fix: add horizontal rules for better section separation in documentation

docs/en/quick_start/docker_deployment.md

@@ -13,6 +13,8 @@ docker build -t mineru-sglang:latest -f Dockerfile .
 > The [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/global/Dockerfile) uses `lmsysorg/sglang:v0.4.8.post1-cu126` as the base image by default, supporting Turing/Ampere/Ada Lovelace/Hopper platforms.
 > If you are using the newer `Blackwell` platform, please modify the base image to `lmsysorg/sglang:v0.4.8.post1-cu128-b200` before executing the build operation.
 
+---
+
 ## Docker Description
 
 MinerU's Docker uses `lmsysorg/sglang` as the base image, so it includes the `sglang` inference acceleration framework and necessary dependencies by default. Therefore, on compatible devices, you can directly use `sglang` to accelerate VLM model inference.
@@ -26,6 +28,8 @@ MinerU's Docker uses `lmsysorg/sglang` as the base image, so it includes the `sg
 >
 > If your device doesn't meet the above requirements, you can still use other features of MinerU, but cannot use `sglang` to accelerate VLM model inference, meaning you cannot use the `vlm-sglang-engine` backend or start the `vlm-sglang-server` service.
 
+---
+
 ## Start Docker Container:
 
 ```bash
@@ -40,6 +44,8 @@ docker run --gpus all \
 After executing this command, you will enter the Docker container's interactive terminal with some ports mapped for potential services. You can directly run MinerU-related commands within the container to use MinerU's features.
 You can also directly start MinerU services by replacing `/bin/bash` with service startup commands. For detailed instructions, please refer to the [MinerU Usage Documentation](../usage/index.md).
 
+---
+
 ## Start Services Directly with Docker Compose
 
 We provide a [compose.yaml](https://github.com/opendatalab/MinerU/blob/master/docker/compose.yaml) file that you can use to quickly start MinerU services.
@@ -55,7 +61,8 @@ wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
 >- Different services might have additional parameter configurations, which you can view and edit in the `compose.yaml` file.
 >- Due to the pre-allocation of GPU memory by the `sglang` inference acceleration framework, you may not be able to run multiple `sglang` services simultaneously on the same machine. Therefore, ensure that other services that might use GPU memory have been stopped before starting the `vlm-sglang-server` service or using the `vlm-sglang-engine` backend.
 
-- Start `sglang-server` service and connect to `sglang-server` via `vlm-sglang-client` backend:
+### Start sglang-server service
+connect to `sglang-server` via `vlm-sglang-client` backend
   ```bash
   docker compose -f compose.yaml --profile mineru-sglang-server up -d
   ```
@@ -65,14 +72,14 @@ wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
   > mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://<server_ip>:30000
   > ```
 
-- Start API service:
+### Start Web API service
   ```bash
   docker compose -f compose.yaml --profile mineru-api up -d
   ```
   >[!TIP]
   >Access `http://<server_ip>:8000/docs` in your browser to view the API documentation.
 
-- Start Gradio WebUI service:
+### Start Gradio WebUI service
   ```bash
   docker compose -f compose.yaml --profile mineru-gradio up -d
   ```

docs/en/usage/advanced_cli_parameters.md

@@ -1,5 +1,7 @@
 # Advanced Command Line Parameters
 
+---
+
 ## SGLang Acceleration Parameter Optimization
 
 ### Memory Optimization Parameters
@@ -9,6 +11,8 @@
