align

NOTICE

@@ -420,16 +420,6 @@ Please see above.
 
 
 
-------------- LICENSE FOR MonkeyOCR CODE --------------
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-Copyright notice:No copyright info provided
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-License:apache2.0
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-Please see above.
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 ------------- LICENSE FOR OmniDocbench CODE --------------
 
 Copyright notice:No copyright info provided
@@ -450,31 +440,6 @@ Please see above.
 
 
 
-------------- LICENSE FOR aimv2 CODE --------------
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-License：
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-IMPORTANT:  This Apple software is supplied to you by Apple Inc. ("Apple") in consideration of your agreement to the following terms, and your use, installation, modification or redistribution of this Apple software constitutes acceptance of these terms.  If you do not agree with these terms, please do not use, install, modify or
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 ------------- LICENSE FOR Hugging Face CODE --------------
 
 Copyright notice：Copyright 2019 Ross Wightman
@@ -587,193 +552,3 @@ Section 7.  Miscellaneous
 
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README.md

@@ -8,13 +8,13 @@
 dots.ocr
 </h1>
 
-[![HuggingFace](https://img.shields.io/badge/HuggingFace%20Weights-black.svg?logo=HuggingFace)](https://huggingface.co/rednote-hilab/dots.ocr-1.5)
+[![HuggingFace](https://img.shields.io/badge/HuggingFace%20Weights-black.svg?logo=HuggingFace)](https://huggingface.co/rednote-hilab/dots.mocr)
 [![Arxiv](https://img.shields.io/badge/arXiv-Paper-B31B1B.svg?logo=arxiv)](https://arxiv.org/abs/2512.02498)
 
 
 <div align="center">
   <a href="https://dotsocr.xiaohongshu.com" target="_blank" rel="noopener noreferrer"><strong>🖥️ Live Demo</strong></a> | 
-  <a href="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/wechat.jpg" target="_blank" rel="noopener noreferrer"><strong>💬 WeChat</strong></a> | 
+  <a href="assets/wechat.jpg" target="_blank" rel="noopener noreferrer"><strong>💬 WeChat</strong></a> | 
   <a href="https://www.xiaohongshu.com/user/profile/683ffe42000000001d021a4c" target="_blank" rel="noopener noreferrer"><strong>📕 rednote</strong></a> | 
   <a href="https://x.com/rednotehilab" target="_blank" rel="noopener noreferrer"><strong>🐦 X</strong></a>
 </div>
@@ -48,45 +48,59 @@ dots.ocr
       <th>olmOCR-Bench</th>
       <th>OmniDocBench (v1.5)</th>
       <th>XDocParse</th>
+      <th>Average</th>
     </tr>
   </thead>
   <tbody>
+    <tr>
+      <td>MonkeyOCR-pro-3B</td>
+      <td>895.0</td>
+      <td>811.3</td>
+      <td>637.1</td>
+      <td>781.1</td>
+    </tr>
     <tr>
       <td>GLM-OCR</td>
-      <td>859.9</td>
+      <td>884.2</td>
-      <td>937.5</td>
+      <td>972.6</td>
-      <td>742.1</td>
+      <td>820.7</td>
+      <td>892.5</td>
     </tr>
     <tr>
       <td>PaddleOCR-VL-1.5</td>
-      <td>873.6</td>
+      <td>897.3</td>
-      <td>965.6</td>
+      <td>997.9</td>
-      <td>797.6</td>
+      <td>866.4</td>
+      <td>920.5</td>
     </tr>
     <tr>
       <td>HuanyuanOCR</td>
-      <td>978.9</td>
+      <td>997.6</td>
-      <td>974.4</td>
+      <td>1003.9</td>
-      <td>895.9</td>
+      <td>951.1</td>
+      <td>984.2</td>
     </tr>
     <tr>
       <td>dots.ocr</td>
-      <td>1027.4</td>
+      <td>1041.1</td>
-      <td>994.7</td>
+      <td>1027.2</td>
-      <td>1133.4</td>
+      <td>1190.3</td>
+      <td>1086.2</td>
     </tr>
-    <!-- Highlighting dots.ocr-1.5 row with bold tags -->
+    <!-- Highlighting dots.mocr row with bold tags -->
     <tr>
-      <td><strong>dots.ocr-1.5</strong></td>
+      <td><strong>dots.mocr</strong></td>
-      <td><strong>1089.0</strong></td>
+      <td><strong>1104.4</strong></td>
-      <td><strong>1025.8</strong></td>
+      <td><strong>1059.0</strong></td>
-      <td><strong>1157.1</strong></td>
+      <td><strong>1210.7</strong></td>
+      <td><strong>1124.7</strong></td>
     </tr>
     <tr>
       <td>Gemini 3 Pro</td>
-      <td>1171.2</td>
+      <td>1180.4</td>
-      <td>1102.1</td>
+      <td>1128.0</td>
-      <td>1273.9</td>
+      <td>1323.7</td>
+      <td>1210.7</td>
     </tr>
   </tbody>
 </table>
@@ -94,7 +108,7 @@ dots.ocr
 
 > **Notes:** 
 > - Results for Gemini 3 Pro, PaddleOCR-VL-1.5, and GLM-OCR were obtained via APIs, while HuanyuanOCR results were generated using local inference.
-> - The Elo score evaluation was conducted using Gemini 3 Flash. The prompt can be found at: [Elo Score Prompt](https://github.com/rednote-hilab/dots.ocr/blob/master/tools/elo_score_prompt.py). These results are consistent with the findings on [ocrarena](https://www.ocrarena.ai/battle).
+> - The Elo score evaluation was conducted using Gemini 3 Flash. The prompt can be found at: [Elo Score Prompt](tools/elo_score_prompt.py). These results are consistent with the findings on [ocrarena](https://www.ocrarena.ai/battle).
 
 
 #### 1.2 olmOCR-bench
@@ -235,7 +249,7 @@ dots.ocr
             <td>79.1±1.0</td>
         </tr>
         <tr>
-            <td><strong>dots.ocr-1.5</strong></td>
+            <td><strong>dots.mocr</strong></td>
             <td><strong>85.9</strong></td>
             <td><strong>85.5</strong></td>
             <td><strong>90.7</strong></td>
@@ -372,7 +386,7 @@ dots.ocr
       <td>9.29</td>
     </tr>
     <tr>
-      <td><u><strong>dots.ocr-1.5</strong></u></td>
+      <td><u><strong>dots.mocr</strong></u></td>
       <td>3B</td>
       <td><strong>0.031</strong></td>
       <td><strong>0.029</strong></td>
@@ -386,8 +400,8 @@ dots.ocr
 > - Formula and Table metrics for OmniDocBench1.5 are omitted due to their high sensitivity to detection and matching protocols.
 
