docs: update readme

README.md

@@ -94,9 +94,9 @@ Alternatively, for built-in high-precision model parsing capabilities, use:
 ```bash
 pip install magic-pdf[full-cpu]
 ```
-The high-precision models depend on detectron2, which requires a compiled installation. 
-If you need to compile it yourself, refer to https://github.com/facebookresearch/detectron2/issues/5114 
-Or directly use our pre-compiled wheel packages (limited to python 3.10):
+The high-precision models depend on detectron2, which requires a compiled installation.  
+If you need to compile it yourself, refer to https://github.com/facebookresearch/detectron2/issues/5114  
+Or directly use our pre-compiled wheel packages (limited to python 3.10):  
 ```bash
 pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
 ```
@@ -104,7 +104,7 @@ pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
 
 #### 2. Downloading model weights files
 
-For detailed references, please see below[how_to_download_models](docs/how_to_download_models_en.md)
+For detailed references, please see below [how_to_download_models](docs/how_to_download_models_en.md)
 
 After downloading the model weights, move the 'models' directory to a directory on a larger disk space, preferably an SSD.
 
@@ -130,9 +130,9 @@ In magic-pdf.json, configure "models-dir" to point to the directory where the mo
 ```bash
 magic-pdf pdf-command --pdf "pdf_path" --inside_model true
 ```
-After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".
-You can find the corresponding xxx_model.json file in the markdown directory. 
-If you intend to do secondary development on the post-processing pipeline, you can use the command:
+After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".  
+You can find the corresponding xxx_model.json file in the markdown directory.   
+If you intend to do secondary development on the post-processing pipeline, you can use the command:  
 ```bash
 magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
 ```
@@ -150,12 +150,12 @@ magic-pdf --help
 
 ##### CUDA
 
-You need to install the corresponding PyTorch version according to your CUDA version.
+You need to install the corresponding PyTorch version according to your CUDA version.  
+This example installs the CUDA 11.8 version.More information https://pytorch.org/get-started/locally/  
 ```bash
-# When using the GPU solution, you need to reinstall PyTorch for the corresponding CUDA version. This example installs the CUDA 11.8 version.
 pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
 ```
-Also, you need to modify the value of "device-mode" in the configuration file magic-pdf.json.
+Also, you need to modify the value of "device-mode" in the configuration file magic-pdf.json.  
 ```json
 {
   "device-mode":"cuda"
@@ -164,9 +164,8 @@ Also, you need to modify the value of "device-mode" in the configuration file ma
 
 ##### MPS
 
-For macOS users with M-series chip devices, you can use MPS for inference acceleration.
-You also need to modify the value of "device-mode" in the configuration file magic-pdf.json.
-
+For macOS users with M-series chip devices, you can use MPS for inference acceleration.  
+You also need to modify the value of "device-mode" in the configuration file magic-pdf.json.  
 ```json
 {
   "device-mode":"mps"

README_zh-CN.md

@@ -70,7 +70,7 @@ https://github.com/opendatalab/MinerU/assets/11393164/618937cb-dc6a-4646-b433-e3
 
 python >= 3.9
 
-推荐使用虚拟环境，以避免可能发生的依赖冲突，venv和conda均可使用。
+推荐使用虚拟环境，以避免可能发生的依赖冲突，venv和conda均可使用。  
 例如：
 ```bash
 conda create -n MinerU python=3.10
@@ -90,19 +90,19 @@ pip install magic-pdf
 ```bash
 pip install magic-pdf[full-cpu]
 ```
-高精度模型依赖于detectron2，该库需要编译安装，如需自行编译，请参考https://github.com/facebookresearch/detectron2/issues/5114
-或是直接使用我们预编译的whl包(仅限python 3.10)：
+高精度模型依赖于detectron2，该库需要编译安装，如需自行编译，请参考 https://github.com/facebookresearch/detectron2/issues/5114  
+或是直接使用我们预编译的whl包(仅限python 3.10)：  
 ```bash
 pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
 ```
 
 #### 2. 下载模型权重文件
 
-详细参考[如何下载模型文件](docs/how_to_download_models_zh_cn.md)
-下载后请将models目录移动到空间较大的ssd磁盘目录
+详细参考 [如何下载模型文件](docs/how_to_download_models_zh_cn.md)  
+下载后请将models目录移动到空间较大的ssd磁盘目录  
 
 #### 3. 拷贝配置文件并进行配置
-在仓库根目录可以获得[magic-pdf.template.json](magic-pdf.template.json)文件
+在仓库根目录可以获得 [magic-pdf.template.json](magic-pdf.template.json) 文件
 ```bash
 cp magic-pdf.template.json ~/magic-pdf.json
 ```
@@ -120,8 +120,8 @@ cp magic-pdf.template.json ~/magic-pdf.json
 ```bash
 magic-pdf pdf-command --pdf "pdf_path" --inside_model true
 ```
-程序运行完成后，你可以在"/tmp/magic-pdf"目录下看到生成的markdown文件，markdown目录中可以找到对应的xxx_model.json文件
-如果您有意对后处理pipeline进行二次开发，可以使用命令
+程序运行完成后，你可以在"/tmp/magic-pdf"目录下看到生成的markdown文件，markdown目录中可以找到对应的xxx_model.json文件  
+如果您有意对后处理pipeline进行二次开发，可以使用命令  
 ```bash
 magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
 ```
@@ -138,9 +138,9 @@ magic-pdf --help
 
 ###### CUDA
 
-需要根据自己的CUDA版本安装对应的pytorch版本
+需要根据自己的CUDA版本安装对应的pytorch版本  
+以下是对应CUDA 11.8版本的安装命令，更多信息请参考 https://pytorch.org/get-started/locally/  
 ```bash
-# 使用gpu方案时，需要重新安装对应cuda版本的pytorch，例子是安装CUDA 11.8版本的
 pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
 ```
 
@@ -152,9 +152,8 @@ pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https
 ```
 
 ###### MPS
-使用macOS(M系列芯片设备)可以使用MPS进行推理加速
-
-需要修改配置文件magic-pdf.json中"device-mode"的值
+使用macOS(M系列芯片设备)可以使用MPS进行推理加速  
+需要修改配置文件magic-pdf.json中"device-mode"的值  
 ```json
 {
   "device-mode":"mps"