docs: improve clarity and formatting in multiple documentation files

docs/en/index.md

@@ -4,6 +4,7 @@
   <img src="../images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
 </p>
 </div>
+
 <!-- icon -->
 
 [![stars](https://img.shields.io/github/stars/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)

docs/en/quick_start/docker_deployment.md

@@ -19,6 +19,7 @@ MinerU's Docker uses `lmsysorg/sglang` as the base image, so it includes the `sg
 
 > [!NOTE]
 > Requirements for using `sglang` to accelerate VLM model inference:
+> 
 > - Device must have Turing architecture or later graphics cards with 8GB+ available VRAM.
 > - The host machine's graphics driver should support CUDA 12.6 or higher; `Blackwell` platform should support CUDA 12.8 or higher. You can check the driver version using the `nvidia-smi` command.
 > - Docker container must have access to the host machine's graphics devices.
@@ -59,10 +60,14 @@ wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
   ```bash
   docker compose -f compose.yaml --profile mineru-api up -d
   ```
-  Access `http://<server_ip>:8000/docs` in your browser to view the API documentation.
+  >[!TIP]
+  >Access `http://<server_ip>:8000/docs` in your browser to view the API documentation.
 
 - Start Gradio WebUI service:
   ```bash
   docker compose -f compose.yaml --profile mineru-gradio up -d
   ```
-  Access `http://<server_ip>:7860` in your browser to use the Gradio WebUI or access `http://<server_ip>:7860/?view=api` to use the Gradio API.
+  >[!TIP]
+  >
+  >- Access `http://<server_ip>:7860` in your browser to use the Gradio WebUI.
+  >- Access `http://<server_ip>:7860/?view=api` to use the Gradio API.

docs/en/usage/advanced_cli_parameters.md

@@ -5,12 +5,14 @@
 ### Memory Optimization Parameters
 > [!TIP]
 > SGLang acceleration mode currently supports running on Turing architecture graphics cards with a minimum of 8GB VRAM, but graphics cards with <24GB VRAM may encounter insufficient memory issues. You can optimize memory usage with the following parameters:
+> 
 > - 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:
+> 
 > - 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`
 
@@ -31,7 +33,7 @@
 
 ### Common Device Configuration Examples
 > [!TIP]
-> - Here are some common `CUDA_VISIBLE_DEVICES` setting examples:
+> Here are some common `CUDA_VISIBLE_DEVICES` setting examples:
 >   ```bash
 >   CUDA_VISIBLE_DEVICES=1 Only device 1 will be seen
 >   CUDA_VISIBLE_DEVICES=0,1 Devices 0 and 1 will be visible
@@ -43,10 +45,12 @@
 ### Practical Application Scenarios
 > [!TIP]
 > Here are some possible usage scenarios:
+> 
 > - If you have multiple graphics cards and need to specify cards 0 and 1, using multi-card parallelism to start 'sglang-server', you can use the following command:
 >   ```bash
 >   CUDA_VISIBLE_DEVICES=0,1 mineru-sglang-server --port 30000 --dp-size 2
 >   ```
+>   
 > - If you have multiple graphics cards and need to start two `fastapi` services on cards 0 and 1, listening on different ports respectively, you can use the following commands:
 >   ```bash
 >   # In terminal 1

docs/en/usage/cli_tools.md

@@ -63,6 +63,8 @@ Options:
 ## Environment Variables Description
 
 Some parameters of MinerU command line tools have equivalent environment variable configurations. Generally, environment variable configurations have higher priority than command line parameters and take effect across all command line tools.
+Here are the environment variables and their descriptions:
+
 - `MINERU_DEVICE_MODE`: Used to specify inference device, supports device types like `cpu/cuda/cuda:0/npu/mps`, only effective for `pipeline` backend.
 - `MINERU_VIRTUAL_VRAM_SIZE`: Used to specify maximum GPU VRAM usage per process (GB), only effective for `pipeline` backend.
 - `MINERU_MODEL_SOURCE`: Used to specify model source, supports `huggingface/modelscope/local`, defaults to `huggingface`, can be switched to `modelscope` or local models through environment variables.

docs/en/usage/index.md

@@ -15,14 +15,16 @@ MinerU has built-in command line tools that allow users to quickly use MinerU fo
 # Default parsing using pipeline backend
 mineru -p <input_path> -o <output_path>
 ```
-- `<input_path>`: Local PDF/image file or directory
-- `<output_path>`: Output directory
+> [!TIP]
+>- `<input_path>`: Local PDF/image file or directory
+>- `<output_path>`: Output directory
+>
+> For more information about output files, please refer to [Output File Documentation](./output_file.md).
 
 > [!NOTE]
-> The command line tool will automatically attempt cuda/mps acceleration on Linux and macOS systems. Windows users who need cuda acceleration should visit the [PyTorch official website](https://pytorch.org/get-started/locally/) to select the appropriate command for their cuda version to install acceleration-enabled `torch` and `torchvision`.
+> The command line tool will automatically attempt cuda/mps acceleration on Linux and macOS systems. 
+> Windows users who need cuda acceleration should visit the [PyTorch official website](https://pytorch.org/get-started/locally/) to select the appropriate command for their cuda version to install acceleration-enabled `torch` and `torchvision`.
 
-> [!TIP]
-> For more information about output files, please refer to [Output File Documentation](./output_file.md).
 
 ```bash
 # Or specify vlm backend for parsing

docs/zh/quick_start/docker_deployment.md

@@ -67,5 +67,5 @@ wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
   ```
   >[!TIP]
   > 
-  >- 在浏览器中访问 `http://<server_ip>:7860` 使用 Gradio WebUI 
-  >- 访问 `http://<server_ip>:7860/?view=api` 使用 Gradio API
+  >- 在浏览器中访问 `http://<server_ip>:7860` 使用 Gradio WebUI。
+  >- 访问 `http://<server_ip>:7860/?view=api` 使用 Gradio API。