 
-### 2. Vision-Language Parsing
+### 2. Structured Graphics Parsing
-Visual languages (e.g., charts, graphics, chemical formulas, logos) encapsulate dense human knowledge. **dots.ocr-1.5** unifies the interpretation of these elements by parsing them directly into **SVG code**.
+Visual languages (e.g., charts, graphics, chemical formulas, logos) encapsulate dense human knowledge. **dots.mocr** unifies the interpretation of these elements by parsing them directly into **SVG code**.
 
 <table>
   <thead>
@@ -430,7 +444,7 @@ Visual languages (e.g., charts, graphics, chemical formulas, logos) encapsulate
       <td>0.839</td>
     </tr>
     <tr>
-      <td style="text-align: left;">dots.ocr-1.5</td>
+      <td style="text-align: left;">dots.mocr</td>
       <td>0.850</td>
       <td>0.923</td>
       <td>0.894</td>
@@ -441,7 +455,7 @@ Visual languages (e.g., charts, graphics, chemical formulas, logos) encapsulate
       <td>0.790</td>
     </tr>
     <tr>
-      <td style="text-align: left;"><strong>dots.ocr-1.5-svg</strong></td>
+      <td style="text-align: left;"><strong>dots.mocr-svg</strong></td>
       <td><strong>0.860</strong></td>
       <td><strong>0.931</strong></td>
       <td><strong>0.902</strong></td>
@@ -457,8 +471,8 @@ Visual languages (e.g., charts, graphics, chemical formulas, logos) encapsulate
 
 > **Note:**
 > - We use the ISVGEN metric from [UniSVG](https://ryanlijinke.github.io/) to evaluate the parsing result. For benchmarks that do not natively support image parsing, we use the original images as input, and calculate the ISVGEN score between the rendered output and the original image. 
-> - [OCRVerse](https://github.com/DocTron-hub/OCRVerse) results are derived from various code formats (e.g., SVG, Python), whereas results for Gemini 3 Pro and dots.ocr-1.5 are based specifically on SVG code.
+> - [OCRVerse](https://github.com/DocTron-hub/OCRVerse) results are derived from various code formats (e.g., SVG, Python), whereas results for Gemini 3 Pro and dots.mocr are based specifically on SVG code.
-> - Due to the capacity constraints of a 3B-parameter VLM, dots.ocr-1.5 may not excel in all tasks yet like svg. To complement this, we are simultaneously releasing dots.ocr-1.5-svg. We plan to further address these limitations in future updates.
+> - Due to the capacity constraints of a 3B-parameter VLM, dots.mocr may not excel in all tasks yet like svg. To complement this, we are simultaneously releasing dots.mocr-svg. We plan to further address these limitations in future updates.
 
 
 ### 3. General Vision Tasks
@@ -494,7 +508,20 @@ Visual languages (e.g., charts, graphics, chemical formulas, logos) encapsulate
             <td>-</td>
         </tr>
         <tr>
-            <td><strong>dots.ocr-1.5</strong></td>
+            <td>Qwen3vl-4b-instruct</td>
+            <td>76.2</td>
+            <td>39.7</td>
+            <td>-</td>
+            <td>80.3</td>
+            <td>95.3</td>
+            <td>-</td>
+            <td>88.1</td>
+            <td>84.1</td>
+            <td>84.9</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td><strong>dots.mocr</strong></td>
             <td>77.4</td>
             <td>55.3</td>
             <td>22.85</td>
@@ -513,29 +540,25 @@ Visual languages (e.g., charts, graphics, chemical formulas, logos) encapsulate
 
 # Quick Start
 ## 1. Installation
-### Install dots.ocr-1.5
+### Install dots.mocr
 ```shell
-conda create -n dots_ocr python=3.12
+conda create -n dots_mocr python=3.12
-conda activate dots_ocr
+conda activate dots_mocr
 
-git clone https://github.com/rednote-hilab/dots.ocr.git
+git clone https://github.com/rednote-hilab/dots.mocr.git
-cd dots.ocr
+cd dots.mocr
 
 # Install pytorch, see https://pytorch.org/get-started/previous-versions/ for your cuda version
-pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu128
+# pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu128
+# install flash-attn==2.8.0.post2 for faster inference
 pip install -e .
 ```
 
 If you have trouble with the installation, try our [Docker Image](https://hub.docker.com/r/rednotehilab/dots.ocr) for an easier setup, and follow these steps:
-```shell
-git clone https://github.com/rednote-hilab/dots.ocr.git
-cd dots.ocr
-pip install -e .
-```
 
 
 ### Download Model Weights
-> 💡**Note:** Please use a directory name without periods (e.g., `DotsOCR_1_5` instead of `dots.ocr-1.5`) for the model save path. This is a temporary workaround pending our integration with Transformers.
+> 💡**Note:** Please use a directory name without periods (e.g., `DotsMOCR` instead of `dots.mocr`) for the model save path. This is a temporary workaround pending our integration with Transformers.
 ```shell
 python3 tools/download_model.py
 
@@ -546,28 +569,25 @@ python3 tools/download_model.py --type modelscope
 
 ## 2. Deployment
 ### vLLM inference
-We highly recommend using vLLM for deployment and inference. All of our evaluations results are based on vLLM 0.9.1 via out-of-tree model registration. **Since vLLM version 0.11.0, Dots OCR has been officially integrated into vLLM with verified performance** and you can use vLLM docker image directly (e.g, `vllm/vllm-openai:v0.11.0`) to deploy the model server.
+We highly recommend using vLLM for deployment and inference. **Since vLLM version 0.11.0, Dots OCR has been officially integrated into vLLM with verified performance** and you can use vLLM docker image directly (e.g, `vllm/vllm-openai:v0.11.0`) to deploy the model server.
-
-> **Note:**
-> - We found a little bit performance drop when using vLLM 0.11.0. We are working on a fix.
 
