Merge pull request #3508 from myhloli/dev

update docs

README.md

@@ -46,25 +46,25 @@
 
 - 2025/09/19 2.5.0 Released
 
-We are officially releasing MinerU2.5, currently the most powerful multimodal large model for document parsing.
-With only 1.2B parameters, MinerU2.5's accuracy on the OmniDocBench benchmark comprehensively surpasses top-tier multimodal models like Gemini 2.5 Pro, GPT-4o, and Qwen2.5-VL-72B. It also significantly outperforms leading specialized models such as dots.ocr, MonkeyOCR, and PP-StructureV3.
-The model has been released on [HuggingFace](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B) and [ModelScope](https://modelscope.cn/models/opendatalab/MinerU2.5-2509-1.2B) platforms. Welcome to download and use!
-- Core Highlights:
-  - SOTA Performance with Extreme Efficiency: As a 1.2B model, it achieves State-of-the-Art (SOTA) results that exceed models in the 10B and 100B+ classes, redefining the performance-per-parameter standard in document AI.
-  - Advanced Architecture for Across-the-Board Leadership: By combining a two-stage inference pipeline (decoupling layout analysis from content recognition) with a native high-resolution architecture, it achieves SOTA performance across five key areas: layout analysis, text recognition, formula recognition, table recognition, and reading order.
-- Key Capability Enhancements:
-  - Layout Detection: Delivers more complete results by accurately covering non-body content like headers, footers, and page numbers. It also provides more precise element localization and natural format reconstruction for lists and references.
-  - Table Parsing: Drastically improves parsing for challenging cases, including rotated tables, borderless/semi-structured tables, and long/complex tables.
-  - Formula Recognition: Significantly boosts accuracy for complex, long-form, and hybrid Chinese-English formulas, greatly enhancing the parsing capability for mathematical documents.
-
-Additionally, with the release of vlm 2.5, we have made some adjustments to the repository:
-- The vlm backend has been upgraded to version 2.5, supporting the MinerU2.5 model and no longer compatible with the MinerU2.0-2505-0.9B model. The last version supporting the 2.0 model is mineru-2.2.2.
-- VLM inference-related code has been moved to [mineru_vl_utils](https://github.com/opendatalab/mineru-vl-utils), reducing coupling with the main mineru repository and facilitating independent iteration in the future.
-- The vlm accelerated inference framework has been switched from `sglang` to `vllm`, achieving full compatibility with the vllm ecosystem, allowing users to use the MinerU2.5 model and accelerated inference on any platform that supports the vllm framework.
-- Due to major upgrades in the vlm model supporting more layout types, we have made some adjustments to the structure of the parsing intermediate file `middle.json` and result file `content_list.json`. Please refer to the [documentation](https://opendatalab.github.io/MinerU/reference/output_files/) for details.
-
-Other repository optimizations:
-- Removed file extension whitelist validation for input files. When input files are PDF documents or images, there are no longer requirements for file extensions, improving usability.
+  We are officially releasing MinerU2.5, currently the most powerful multimodal large model for document parsing.
+  With only 1.2B parameters, MinerU2.5's accuracy on the OmniDocBench benchmark comprehensively surpasses top-tier multimodal models like Gemini 2.5 Pro, GPT-4o, and Qwen2.5-VL-72B. It also significantly outperforms leading specialized models such as dots.ocr, MonkeyOCR, and PP-StructureV3.
+  The model has been released on [HuggingFace](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B) and [ModelScope](https://modelscope.cn/models/opendatalab/MinerU2.5-2509-1.2B) platforms. Welcome to download and use!
+  - Core Highlights:
+    - SOTA Performance with Extreme Efficiency: As a 1.2B model, it achieves State-of-the-Art (SOTA) results that exceed models in the 10B and 100B+ classes, redefining the performance-per-parameter standard in document AI.
+    - Advanced Architecture for Across-the-Board Leadership: By combining a two-stage inference pipeline (decoupling layout analysis from content recognition) with a native high-resolution architecture, it achieves SOTA performance across five key areas: layout analysis, text recognition, formula recognition, table recognition, and reading order.
+  - Key Capability Enhancements:
+    - Layout Detection: Delivers more complete results by accurately covering non-body content like headers, footers, and page numbers. It also provides more precise element localization and natural format reconstruction for lists and references.
+    - Table Parsing: Drastically improves parsing for challenging cases, including rotated tables, borderless/semi-structured tables, and long/complex tables.
+    - Formula Recognition: Significantly boosts accuracy for complex, long-form, and hybrid Chinese-English formulas, greatly enhancing the parsing capability for mathematical documents.
+
+  Additionally, with the release of vlm 2.5, we have made some adjustments to the repository:
+  - The vlm backend has been upgraded to version 2.5, supporting the MinerU2.5 model and no longer compatible with the MinerU2.0-2505-0.9B model. The last version supporting the 2.0 model is mineru-2.2.2.
+  - VLM inference-related code has been moved to [mineru_vl_utils](https://github.com/opendatalab/mineru-vl-utils), reducing coupling with the main mineru repository and facilitating independent iteration in the future.
+  - The vlm accelerated inference framework has been switched from `sglang` to `vllm`, achieving full compatibility with the vllm ecosystem, allowing users to use the MinerU2.5 model and accelerated inference on any platform that supports the vllm framework.
+  - Due to major upgrades in the vlm model supporting more layout types, we have made some adjustments to the structure of the parsing intermediate file `middle.json` and result file `content_list.json`. Please refer to the [documentation](https://opendatalab.github.io/MinerU/reference/output_files/) for details.
+
+  Other repository optimizations:
+  - Removed file extension whitelist validation for input files. When input files are PDF documents or images, there are no longer requirements for file extensions, improving usability.
 