 > - If you encounter insufficient VRAM when using a single graphics card, you may need to reduce the KV cache size with `--mem-fraction-static 0.5`. If VRAM issues persist, try reducing it further to `0.4` or lower.
 > - If you have two or more graphics cards, you can try using tensor parallelism (TP) mode to simply expand available VRAM: `--tp-size 2`
 
+---
+
 ### Performance Optimization Parameters
 > [!TIP]
 > If you can already use SGLang normally for accelerated VLM model inference but still want to further improve inference speed, you can try the following parameters:
@@ -16,11 +20,15 @@
 > - If you have multiple graphics cards, you can use SGLang's multi-card parallel mode to increase throughput: `--dp-size 2`
 > - You can also enable `torch.compile` to accelerate inference speed by approximately 15%: `--enable-torch-compile`
 
+---
+
 ### Parameter Passing Instructions
 > [!TIP]
 > - All officially supported SGLang parameters can be passed to MinerU through command line arguments, including the following commands: `mineru`, `mineru-sglang-server`, `mineru-gradio`, `mineru-api`
 > - If you want to learn more about `sglang` parameter usage, please refer to the [SGLang official documentation](https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands)
 
+---
+
 ## GPU Device Selection and Configuration
 
 ### CUDA_VISIBLE_DEVICES Basic Usage
@@ -31,6 +39,8 @@
 >   ```
 > - This specification method is effective for all command line calls, including `mineru`, `mineru-sglang-server`, `mineru-gradio`, and `mineru-api`, and applies to both `pipeline` and `vlm` backends.
 
+---
+
 ### Common Device Configuration Examples
 > [!TIP]
 > Here are some common `CUDA_VISIBLE_DEVICES` setting examples:
@@ -42,6 +52,8 @@
 >   CUDA_VISIBLE_DEVICES=""  # No GPU will be visible
 >   ```
 
+---
+
 ## Practical Application Scenarios
 > [!TIP]
 > Here are some possible usage scenarios:

docs/en/usage/index.md

@@ -72,6 +72,8 @@ If you need to adjust parsing options through custom parameters, you can also ch
 > All officially supported sglang parameters can be passed to MinerU through command line arguments, including the following commands: `mineru`, `mineru-sglang-server`, `mineru-gradio`, `mineru-api`.
 > We have compiled some commonly used parameters and usage methods for `sglang`, which can be found in the documentation [Advanced Command Line Parameters](./advanced_cli_parameters.md).
 
+---
+
 ## Extending MinerU Functionality with Configuration Files
 
 MinerU is now ready to use out of the box, but also supports extending functionality through configuration files. You can edit `mineru.json` file in your user directory to add custom configurations.  

docs/zh/quick_start/docker_deployment.md

@@ -13,6 +13,8 @@ docker build -t mineru-sglang:latest -f Dockerfile .
 > [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/china/Dockerfile)默认使用`lmsysorg/sglang:v0.4.8.post1-cu126`作为基础镜像，支持Turing/Ampere/Ada Lovelace/Hopper平台，
 > 如您使用较新的`Blackwell`平台，请将基础镜像修改为`lmsysorg/sglang:v0.4.8.post1-cu128-b200` 再执行build操作。
 
+---
+
 ## Docker说明
 
 Mineru的docker使用了`lmsysorg/sglang`作为基础镜像，因此在docker中默认集成了`sglang`推理加速框架和必需的依赖环境。因此在满足条件的设备上，您可以直接使用`sglang`加速VLM模型推理。
@@ -25,6 +27,8 @@ Mineru的docker使用了`lmsysorg/sglang`作为基础镜像，因此在docker中
 >
 > 如果您的设备不满足上述条件，您仍然可以使用MinerU的其他功能，但无法使用`sglang`加速VLM模型推理，即无法使用`vlm-sglang-engine`后端和启动`vlm-sglang-server`服务。
 
+---
+
 ## 启动 Docker 容器：
 
 ```bash
@@ -39,6 +43,7 @@ docker run --gpus all \
 执行该命令后，您将进入到Docker容器的交互式终端，并映射了一些端口用于可能会使用的服务，您可以直接在容器内运行MinerU相关命令来使用MinerU的功能。
 您也可以直接通过替换`/bin/bash`为服务启动命令来启动MinerU服务，详细说明请参考[MinerU使用文档](../usage/index.md)。
 