 ```shell
 # Launch vLLM model server
-## dots.ocr-1.5
+## dots.mocr
-CUDA_VISIBLE_DEVICES=0 vllm serve rednote-hilab/dots.ocr-1.5 --tensor-parallel-size 1 --gpu-memory-utilization 0.9 --chat-template-content-format string --served-model-name model --trust-remote-code
+CUDA_VISIBLE_DEVICES=0 vllm serve rednote-hilab/dots.mocr --tensor-parallel-size 1 --gpu-memory-utilization 0.9 --chat-template-content-format string --served-model-name model --trust-remote-code
 
-## dots.ocr-1.5-svg
+## dots.mocr-svg
-CUDA_VISIBLE_DEVICES=0 vllm serve rednote-hilab/dots.ocr-1.5-svg --tensor-parallel-size 1 --gpu-memory-utilization 0.9 --chat-template-content-format string --served-model-name model --trust-remote-code
+CUDA_VISIBLE_DEVICES=0 vllm serve rednote-hilab/dots.mocr-svg --tensor-parallel-size 1 --gpu-memory-utilization 0.9 --chat-template-content-format string --served-model-name model --trust-remote-code
 
 # vLLM API Demo
-# See dots_ocr/model/inference.py and dots_ocr/utils/prompts.py for details on parameter and prompt settings 
+# See dots_mocr/model/inference.py and dots_mocr/utils/prompts.py for details on parameter and prompt settings 
 # that help achieve the best output quality.
 ## document parsing
 python3 ./demo/demo_vllm.py --prompt_mode prompt_layout_all_en 
 ## web parsing 
-python3 ./demo/demo_vllm.py --prompt_mode prompt_web_parsing --image_path ./assets/showcase_dots_ocr_1_5/origin/webpage_1.png
+python3 ./demo/demo_vllm.py --prompt_mode prompt_web_parsing --image_path ./assets/showcase/origin/webpage_1.png
 ## scene spoting
-python3 ./demo/demo_vllm.py --prompt_mode prompt_scene_spotting --image_path ./assets/showcase_dots_ocr_1_5/origin/scene_1.jpg
+python3 ./demo/demo_vllm.py --prompt_mode prompt_scene_spotting --image_path ./assets/showcase/origin/scene_1.jpg
 ## image parsing with svg code
 python3 ./demo/demo_vllm_svg.py --prompt_mode prompt_image_to_svg 
 ## general qa
@@ -586,9 +606,9 @@ python3 demo/demo_hf.py
 import torch
 from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
 from qwen_vl_utils import process_vision_info
-from dots_ocr.utils import dict_promptmode_to_prompt
+from dots_mocr.utils import dict_promptmode_to_prompt
 
-model_path = "./weights/DotsOCR_1_5"
+model_path = "./weights/DotsMOCR"
 model = AutoModelForCausalLM.from_pretrained(
     model_path,
     attn_implementation="flash_attention_2",
@@ -672,21 +692,21 @@ Please refer to [CPU inference](https://github.com/rednote-hilab/dots.ocr/issues
 
 # Parse all layout info, both detection and recognition
 # Parse a single image
-python3 dots_ocr/parser.py demo/demo_image1.jpg
+python3 dots_mocr/parser.py demo/demo_image1.jpg
 # Parse a single PDF
-python3 dots_ocr/parser.py demo/demo_pdf1.pdf  --num_thread 64  # try bigger num_threads for pdf with a large number of pages
+python3 dots_mocr/parser.py demo/demo_pdf1.pdf  --num_thread 64  # try bigger num_threads for pdf with a large number of pages
 
 # Layout detection only
-python3 dots_ocr/parser.py demo/demo_image1.jpg --prompt prompt_layout_only_en
+python3 dots_mocr/parser.py demo/demo_image1.jpg --prompt prompt_layout_only_en
 
 # Parse text only, except Page-header and Page-footer
-python3 dots_ocr/parser.py demo/demo_image1.jpg --prompt prompt_ocr
+python3 dots_mocr/parser.py demo/demo_image1.jpg --prompt prompt_ocr
 
 
 ```
 **Based on Transformers**, you can parse an image or a pdf file using the same commands above, just add `--use_hf true`. 
 
-> Notice: transformers is slower than vllm, if you want to use demo/* with transformers，just add `use_hf=True` in `DotsOCRParser(..,use_hf=True)`
+> Notice: transformers is slower than vllm, if you want to use demo/* with transformers，just add `use_hf=True` in `DotsMOCRParser(..,use_hf=True)`
 
 <details>
 <summary><b>Output Results</b></summary>
@@ -704,32 +724,32 @@ Have fun with the [live demo](https://dotsocr.xiaohongshu.com/).
 
 
 ### Examples for document parsing
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/formula1.png" alt="formula1.png" border="0" />
+<img src="assets/showcase/result/formula1.png" alt="formula1.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/table3.png" alt="table3.png" border="0" />
+<img src="assets/showcase/result/table3.png" alt="table3.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/Tibetan.png" alt="Tibetan.png" border="0" />
+<img src="assets/showcase/result/Tibetan.png" alt="Tibetan.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/tradition_zh.png" alt="tradition_zh.png" border="0" />
+<img src="assets/showcase/result/tradition_zh.png" alt="tradition_zh.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/nl.png" alt="nl.png" border="0" />
+<img src="assets/showcase/result/nl.png" alt="nl.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/kannada.png" alt="kannada.png" border="0" />
+<img src="assets/showcase/result/kannada.png" alt="kannada.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/russian.png" alt="russian.png" border="0" />
+<img src="assets/showcase/result/russian.png" alt="russian.png" border="0" />
 
 
 ### Examples for image parsing
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase_dots_ocr_1_5/result/svg_1.png" alt="svg_1.png" border="0" />
+<img src="assets/showcase/result/svg_1.png" alt="svg_1.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase_dots_ocr_1_5/result/svg_2.png" alt="svg_2.png" border="0" />
+<img src="assets/showcase/result/svg_2.png" alt="svg_2.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase_dots_ocr_1_5/result/svg_4.png" alt="svg_4.png" border="0" />
+<img src="assets/showcase/result/svg_4.png" alt="svg_4.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase_dots_ocr_1_5/result/svg_5.png" alt="svg_5.png" border="0" />
+<img src="assets/showcase/result/svg_5.png" alt="svg_5.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase_dots_ocr_1_5/result/svg_6.png" alt="svg_6.png" border="0" />
+<img src="assets/showcase/result/svg_6.png" alt="svg_6.png" border="0" />
 
 > **Note:**
-> - Inferenced by dots.ocr-1.5-svg
+> - Inferenced by dots.mocr-svg
 
 ### Example for web parsing
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase_dots_ocr_1_5/result/webpage_1.png" alt="webpage_1.png" border="0" />
+<img src="assets/showcase/result/webpage_1.png" alt="webpage_1.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase_dots_ocr_1_5/result/webpage_2.png" alt="webpage_2.png" border="0" />
+<img src="assets/showcase/result/webpage_2.png" alt="webpage_2.png" border="0" />
 
 ### Examples for scene spotting
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase_dots_ocr_1_5/result/scene_1.png" alt="scene_1.png" border="0" />
+<img src="assets/showcase/result/scene_1.png" alt="scene_1.png" border="0" />
-<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase_dots_ocr_1_5/result/scene_2.png" alt="scene_2.png" border="0" />
+<img src="assets/showcase/result/scene_2.png" alt="scene_2.png" border="0" />
 
 
 # Limitation & Future Work
@@ -743,6 +763,18 @@ Have fun with the [live demo](https://dotsocr.xiaohongshu.com/).
 
 # Citation
 
+```BibTeX
+@misc{zheng2026multimodalocrparsedocuments,
+      title={Multimodal OCR: Parse Anything from Documents}, 
+      author={Handong Zheng and Yumeng Li and Kaile Zhang and Liang Xin and Guangwei Zhao and Hao Liu and Jiayu Chen and Jie Lou and Jiyu Qiu and Qi Fu and Rui Yang and Shuo Jiang and Weijian Luo and Weijie Su and Weijun Zhang and Xingyu Zhu and Yabin Li and Yiwei ma and Yu Chen and Zhaohui Yu and Guang Yang and Colin Zhang and Lei Zhang and Yuliang Liu and Xiang Bai},
+      year={2026},
+      eprint={2603.13032},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2603.13032}, 
+}
+```
+
 ```BibTeX
 @misc{li2025dotsocrmultilingualdocumentlayout,
       title={dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model}, 

