docs: make Chinese README the default

2026-07-13 10:09:03 +00:00
parent 37420ffc2e
commit 2156a11d0f
1 changed files with 44 additions and 37 deletions
@@ -1,3 +1,9 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/deepseek-ai/DeepSeek-OCR) · [上游 README](https://github.com/deepseek-ai/DeepSeek-OCR/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <!-- markdownlint-disable first-line-h1 -->
 <!-- markdownlint-disable html -->
 <!-- markdownlint-disable no-duplicate-header -->
@@ -33,14 +39,14 @@


 <p align="center">
-  <a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR"><b>📥 Model Download</b></a> |
-  <a href="https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek_OCR_paper.pdf"><b>📄 Paper Link</b></a> |
-  <a href="https://arxiv.org/abs/2510.18234"><b>📄 Arxiv Paper Link</b></a> |
+  <a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR"><b>📥 模型下载</b></a> |
+  <a href="https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek_OCR_paper.pdf"><b>📄 论文链接</b></a> |
+  <a href="https://arxiv.org/abs/2510.18234"><b>📄 Arxiv 论文链接</b></a> |
 </p>

 <h2>
 <p align="center">
-  <a href="">DeepSeek-OCR: Contexts Optical Compression</a>
+  <a href="">DeepSeek-OCR：上下文光学压缩（Contexts Optical Compression）</a>
 </p>
 </h2>

@@ -48,26 +54,26 @@
 <img src="assets/fig1.png" style="width: 1000px" align=center>
 </p>
 <p align="center">
-<a href="">Explore the boundaries of visual-text compression.</a>       
+<a href="">探索视觉-文本压缩的边界。</a>       
 </p>

-## Release
- [2026/01/27]🚀🚀🚀🚀🚀🚀 We present [DeepSeek-OCR2](https://github.com/deepseek-ai/DeepSeek-OCR-2)
- [2025/10/23]🚀🚀🚀 DeepSeek-OCR is now officially supported in upstream [vLLM](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-OCR.html#installing-vllm). Thanks to the [vLLM](https://github.com/vllm-project/vllm) team for their help.
- [2025/10/20]🚀🚀🚀 We release DeepSeek-OCR, a model to investigate the role of vision encoders from an LLM-centric viewpoint.
+## 发布
+- [2026/01/27]🚀🚀🚀🚀🚀🚀 我们发布 [DeepSeek-OCR2](https://github.com/deepseek-ai/DeepSeek-OCR-2)
+- [2025/10/23]🚀🚀🚀 DeepSeek-OCR 现已在上游 [vLLM](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-OCR.html#installing-vllm). 中获得官方支持。感谢 [vLLM](https://github.com/vllm-project/vllm) 团队的帮助。
+- [2025/10/20]🚀🚀🚀 我们发布 DeepSeek-OCR，这是一个从以 LLM 为中心的视角研究视觉编码器作用的模型。

-## Contents
- [Install](#install)
- [vLLM Inference](#vllm-inference)
- [Transformers Inference](#transformers-inference)
+## 目录
+- [安装](#install)
+- [vLLM 推理](#vllm-inference)
+- [Transformers 推理](#transformers-inference)
  