+---
 
 ## 通过 Docker Compose 直接启动服务
 
@@ -54,7 +59,8 @@ wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
 >- 不同的服务可能会有额外的参数配置，您可以在`compose.yaml`文件中查看并编辑。
 >- 由于`sglang`推理加速框架预分配显存的特性，您可能无法在同一台机器上同时运行多个`sglang`服务，因此请确保在启动`vlm-sglang-server`服务或使用`vlm-sglang-engine`后端时，其他可能使用显存的服务已停止。
 
-- 启动`sglang-server`服务，并通过`vlm-sglang-client`后端连接`sglang-server`：
+### 启动 sglang-server 服务
+并通过`vlm-sglang-client`后端连接`sglang-server`
   ```bash
   docker compose -f compose.yaml --profile mineru-sglang-server up -d
   ```
@@ -64,14 +70,14 @@ wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
   > mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://<server_ip>:30000
   > ```
 
-- 启动 API 服务：
+### 启动 Web API 服务
   ```bash
   docker compose -f compose.yaml --profile mineru-api up -d
   ```
   >[!TIP]
   >在浏览器中访问 `http://<server_ip>:8000/docs` 查看API文档。
 
-- 启动 Gradio WebUI 服务：
+### 启动 Gradio WebUI 服务
   ```bash
   docker compose -f compose.yaml --profile mineru-gradio up -d
   ```

docs/zh/usage/advanced_cli_parameters.md

@@ -1,5 +1,7 @@
 # 命令行参数进阶技巧
 
+---
+
 ## SGLang 加速参数优化
 
 ### 显存优化参数
@@ -9,6 +11,8 @@
 > - 如果您使用单张显卡遇到显存不足的情况时，可能需要调低KV缓存大小，`--mem-fraction-static 0.5`，如仍出现显存不足问题，可尝试进一步降低到`0.4`或更低
 > - 如您有两张以上显卡，可尝试通过张量并行（TP）模式简单扩充可用显存：`--tp-size 2`
 
+---
+
 ### 性能优化参数
 > [!TIP]
 > 如果您已经可以正常使用sglang对vlm模型进行加速推理，但仍然希望进一步提升推理速度，可以尝试以下参数：
@@ -16,11 +20,15 @@
 > - 如果您有超过多张显卡，可以使用sglang的多卡并行模式来增加吞吐量：`--dp-size 2`
 > - 同时您可以启用`torch.compile`来将推理速度加速约15%：`--enable-torch-compile`
 
+---
+
 ### 参数传递说明
 > [!TIP]
 > - 所有sglang官方支持的参数都可用通过命令行参数传递给 MinerU，包括以下命令:`mineru`、`mineru-sglang-server`、`mineru-gradio`、`mineru-api`
 > - 如果您想了解更多有关`sglang`的参数使用方法，请参考 [sglang官方文档](https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands)
 
+---
+
 ## GPU 设备选择与配置
 
 ### CUDA_VISIBLE_DEVICES 基本用法
@@ -31,6 +39,8 @@
 >   ```
 > - 这种指定方式对所有的命令行调用都有效，包括 `mineru`、`mineru-sglang-server`、`mineru-gradio` 和 `mineru-api`，且对`pipeline`、`vlm`后端均适用。
 
+---
+
 ### 常见设备配置示例
 > [!TIP]
 > 以下是一些常见的 `CUDA_VISIBLE_DEVICES` 设置示例：
@@ -42,6 +52,8 @@
 >   CUDA_VISIBLE_DEVICES=""  # No GPU will be visible
 >   ```
 
+---
+
 ## 实际应用场景
 
 > [!TIP]

docs/zh/usage/index.md

@@ -72,6 +72,7 @@ mineru -p <input_path> -o <output_path> -b vlm-transformers
 > 所有sglang官方支持的参数都可用通过命令行参数传递给 MinerU，包括以下命令:`mineru`、`mineru-sglang-server`、`mineru-gradio`、`mineru-api`，
 > 我们整理了一些`sglang`使用中的常用参数和使用方法，可以在文档[命令行进阶参数](./advanced_cli_parameters.md)中获取。
 
+---
 
 ## 基于配置文件扩展 MinerU 功能