demo/demo_gradio.py

@@ -35,21 +35,143 @@ DEFAULT_CONFIG = {
     'port_vllm': 8000,
     'min_pixels': MIN_PIXELS,
     'max_pixels': MAX_PIXELS,
-    'test_images_dir': "./assets/showcase_origin",
+    'test_images_dir': "./assets/showcase/origin",
 }
 
+# ==================== Multi-Model Server Configuration ====================
+MODEL_SERVERS = {
+    "dots.mocr": {
+        'ip': "127.0.0.1",
+        'port_vllm': 8000,
+        'description': "dots.mocr"
+    },
+    "dots.mocr-svg": {
+        'ip': "127.0.0.1",
+        'port_vllm': 8000,  # 请根据实际情况修改端口
+        'description': "dots.mocr-svg"
+    },
+}
+
+
+
+#每个prompt的预处理写死
+PROMPT_TO_FITZ_PREPROCESS = {
+    "prompt_layout_all_en": True,      # 文档布局分析 - 启用预处理
+    "prompt_layout_only_en": True,    # 仅布局检测 - 启用预处理
+    "prompt_ocr": True,               # 仅文字识别 - 启用预处理
+    "prompt_web_parsing": False,       # 网页解析 - 禁用预处理
+    "prompt_scene_spotting": False,   # 场景检测 - 禁用预处理
+    "prompt_image_to_svg": False,     # SVG 转换 - 禁用预处理
+    "prompt_general": False,          # 自由问答 - 禁用预处理
+}
+
+#不同任务需要不同temperature
+PROMPT_TO_TEMPERATURE = {
+    "prompt_layout_all_en": 0.1,      # 文档布局分析 - 低温度，更确定性
+    "prompt_layout_only_en": 0.1,     # 仅布局检测 - 低温度
+    "prompt_ocr": 0.1,                # OCR 识别 - 低温度
+    "prompt_web_parsing": 0.1,        # 网页解析 - 稍高一点
+    "prompt_scene_spotting": 0.1,     # 场景检测 - 中等温度
+    "prompt_image_to_svg": 0.9,       # SVG 转换 - 较低温度
+    "prompt_general": 0.1,            # 自由问答 - 高温度，更有创造性
+}
+
+# 不同prompt_mode对应的模型
+PROMPT_TO_MODEL = {
+    "prompt_image_to_svg": "dots.mocr-svg",  # SVG任务使用SVG模型
+}
+
+# ==================== Demo Case Configuration ====================
+# 根据文件名自动选择 prompt_mode 和预设的 custom_prompt
+DEMO_CASE_CONFIG = {
+    # 格式: "文件名关键字": {"prompt_mode": "xxx", "custom_prompt": "xxx"}
+    
+    # 布局分析类
+    "doc": {"prompt_mode": "prompt_layout_all_en"},
+    "formula": {"prompt_mode": "prompt_layout_all_en"},
+    "table": {"prompt_mode": "prompt_layout_all_en"},
+    
+    # 仅布局检测
+    "detect": {"prompt_mode": "prompt_layout_only_en"},
+    # OCR 识别
+    "ocr": {"prompt_mode": "prompt_ocr"},
+
+    # 网页解析
+    "webpage": {"prompt_mode": "prompt_web_parsing"},
+
+    # 场景文字检测
+    "scene": {"prompt_mode": "prompt_scene_spotting"},
+
+    # SVG 转换
+    "svg": {"prompt_mode": "prompt_image_to_svg"},
+
+    # QA 任务（带预设 prompt）
+    "general_qa": {
+        "prompt_mode": "prompt_general", 
+        "custom_prompt": "Across panels 1-12 plotting against clean accuracy, which variable appears most positively correlated with clean accuracy?"
+    },
+
+
+}
+
+# 默认配置（找不到匹配时使用）
+DEFAULT_DEMO_CONFIG = {"prompt_mode": "prompt_layout_all_en"}
+
+def get_config_for_file(file_path):
+    """
+    根据文件名自动匹配 prompt_mode 和 custom_prompt
+    支持部分匹配（文件名包含关键字即可）
+    """
+    if not file_path:
+        return DEFAULT_DEMO_CONFIG.copy()
+    
+    filename = os.path.basename(file_path).lower()
+    
+    # 遍历配置字典，查找匹配的关键字
+    for keyword, config in DEMO_CASE_CONFIG.items():
+        if keyword.lower() in filename:
+            return config.copy()
+    
+    # 没有匹配则返回默认配置
+    return DEFAULT_DEMO_CONFIG.copy()
+
 # ==================== Global Variables ====================
 # Store current configuration
 current_config = DEFAULT_CONFIG.copy()
 
-# Create DotsOCRParser instance
+# Parser cache for multiple models
-dots_parser = DotsOCRParser(
+_parser_cache = {}
-    ip=DEFAULT_CONFIG['ip'],
+
-    port=DEFAULT_CONFIG['port_vllm'],
+def get_parser(model_name: str, min_pixels: int = None, max_pixels: int = None) -> DotsMOCRParser:
+    """
+    Get or create a parser instance for the specified model.
+    Uses cache to avoid recreating parsers for the same model.
+    """
+    if model_name not in MODEL_SERVERS:
+        raise ValueError(f"Unknown model: {model_name}")
+    
+    model_config = MODEL_SERVERS[model_name]
+    
+    # Create cache key based on model and pixel settings
+    cache_key = model_name
+    
+    # If parser exists in cache, update its settings and return
+    if cache_key in _parser_cache:
+        parser = _parser_cache[cache_key]
+        parser.min_pixels = min_pixels or DEFAULT_CONFIG['min_pixels']
+        parser.max_pixels = max_pixels or DEFAULT_CONFIG['max_pixels']
+        return parser
+    
+    # Create new parser instance
+    parser = DotsMOCRParser(
+        ip=model_config['ip'],
+        port=model_config['port_vllm'],
         dpi=200,
-    min_pixels=DEFAULT_CONFIG['min_pixels'],
+        min_pixels=min_pixels or DEFAULT_CONFIG['min_pixels'],
-    max_pixels=DEFAULT_CONFIG['max_pixels']
+        max_pixels=max_pixels or DEFAULT_CONFIG['max_pixels']
     )
+    _parser_cache[cache_key] = parser
+    return parser
 
 def get_initial_session_state():
     return {
@@ -71,7 +193,8 @@ def get_initial_session_state():
             "file_type": None,
             "is_parsed": False,
             "results": []
-        }
+        },
+        'auto_custom_prompt': None,
     }
 
 def read_image_v2(img):
@@ -118,6 +241,46 @@ def load_file_for_preview(file_path, session_state):
     
     return pages[0], f"<div id='page_info_box'>1 / {len(pages)}</div>", session_state
 
