```bash
+    huggingface-cli download evaluate-metric/google_bleu --repo-type space --local-dir "your/dir/path"
+    ```
+
+2. 把这个目录上传远端服务器，并在`TexTeller/src/models/ocr_model/utils/metrics.py`中把`evaluate.load('google_bleu')`改为`evaluate.load('your/dir/path')`
+
+## Web Demo
+
+要想启动web demo，你需要先进入 `TexTeller/src` 目录，然后运行以下命令
+
+```bash
+./start_web.sh
+```
+
+然后在浏览器里输入`http://localhost:8501`就可以看到web demo
+
+> 你可以改变`start_web.sh`的默认配置， 例如使用GPU进行推理(e.g. `USE_CUDA=True`) 或者增加beams的数量(e.g. `NUM_BEAM=3`)来获得更高的精确度
+
+## API
+
+我们使用[ray serve](https://github.com/ray-project/ray)来对外提供一个TexTeller的API接口，通过使用这个接口，你可以把TexTeller整合到自己的项目里。要想启动server，你需要先进入`TexTeller/src`目录然后运行以下命令:
+
+```bash
+python serve.py  # default settings
+```
+
+你可以给`serve.py`传递以下参数来改变server的推理设置(e.g. `python serve.py --use_gpu` 来启动GPU推理):
+
+| Argument | Description |
+| --- | --- |
+| `-ckpt` | Path to the checkpoint file to load, default is TexTeller pretrained model. |
+| `-tknz` | Path to the tokenizer, default is TexTeller tokenizer. |
+| `-port` | Port number to run the server on, *default is 8000*. |
+| `--use_gpu` | Whether to use GPU for inference. |
+| `--num_beams` | Number of beams to use for beam search decoding, *default is 1*. |
+| `--num_replicas` | Number of replicas to run the server on, *default is 1*. You can use this to get higher throughput. |
+| `--ncpu_per_replica` | Number of CPU cores to use per replica, *default is 1*. |
+| `--ngpu_per_replica` | Number of GPUs to use per replica, *default is 1*. You can set this to 0~1 to run multiple replicas on a single GPU(if --num_replicas 2, --ngpu_per_replica 0.7, then 2 gpus are required) |
+
+> 一个客户端demo可以在`TexTeller/client/demo.py`找到，你可以参考`demo.py`来给server发送请求
+
+## Training
+
+### Dataset
+
+我们在`TexTeller/src/models/ocr_model/train/dataset`目录中提供了一个数据集的例子，你可以把自己的图片放在`images`目录然后在`formulas.jsonl`中为每张图片标注对应的公式。
+
+准备好数据集后，你需要在`.../dataset/loader.py`中把 **`DIR_URL`变量改成你自己数据集的路径**
+
+### Retrain the tokenizer
+
+如果你使用了不一样的数据集，你可能需要重新训练tokenizer来得到一个不一样的字典。配置好数据集后，可以通过以下命令来训练自己的tokenizer：
+
+1. 在`TexTeller/src/models/ocr_model/tokenizer/train.py`中，修改`new_tokenizer.save_pretrained('./your_dir_name')`为你自定义的输出目录
+    > 如果要用一个不一样大小的字典(默认1W个token)，你需要在 `TexTeller/src/models/globals.py`中修改`VOCAB_SIZE`变量
+
+2. **在 `TexTeller/src` 目录下**运行以下命令:
+
+    ```bash
+    python -m models.ocr_model.tokenizer.train
+    ```
+
+### Train the model
+
+要想训练模型, 你需要在`TexTeller/src`目录下运行以下命令：
+
+```bash
+python -m models.ocr_model.train.train
+```
+
+你可以在`TexTeller/src/models/ocr_model/train/train.py`中设置自己的tokenizer和checkpoint路径（请参考`train.py`）。如果你使用了与TexTeller一样的架构和相同的字典，你还可以用自己的数据集来微调TexTeller的默认权重。
+
+在`TexTeller/src/globals.py`和`TexTeller/src/models/ocr_model/train/train_args.py`中，你可以改变模型的架构以及训练的超参数。
+
+> 我们的训练脚本使用了[Hugging Face Transformers](https://github.com/huggingface/transformers)库, 所以你可以参考他们提供的[文档](https://huggingface.co/docs/transformers/v4.32.1/main_classes/trainer#transformers.TrainingArguments)来获取更多训练参数的细节以及配置。
+
+## To-Do
+
+- [ ] 使用更大的数据集来训练模型(5.5M样本，即将发布)
+
+- [ ] 推理加速
+
+- [ ] ...
+
+## Acknowledgements
+
+Thanks to [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) which has brought me a lot of inspiration, and [im2latex-100K](https://zenodo.org/records/56198#.V2px0jXT6eA) which enriches our dataset.
diff --git a/src/models/ocr_model/model/TexTeller.py b/src/models/ocr_model/model/TexTeller.py
index 740ccc5..4b059bd 100644
--- a/src/models/ocr_model/model/TexTeller.py
+++ b/src/models/ocr_model/model/TexTeller.py
@@ -30,14 +30,14 @@ class TexTeller(VisionEncoderDecoderModel):
     
     @classmethod
     def from_pretrained(cls, model_path: str = None):
-        if model_path is None or model_path == cls.REPO_NAME:
+        if model_path is None or model_path == 'default':
             return VisionEncoderDecoderModel.from_pretrained(cls.REPO_NAME)
         model_path = Path(model_path).resolve()
         return VisionEncoderDecoderModel.from_pretrained(str(model_path))
 
     @classmethod
     def get_tokenizer(cls, tokenizer_path: str = None) -> RobertaTokenizerFast:
-        if tokenizer_path is None or tokenizer_path == cls.REPO_NAME:
+        if tokenizer_path is None or tokenizer_path == 'default':
             return RobertaTokenizerFast.from_pretrained(cls.REPO_NAME)
         tokenizer_path = Path(tokenizer_path).resolve()
         return RobertaTokenizerFast.from_pretrained(str(tokenizer_path))
diff --git a/src/start_web.sh b/src/start_web.sh
index 7d32eed..450dff2 100755
--- a/src/start_web.sh
+++ b/src/start_web.sh
@@ -1,9 +1,9 @@
 #!/usr/bin/env bash
 set -exu
 
-export CHECKPOINT_DIR="OleehyO/TexTeller"
-export TOKENIZER_DIR="OleehyO/TexTeller"
-export USE_CUDA=True  # True or False (case-sensitive)
-export NUM_BEAM=5
+export CHECKPOINT_DIR="default"
+export TOKENIZER_DIR="default"
+export USE_CUDA=False  # True or False (case-sensitive)
+export NUM_BEAM=1
 
 streamlit run web.py