-## Install
->Our environment is cuda11.8+torch2.6.0.
-1. Clone this repository and navigate to the DeepSeek-OCR folder
+## 安装
+>我们的环境为 cuda11.8+torch2.6.0。
+1. 克隆本仓库并进入 DeepSeek-OCR 文件夹
 ```bash
 git clone https://github.com/deepseek-ai/DeepSeek-OCR.git
 ```
@@ -76,37 +82,37 @@ git clone https://github.com/deepseek-ai/DeepSeek-OCR.git
 conda create -n deepseek-ocr python=3.12.9 -y
 conda activate deepseek-ocr
 ```
-3. Packages
+3. 依赖包

- download the vllm-0.8.5 [whl](https://github.com/vllm-project/vllm/releases/tag/v0.8.5) 
+- 下载 vllm-0.8.5 [whl](https://github.com/vllm-project/vllm/releases/tag/v0.8.5) 
 ```Shell
 pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118
 pip install vllm-0.8.5+cu118-cp38-abi3-manylinux1_x86_64.whl
 pip install -r requirements.txt
 pip install flash-attn==2.7.3 --no-build-isolation
 ```
-**Note:** if you want vLLM and transformers codes to run in the same environment, you don't need to worry about this installation error like: vllm 0.8.5+cu118 requires transformers>=4.51.1
+**注意：** 若你希望 vLLM 与 Transformers 代码在同一环境中运行，则无需担心如下安装错误：vllm 0.8.5+cu118 requires transformers>=4.51.1

-## vLLM-Inference
+## vLLM 推理
 - VLLM:
->**Note:** change the INPUT_PATH/OUTPUT_PATH and other settings in the DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py
+>**注意：** 请在 DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py 中修改 INPUT_PATH/OUTPUT_PATH 及其他设置
 ```Shell
 cd DeepSeek-OCR-master/DeepSeek-OCR-vllm
 ```
-1. image: streaming output
+1. 图像：流式输出
 ```Shell
 python run_dpsk_ocr_image.py
 ```
-2. pdf: concurrency ~2500tokens/s(an A100-40G)
+2. PDF：并发约 2500 tokens/s（A100-40G）
 ```Shell
 python run_dpsk_ocr_pdf.py
 ```
-3. batch eval for benchmarks
+3. 基准测试批量评估
 ```Shell
 python run_dpsk_ocr_eval_batch.py
 ```

-**[2025/10/23] The version of upstream [vLLM](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-OCR.html#installing-vllm):**
+**[2025/10/23] 上游 [vLLM](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-OCR.html#installing-vllm):**

 ```shell
 uv venv
@@ -162,7 +168,7 @@ model_outputs = llm.generate(model_input, sampling_param)
 for output in model_outputs:
    print(output.outputs[0].text)
 ```
-## Transformers-Inference
+## Transformers 推理
 - Transformers
 ```python
 from transformers import AutoModel, AutoTokenizer
@@ -182,22 +188,22 @@ output_path = 'your/output/dir'

 res = model.infer(tokenizer, prompt=prompt, image_file=image_file, output_path = output_path, base_size = 1024, image_size = 640, crop_mode=True, save_results = True, test_compress = True)
 ```
-or you can
+或者你可以
 ```Shell
 cd DeepSeek-OCR-master/DeepSeek-OCR-hf
 python run_dpsk_ocr.py
 ```
-## Support-Modes
-The current open-source model supports the following modes:
- Native resolution:
+## 支持模式
+当前开源模型支持以下模式：
+- 原生分辨率：
  - Tiny: 512×512 （64 vision tokens）✅
  - Small: 640×640 （100 vision tokens）✅
  - Base: 1024×1024 （256 vision tokens）✅
  - Large: 1280×1280 （400 vision tokens）✅
- Dynamic resolution
+- 动态分辨率
  - Gundam: n×640×640 + 1×1024×1024 ✅

-## Prompts examples
+## 提示词示例
 ```python
 # document: <image>\n<|grounding|>Convert the document to markdown.
 # other image: <image>\n<|grounding|>OCR this image.
@@ -209,7 +215,7 @@ The current open-source model supports the following modes:
 ```


-## Visualizations
+## 可视化
 <table>
 <tr>
 <td><img src="assets/show1.jpg" style="width: 500px"></td>
@@ -222,13 +228,13 @@ The current open-source model supports the following modes:
 </table>


-## Acknowledgement
+## 致谢

-We would like to thank [Vary](https://github.com/Ucas-HaoranWei/Vary/), [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/), [MinerU](https://github.com/opendatalab/MinerU), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [OneChart](https://github.com/LingyvKong/OneChart), [Slow Perception](https://github.com/Ucas-HaoranWei/Slow-Perception) for their valuable models and ideas.
+我们感谢 [Vary](https://github.com/Ucas-HaoranWei/Vary/), [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/), [MinerU](https://github.com/opendatalab/MinerU), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [OneChart](https://github.com/LingyvKong/OneChart), [Slow Perception](https://github.com/Ucas-HaoranWei/Slow-Perception) 提供的宝贵模型与思路。

-We also appreciate the benchmarks: [Fox](https://github.com/ucaslcl/Fox), [OminiDocBench](https://github.com/opendatalab/OmniDocBench).
+我们也感谢以下基准测试：[Fox](https://github.com/ucaslcl/Fox), [OminiDocBench](https://github.com/opendatalab/OmniDocBench).

-## Citation
+## 引用

 ```bibtex
@article{wei2025deepseek,
@@ -237,3 +243,4 @@ We also appreciate the benchmarks: [Fox](https://github.com/ucaslcl/Fox), [Omini
  journal={arXiv preprint arXiv:2510.18234},
  year={2025}
 }
+```