+def on_test_image_select(file_path, session_state):
+    """选择测试图片时的回调：加载预览 + 自动设置 prompt_mode + 自动切换模型"""
+    preview_image, page_info, session_state = load_file_for_preview(file_path, session_state)
+    
+    if not file_path:
+        return (
+            preview_image, 
+            page_info, 
+            session_state,
+            gr.update(), 
+            gr.update(), 
+            gr.update()
+        )
+    
+    auto_config = get_config_for_file(file_path)
+    prompt_mode_value = auto_config["prompt_mode"]
+    custom_prompt_value = auto_config.get("custom_prompt", "")
+    
+    session_state['auto_custom_prompt'] = custom_prompt_value if custom_prompt_value else None
+    
+    is_free_qa = prompt_mode_value == 'prompt_general'
+    if is_free_qa and custom_prompt_value:
+        prompt_text = custom_prompt_value
+    else:
+        prompt_text = update_prompt_display(prompt_mode_value)
+    
+    # 根据prompt_mode自动选择模型
+    auto_model = PROMPT_TO_MODEL.get(prompt_mode_value, list(MODEL_SERVERS.keys())[0])
+    
+    return (
+        preview_image, 
+        page_info, 
+        session_state,
+        gr.update(value=prompt_mode_value),
+        gr.update(value=prompt_text, interactive=is_free_qa),
+        gr.update(value=auto_model),
+    )
+
+
+
 def turn_page(direction, session_state):
     """Page turning function"""
     pdf_cache = session_state['pdf_cache']
@@ -152,20 +315,23 @@ def get_test_images():
     test_images = []
     test_dir = current_config['test_images_dir']
     if os.path.exists(test_dir):
-        test_images = [os.path.join(test_dir, name) for name in os.listdir(test_dir) 
+        test_images = sorted([
-                      if name.lower().endswith(('.png', '.jpg', '.jpeg', '.pdf'))]
+            os.path.join(test_dir, name) 
+            for name in os.listdir(test_dir) 
+            if name.lower().endswith(('.png', '.jpg', '.jpeg', '.pdf'))
+        ])
     return test_images
 
 def create_temp_session_dir():
     """Creates a unique temporary directory for each processing request"""
     session_id = uuid.uuid4().hex[:8]
-    temp_dir = os.path.join(tempfile.gettempdir(), f"dots_ocr_demo_{session_id}")
+    temp_dir = os.path.join(tempfile.gettempdir(), f"dots_mocr_demo_{session_id}")
     os.makedirs(temp_dir, exist_ok=True)
     return temp_dir, session_id
 
-def parse_image_with_high_level_api(parser, image, prompt_mode, fitz_preprocess=False):
+def parse_image_with_high_level_api(parser, image, prompt_mode, fitz_preprocess=False, custom_prompt=None, temperature=None):
     """
-    Processes using the high-level API parse_image from DotsOCRParser
+    Processes using the high-level API parse_image from DotsMOCRParser
     """
     # Create a temporary session directory
     temp_dir, session_id = create_temp_session_dir()
@@ -182,7 +348,9 @@ def parse_image_with_high_level_api(parser, image, prompt_mode, fitz_preprocess=
             filename=filename, 
             prompt_mode=prompt_mode,
             save_dir=temp_dir,
-            fitz_preprocess=fitz_preprocess
+            fitz_preprocess=fitz_preprocess,
+            custom_prompt=custom_prompt,
+            temperature=temperature,
         )
         
         # Parse the results
@@ -223,7 +391,7 @@ def parse_image_with_high_level_api(parser, image, prompt_mode, fitz_preprocess=
 
 def parse_pdf_with_high_level_api(parser, pdf_path, prompt_mode):
     """
-    Processes using the high-level API parse_pdf from DotsOCRParser
+    Processes using the high-level API parse_pdf from DotsMOCRParser
     """
     # Create a temporary session directory
     temp_dir, session_id = create_temp_session_dir()
@@ -292,8 +460,9 @@ def parse_pdf_with_high_level_api(parser, pdf_path, prompt_mode):
 
 # ==================== Core Processing Function ====================
 def process_image_inference(session_state, test_image_input, file_input,
-                          prompt_mode, server_ip, server_port, min_pixels, max_pixels,
+                          prompt_mode, model_selector,  # Changed: use model_selector instead of server_ip/port
-                          fitz_preprocess=False
+                          min_pixels, max_pixels,
+                          fitz_preprocess=False, custom_prompt=""
                           ):
     """Core function to handle image/PDF inference"""
     # Use session_state instead of global variables
@@ -310,18 +479,23 @@ def process_image_inference(session_state, test_image_input, file_input,
     session_state['processing_results'] = get_initial_session_state()['processing_results']
     processing_results = session_state['processing_results']
     
+    fitz_preprocess = PROMPT_TO_FITZ_PREPROCESS.get(prompt_mode, True)
+    temperature = PROMPT_TO_TEMPERATURE.get(prompt_mode, 0.1) 
+    print(temperature)
+    # Get the selected model configuration
+    model_config = MODEL_SERVERS[model_selector]
     current_config.update({
-        'ip': server_ip,
+        'ip': model_config['ip'],
-        'port_vllm': server_port,
+        'port_vllm': model_config['port_vllm'],
         'min_pixels': min_pixels,
         'max_pixels': max_pixels
     })
     