 <details>
   <summary>History Log</summary>

README_zh-CN.md

@@ -45,24 +45,24 @@
 # 更新记录
 
 - 2025/09/19 2.5.0 发布
-我们正式发布 MinerU2.5，当前最强文档解析多模态大模型。仅凭 1.2B 参数，MinerU2.5 在 OmniDocBench 文档解析评测中，精度已全面超越 Gemini2.5-Pro、GPT-4o、Qwen2.5-VL-72B等顶级多模态大模型，并显著领先于主流文档解析专用模型（如 dots.ocr, MonkeyOCR, PP-StructureV3 等）。
-模型已发布至[HuggingFace](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B)和[ModelScope](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B)平台，欢迎大家下载使用！
-- 核心亮点
-  - 极致能效，性能SOTA: 以 1.2B 的轻量化规模，实现了超越百亿乃至千亿级模型的SOTA性能，重新定义了文档解析的能效比。
-  - 先进架构，全面领先: 通过 “两阶段推理” (解耦布局分析与内容识别) 与 原生高分辨率架构 的结合，在布局分析、文本识别、公式识别、表格识别及阅读顺序五大方面均达到 SOTA 水平。
-- 关键能力提升
-  - 布局检测: 结果更完整，精准覆盖页眉、页脚、页码等非正文内容；同时提供更精准的元素定位与更自然的格式还原（如列表、参考文献）。
-  - 表格解析: 大幅优化了对旋转表格、无线/少线表、以及长难表格的解析能力。
-  - 公式识别: 显著提升中英混合公式及复杂长公式的识别准确率，大幅改善数学类文档解析能力。
-
-此外，伴随vlm 2.5的发布，我们对仓库做出一些调整：
-- vlm后端升级至2.5版本，支持MinerU2.5模型，不再兼容MinerU2.0-2505-0.9B模型，最后一个支持2.0模型的版本为mineru-2.2.2。
-- vlm推理相关代码已移至[mineru_vl_utils](https://github.com/opendatalab/mineru-vl-utils),降低与mineru主仓库的耦合度，便于后续独立迭代。
-- vlm加速推理框架从`sglang`切换至`vllm`,并实现对vllm生态的完全兼容，使得用户可以在任何支持vllm框架的平台上使用MinerU2.5模型并加速推理。
-- 由于vlm模型的重大升级，支持更多layout type，因此我们对解析的中间文件`middle.json`和结果文件`content_list.json`的结构做出一些调整，请参考[文档](https://opendatalab.github.io/MinerU/zh/reference/output_files/)了解详情。
-
-其他仓库优化：
-- 移除对输入文件的后缀名白名单校验，当输入文件为PDF文档或图片时，对文件的后缀名不再有要求，提升易用性。
+  我们正式发布 MinerU2.5，当前最强文档解析多模态大模型。仅凭 1.2B 参数，MinerU2.5 在 OmniDocBench 文档解析评测中，精度已全面超越 Gemini2.5-Pro、GPT-4o、Qwen2.5-VL-72B等顶级多模态大模型，并显著领先于主流文档解析专用模型（如 dots.ocr, MonkeyOCR, PP-StructureV3 等）。
+  模型已发布至[HuggingFace](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B)和[ModelScope](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B)平台，欢迎大家下载使用！
+  - 核心亮点
+    - 极致能效，性能SOTA: 以 1.2B 的轻量化规模，实现了超越百亿乃至千亿级模型的SOTA性能，重新定义了文档解析的能效比。
+    - 先进架构，全面领先: 通过 “两阶段推理” (解耦布局分析与内容识别) 与 原生高分辨率架构 的结合，在布局分析、文本识别、公式识别、表格识别及阅读顺序五大方面均达到 SOTA 水平。
+  - 关键能力提升
+    - 布局检测: 结果更完整，精准覆盖页眉、页脚、页码等非正文内容；同时提供更精准的元素定位与更自然的格式还原（如列表、参考文献）。
+    - 表格解析: 大幅优化了对旋转表格、无线/少线表、以及长难表格的解析能力。
+    - 公式识别: 显著提升中英混合公式及复杂长公式的识别准确率，大幅改善数学类文档解析能力。
+
+  此外，伴随vlm 2.5的发布，我们对仓库做出一些调整：
+  - vlm后端升级至2.5版本，支持MinerU2.5模型，不再兼容MinerU2.0-2505-0.9B模型，最后一个支持2.0模型的版本为mineru-2.2.2。
+  - vlm推理相关代码已移至[mineru_vl_utils](https://github.com/opendatalab/mineru-vl-utils),降低与mineru主仓库的耦合度，便于后续独立迭代。
+  - vlm加速推理框架从`sglang`切换至`vllm`,并实现对vllm生态的完全兼容，使得用户可以在任何支持vllm框架的平台上使用MinerU2.5模型并加速推理。
+  - 由于vlm模型的重大升级，支持更多layout type，因此我们对解析的中间文件`middle.json`和结果文件`content_list.json`的结构做出一些调整，请参考[文档](https://opendatalab.github.io/MinerU/zh/reference/output_files/)了解详情。
+
+  其他仓库优化：
+  - 移除对输入文件的后缀名白名单校验，当输入文件为PDF文档或图片时，对文件的后缀名不再有要求，提升易用性。
 
 <details>
   <summary>历史日志</summary>