-    # Update parser configuration
+    # Get parser for the selected model
-    dots_parser.ip = server_ip
+    try:
-    dots_parser.port = server_port
+        dots_parser = get_parser(model_selector, min_pixels, max_pixels)
-    dots_parser.min_pixels = min_pixels
+    except ValueError as e:
-    dots_parser.max_pixels = max_pixels
+        return None, f"Error: {str(e)}", "", "", gr.update(value=None), None, "", session_state
     
     input_file_path = file_input if file_input else test_image_input
     
@@ -348,7 +522,7 @@ def process_image_inference(session_state, test_image_input, file_input,
             })
             
             total_elements = len(pdf_result['combined_cells_data'])
-            info_text = f"**PDF Information:**\n- Total Pages: {pdf_result['total_pages']}\n- Server: {current_config['ip']}:{current_config['port_vllm']}\n- Total Detected Elements: {total_elements}\n- Session ID: {pdf_result['session_id']}"
+            info_text = f"**PDF Information:**\n- Total Pages: {pdf_result['total_pages']}\n- Model: {model_selector}\n- Server: {model_config['ip']}:{model_config['port_vllm']}\n- Total Detected Elements: {total_elements}\n- Session ID: {pdf_result['session_id']}"
             
             current_page_layout_image = preview_image
             current_page_json = ""
@@ -381,10 +555,11 @@ def process_image_inference(session_state, test_image_input, file_input,
             session_state['pdf_cache'] = get_initial_session_state()['pdf_cache']
             
             original_image = image
-            parse_result = parse_image_with_high_level_api(dots_parser, image, prompt_mode, fitz_preprocess)
+            effective_custom_prompt = custom_prompt if prompt_mode == 'prompt_general' else None
+            parse_result = parse_image_with_high_level_api(dots_parser, image, prompt_mode, fitz_preprocess, effective_custom_prompt, temperature)
             
             if parse_result['filtered']:
-                 info_text = f"**Image Information:**\n- Original Size: {original_image.width} x {original_image.height}\n- Processing: JSON parsing failed, using cleaned text output\n- Server: {current_config['ip']}:{current_config['port_vllm']}\n- Session ID: {parse_result['session_id']}"
+                 info_text = f"**Image Information:**\n- Original Size: {original_image.width} x {original_image.height}\n- Model: {model_selector}\n- Processing: JSON parsing failed, using cleaned text output\n- Server: {model_config['ip']}:{model_config['port_vllm']}\n- Session ID: {parse_result['session_id']}"
                  processing_results.update({
                      'original_image': original_image, 'markdown_content': parse_result['md_content'],
                      'temp_dir': parse_result['temp_dir'], 'session_id': parse_result['session_id'],
@@ -401,7 +576,7 @@ def process_image_inference(session_state, test_image_input, file_input,
             })
             
             num_elements = len(parse_result['cells_data']) if parse_result['cells_data'] else 0
-            info_text = f"**Image Information:**\n- Original Size: {original_image.width} x {original_image.height}\n- Model Input Size: {parse_result['input_width']} x {parse_result['input_height']}\n- Server: {current_config['ip']}:{current_config['port_vllm']}\n- Detected {num_elements} layout elements\n- Session ID: {parse_result['session_id']}"
+            info_text = f"**Image Information:**\n- Original Size: {original_image.width} x {original_image.height}\n- Model Input Size: {parse_result['input_width']} x {parse_result['input_height']}\n- Model: {model_selector}\n- Server: {model_config['ip']}:{model_config['port_vllm']}\n- Detected {num_elements} layout elements\n- Session ID: {parse_result['session_id']}"
             
             current_json = json.dumps(parse_result['cells_data'], ensure_ascii=False, indent=2) if parse_result['cells_data'] else ""
             
@@ -452,6 +627,8 @@ def clear_all_data(session_state):
 
 def update_prompt_display(prompt_mode):
     """Updates the prompt display content"""
+    if prompt_mode == 'prompt_general':
+        return ""  # free_qa 模式下清空，让用户输入
     return dict_promptmode_to_prompt[prompt_mode]
 
 # ==================== Gradio Interface ====================
@@ -515,18 +692,22 @@ def create_gradio_interface():
     #markdown_tabs {
         height: 100%;
     }
+
+    #model_selector_box {
+        margin-bottom: 8px;
+    }
     """
     
-    with gr.Blocks(theme="ocean", css=css, title='dots.ocr') as demo:
+    with gr.Blocks(theme="ocean", css=css, title='dots.mocr') as demo:
         session_state = gr.State(value=get_initial_session_state())
         
         # Title
         gr.HTML("""
             <div style="display: flex; align-items: center; justify-content: center; margin-bottom: 20px;">
-                <h1 style="margin: 0; font-size: 2em;">🔍 dots.ocr</h1>
+                <h1 style="margin: 0; font-size: 2em;">🔍 dots.mocr</h1>
             </div>
             <div style="text-align: center; margin-bottom: 10px;">
-                <em>Supports image/PDF layout analysis and structured output</em>
+                <em>Recognize Any Human Scripts and Symbols</em>
             </div>
         """)
         
@@ -540,6 +721,15 @@ def create_gradio_interface():
                     file_types=[".pdf", ".jpg", ".jpeg", ".png"],
                 )
                 
+                # ============ NEW: Model Selector ============
+                model_selector = gr.Dropdown(
+                    label="🤖 Select Model",
+                    choices=list(MODEL_SERVERS.keys()),
+                    value=list(MODEL_SERVERS.keys())[0],
+                    elem_id="model_selector_box",
+                    info="Switch between different model servers"
+                )
+                
                 test_images = get_test_images()
                 test_image_input = gr.Dropdown(
                     label="Or Select an Example",
@@ -550,7 +740,15 @@ def create_gradio_interface():
                 gr.Markdown("### ⚙️ Prompt & Actions")
                 prompt_mode = gr.Dropdown(
                     label="Select Prompt",
-                    choices=["prompt_layout_all_en", "prompt_layout_only_en", "prompt_ocr"],
+                        choices=[
+                            "prompt_layout_all_en", 
+                            "prompt_web_parsing",
+                            "prompt_scene_spotting",         
+                            "prompt_image_to_svg",       
+                            "prompt_general",
+                            "prompt_layout_only_en", 
+                            "prompt_ocr",            
+                        ],
                     value="prompt_layout_all_en",
                 )
                 
@@ -560,8 +758,7 @@ def create_gradio_interface():
                     value=dict_promptmode_to_prompt[list(dict_promptmode_to_prompt.keys())[0]],
                     lines=4,
                     max_lines=8,
-                    interactive=False,
+                    interactive=False,  # 默认不可编辑，free_qa 模式下改为可编辑
-                    show_copy_button=True
                 )
                 
                 with gr.Row():
@@ -572,11 +769,9 @@ def create_gradio_interface():
                     fitz_preprocess = gr.Checkbox(
                         label="Enable fitz_preprocess for images", 
                         value=True,
-                        info="Processes image via a PDF-like pipeline (image->pdf->200dpi image). Recommended if your image DPI is low."
+                        info="Processes image via a PDF-like pipeline (image->pdf->200dpi image). Recommended if your image DPI is low.",
+                        visible=False, ###直接隐藏，调用模型前根据prompt mode 写死
                     )
-                    with gr.Row():
-                        server_ip = gr.Textbox(label="Server IP", value=DEFAULT_CONFIG['ip'])
-                        server_port = gr.Number(label="Port", value=DEFAULT_CONFIG['port_vllm'], precision=0)
                     with gr.Row():
                         min_pixels = gr.Number(label="Min Pixels", value=DEFAULT_CONFIG['min_pixels'], precision=0)
                         max_pixels = gr.Number(label="Max Pixels", value=DEFAULT_CONFIG['max_pixels'], precision=0)
@@ -590,7 +785,7 @@ def create_gradio_interface():
                             label="Layout Preview",
                             visible=True,
                             height=800,
-                            show_label=False
+                            show_label=False,
                         )
                         
                         # Page navigation (shown during PDF preview)
@@ -621,7 +816,6 @@ def create_gradio_interface():
                                         {"left": "$$", "right": "$$", "display": True},
                                         {"left": "$", "right": "$", "display": False}
                                     ],
-                                    show_copy_button=False,
                                     elem_id="markdown_output"
                                 )
                             
@@ -631,7 +825,6 @@ def create_gradio_interface():
                                     label="Markdown Raw Text",
                                     max_lines=100,
                                     lines=38,
-                                    show_copy_button=True,
                                     elem_id="markdown_output",
                                     show_label=False
                                 )
@@ -642,7 +835,6 @@ def create_gradio_interface():
                                     label="Current Page JSON",
                                     max_lines=100,
                                     lines=38,
-                                    show_copy_button=True,
                                     elem_id="markdown_output",
                                     show_label=False
                                 )
@@ -654,11 +846,32 @@ def create_gradio_interface():
                         visible=False
                     )
 
-        # When the prompt mode changes, update the display content
+        def update_prompt_and_interactive(prompt_mode, session_state):
+            """更新 prompt_display 并自动切换模型"""
+            is_free_qa = prompt_mode == 'prompt_general'
+            auto_custom_prompt = session_state.get('auto_custom_prompt')
+            
+            if is_free_qa and auto_custom_prompt:
+                prompt_text = auto_custom_prompt
+                interactive = True
+            else:
+                prompt_text = update_prompt_display(prompt_mode)
+                interactive = is_free_qa
+            
+            # 根据prompt_mode自动选择模型
+            auto_model = PROMPT_TO_MODEL.get(prompt_mode, list(MODEL_SERVERS.keys())[0])
+            
+            return (
+                gr.update(value=prompt_text, interactive=interactive), 
+                session_state,
+                gr.update(value=auto_model),
+            )
+                
+
         prompt_mode.change(
-            fn=update_prompt_display,
+            fn=update_prompt_and_interactive,
-            inputs=prompt_mode,
+            inputs=[prompt_mode, session_state],
-            outputs=prompt_display,
+            outputs=[prompt_display, session_state, model_selector],
         )
         
         # Show preview on file upload
@@ -671,10 +884,9 @@ def create_gradio_interface():
         
         # Also handle test image selection
         test_image_input.change(
-            # fn=lambda path, state: load_file_for_preview(path, state),
+            fn=on_test_image_select,
-            fn=load_file_for_preview,
             inputs=[test_image_input, session_state],
-            outputs=[result_image, page_info, session_state]
+            outputs=[result_image, page_info, session_state, prompt_mode, prompt_display, model_selector],
         )
 
         prev_btn.click(
@@ -689,12 +901,14 @@ def create_gradio_interface():
             outputs=[result_image, page_info, current_page_json, session_state]
         )
         
+        # ============ MODIFIED: process_btn.click with model_selector ============
         process_btn.click(
             fn=process_image_inference,
             inputs=[
                 session_state, test_image_input, file_input,
-                prompt_mode, server_ip, server_port, min_pixels, max_pixels, 
+                prompt_mode, model_selector,  # Changed: model_selector instead of server_ip/port
-                fitz_preprocess
+                min_pixels, max_pixels, 
+                fitz_preprocess, prompt_display
             ],
             outputs=[
                 result_image, info_display, md_output, md_raw_output,

demo/demo_hf.py

@@ -53,13 +53,14 @@ def inference(image_path, prompt, model, processor):
 
 
 if __name__ == "__main__":
-    # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+    # We recommend enabling flash_attention_2 or flash_attention_3 for better acceleration and memory saving, especially in multi-image and video scenarios.
-    model_path = "./weights/DotsOCR"
+    model_path = "./weights/DotsMOCR"
     model = AutoModelForCausalLM.from_pretrained(
         model_path,
         attn_implementation="flash_attention_2",
         torch_dtype=torch.bfloat16,
         device_map="auto",
+        # device_map="cpu",  # ve里默认使用flash-attn，无法直接运行
         trust_remote_code=True
     )
     processor = AutoProcessor.from_pretrained(model_path,  trust_remote_code=True)

demo/demo_vllm.py

@@ -10,7 +10,7 @@ from dots_ocr.model.inference import inference_with_vllm
 parser = argparse.ArgumentParser()
 parser.add_argument("--ip", type=str, default="localhost")
 parser.add_argument("--port", type=str, default="8000")
-parser.add_argument("--model_name", type=str, default="rednote-hilab/dots.ocr-1.5")
+parser.add_argument("--model_name", type=str, default="rednote-hilab/dots.mocr")
 parser.add_argument("--image_path", type=str, default="demo/demo_image1.jpg")
 parser.add_argument("--prompt_mode", type=str, default="prompt_layout_all_en",help=(
         "Choose a task prompt: "

demo/demo_vllm_general.py

@@ -10,7 +10,7 @@ from dots_ocr.model.inference import inference_with_vllm
 parser = argparse.ArgumentParser()
 parser.add_argument("--ip", type=str, default="localhost")
 parser.add_argument("--port", type=str, default="8000")
-parser.add_argument("--model_name", type=str, default="rednote-hilab/dots.ocr-1.5")
+parser.add_argument("--model_name", type=str, default="rednote-hilab/dots.mocr")
 parser.add_argument("--custom_prompt", type=str, default="Please describe the content of this image.")
 
 args = parser.parse_args()

demo/demo_vllm_svg.py

@@ -10,7 +10,7 @@ from dots_ocr.model.inference import inference_with_vllm
 parser = argparse.ArgumentParser()
 parser.add_argument("--ip", type=str, default="localhost")
 parser.add_argument("--port", type=str, default="8000")
-parser.add_argument("--model_name", type=str, default="rednote-hilab/dots.ocr-1.5-svg")
+parser.add_argument("--model_name", type=str, default="rednote-hilab/dots.mocr")
 parser.add_argument("--prompt_mode", type=str, default="prompt_image_to_svg")
 
 args = parser.parse_args()

demo/launch_model_vllm.sh

@@ -1,17 +1 @@
-# download model to /path/to/model
+CUDA_VISIBLE_DEVICES=0 nohup vllm serve dots.mocr --tensor-parallel-size 1 --gpu-memory-utilization 0.9  --chat-template-content-format string --served-model-name ${model_name} --trust-remote-code
-if [ -z "$NODOWNLOAD" ]; then
-    python3 tools/download_model.py
-fi
-
-# register model to vllm
-hf_model_path=./weights/DotsOCR  # Path to your downloaded model weights
-export PYTHONPATH=$(dirname "$hf_model_path"):$PYTHONPATH
-sed -i '/^from vllm\.entrypoints\.cli\.main import main$/a\
-from DotsOCR import modeling_dots_ocr_vllm' `which vllm`
-
-# launch vllm server
-model_name=model
-CUDA_VISIBLE_DEVICES=0 vllm serve ${hf_model_path} --tensor-parallel-size 1 --gpu-memory-utilization 0.95  --chat-template-content-format string --served-model-name ${model_name} --trust-remote-code
-
-# # run python demo after launch vllm server
-# python demo/demo_vllm.py

dots.ocr LICENSE AGREEMENT

@@ -1,12 +1,12 @@
-dots.ocr LICENSE AGREEMENT
+dots.mocr LICENSE AGREEMENT
 
 Effective Date: [ August 8, 2025]
 
 Copyright Holder: [Xingyin Information Technology (Shanghai) Co., Ltd]
 
-This License Agreement (“Agreement”) governs Your use, reproduction, modification, and distribution of dots.ocr (the "Model Materials"). This Agreement is designed to maximize the openness and use of the Model Materials while addressing the unique legal, ethical, and technical challenges posed by large language models.
+This License Agreement (“Agreement”) governs Your use, reproduction, modification, and distribution of dots.mocr (the "Model Materials"). This Agreement is designed to maximize the openness and use of the Model Materials while addressing the unique legal, ethical, and technical challenges posed by large language models.
 
-WHEREAS, Licensor has developed the dots.ocr document parsing model and intends to distribute the Model Materials under an open‑source framework;
+WHEREAS, Licensor has developed the dots.mocr document parsing model and intends to distribute the Model Materials under an open‑source framework;
 WHEREAS, traditional open-source licenses (e.g., the MIT License) may not fully address the complexity inherent complexities of document parsing models, namely their multiple components (code, weights, training data), potential ethical risks, data‑governance issues, and intellectual‑property and liability questions regarding AI‑generated content;
 WHEREAS, Licensor seeks to provide a legal framework that ensures maximum access to and use of the Model Materials while clearly defining the rights, obligations, and liabilities of Licensee;
 
@@ -24,7 +24,7 @@ Purpose: To define key terms used in this Agreement, particularly "Model Materia
         (b) all associated preprocessing, training, inference, and fine‑tuning code;
         (c) training datasets and evaluation scripts (or their detailed descriptions and access mechanisms); and
         (d) any accompanying documentation, metadata, and tools.
-The above Model Materials shall be subject to the content published on the Licensor’s website or GitHub repository at https://github.com/rednote-hilab/dots.ocr.
+The above Model Materials shall be subject to the content published on the Licensor’s website or GitHub repository at https://github.com/rednote-hilab/dots.mocr.
 
 1.4 “Outputs” shall mean any content generated through the use of the Model Materials, such as text, tables, code,layout information, and formulas extracted from documents.
 
@@ -63,7 +63,7 @@ Purpose: To grant broad, permissive rights to the Licensee for the Model Materia
 If Licensee institutes patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model Materials constitute direct or contributory patent infringement, then any patent licenses granted under this License for the Model Materials shall terminate as of the date such litigation is asserted or filed.
 
 4.3   Outputs: The Outputs generated through the use of the Model Materials generally refer to text, tables, layouts, and other content extracted from documents or images. The extracted content itself does not generate new intellectual property rights, and all intellectual property remains with the original authors or copyright holders. The Licensee is responsible for due diligence regarding the legality of the Outputs, particularly where the content extracted by the OCR model may be substantially similar to existing copyrighted works, which could present intellectual property infringement risks. The Licensor assumes no liability for such infringements.
-4.4   Trademarks. Nothing in this License permits Licensee to make use of Licensor’s trademarks, trade names, logos (e.g., “rednote,” “Xiaohongshu,” “dots.ocr”) or to otherwise suggest endorsement or misrepresent the relationship between the parties, unless Licensor’s prior written approval is granted.
+4.4   Trademarks. Nothing in this License permits Licensee to make use of Licensor’s trademarks, trade names, logos (e.g., “rednote,” “Xiaohongshu,” “dots.mocr”) or to otherwise suggest endorsement or misrepresent the relationship between the parties, unless Licensor’s prior written approval is granted.
 
 5. Data Governance, Privacy, and Security
  
@@ -94,7 +94,7 @@ If Licensee institutes patent litigation against any entity (including a cross-c
 
 7.2   Copyright and Notices. When distributing any part of the Model Materials, Licensee must retain all copyright, patent, trademark, and attribution notices included in the Model Materials.
 
-7.3   Attribution. Licensee is encouraged to prominently display the name of Licensor and the Model Materials in any public statements, products, or services that contain the Model Materials (or any derivative works thereof), to promote transparency and community trust. If Licensee distributes modified weights or fine‑tuned models based on the Model Materials, Licensee must prominently display the following statement in the related website or documentation: “Built with dots.ocr.”
+7.3   Attribution. Licensee is encouraged to prominently display the name of Licensor and the Model Materials in any public statements, products, or services that contain the Model Materials (or any derivative works thereof), to promote transparency and community trust. If Licensee distributes modified weights or fine‑tuned models based on the Model Materials, Licensee must prominently display the following statement in the related website or documentation: “Built with dots.mocr.”
 
 8. Governing Law and Dispute Resolution
 

dots_ocr/model/inference.py

@@ -13,7 +13,7 @@ def inference_with_vllm(
         temperature=0.1,
         top_p=0.9,
         max_completion_tokens=32768,
-        model_name='rednote-hilab/dots.ocr',
+        model_name='rednote-hilab/dots.mocr',
         system_prompt=None,
         ):
     

tools/download_model.py

@@ -5,11 +5,11 @@ import os
 if __name__ == '__main__':
     parser = ArgumentParser()
     parser.add_argument('--type', '-t', type=str, default="huggingface")
-    parser.add_argument('--name', '-n', type=str, default="rednote-hilab/dots.ocr-1.5")
+    parser.add_argument('--name', '-n', type=str, default="rednote-hilab/dots.mocr")
     args = parser.parse_args()
     script_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-    print(f"Attention: The model save dir dots.ocr should be replace by a name without `.` like DotsOCR, util we merge our code to transformers.")
+    print(f"Attention: The model save dir dots.mocr should be replace by a name without `.` like DotsMOCR, util we merge our code to transformers.")
-    model_dir = os.path.join(script_dir, "weights/DotsOCR_1_5")
+    model_dir = os.path.join(script_dir, "weights/DotsMOCR")
     if not os.path.exists(model_dir):
         os.makedirs(model_dir)
     if args.type == "huggingface":