ByteDance
/

Dolphin-v2

@@ -1,525 +1,147 @@
 ---
-license_name: qwen-research
-license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
 language:
-- en
-pipeline_tag: image-text-to-text
 tags:
-- multimodal
 library_name: transformers
 ---
-# Qwen2.5-VL-3B-Instruct
-<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
-</a>
-## Introduction
-In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL.
-#### Key Enhancements:
-* **Understand things visually**: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
-* **Being agentic**: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
-* **Understanding long videos and capturing events**: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments.
-* **Capable of visual localization in different formats**: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.
-* **Generating structured outputs**: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.
-#### Model Architecture Updates:
-* **Dynamic Resolution and Frame Rate Training for Video Understanding**:
-We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments.
-<p align="center">
-    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/>
-<p>
-* **Streamlined and Efficient Vision Encoder**
-We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.
-We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 3B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL).
-## Evaluation
-### Image benchmark
-| Benchmark | InternVL2.5-4B |Qwen2-VL-7B |Qwen2.5-VL-3B |
-| :--- | :---:  | :---: | :---: |
-| MMMU<sub>val</sub>  | 52.3 | 54.1 | 53.1|
-| MMMU-Pro<sub>val</sub>  | **32.7** | 30.5 | 31.6|
-| AI2D<sub>test</sub> | 81.4 | **83.0** | 81.5 |
-| DocVQA<sub>test</sub>  | 91.6 | 94.5 | **93.9** |
-| InfoVQA<sub>test</sub>  | 72.1 | 76.5 | **77.1** |
-| TextVQA<sub>val</sub>  | 76.8 | **84.3** | 79.3|
-| MMBench-V1.1<sub>test</sub>  | 79.3 | **80.7** | 77.6 |
-| MMStar | 58.3 | **60.7** | 55.9 |
-| MathVista<sub>testmini</sub>  | 60.5 | 58.2 | **62.3** |
-| MathVision<sub>full</sub>  | 20.9 | 16.3  | **21.2** |
-### Video benchmark
-| Benchmark | InternVL2.5-4B | Qwen2-VL-7B | Qwen2.5-VL-3B |
-| :--- | :---:  | :---: | :---: |
-| MVBench | 71.6 | 67.0 | 67.0 |
-| VideoMME | 63.6/62.3 | 69.0/63.3 | 67.6/61.5 |
-| MLVU | 48.3 | - | 68.2 |
-| LVBench | - | - | 43.3 |
-| MMBench-Video | 1.73 | 1.44 | 1.63 |
-| EgoSchema | - | - | 64.8 |
-| PerceptionTest | - | - | 66.9 |
-| TempCompass | - | - | 64.4 |
-| LongVideoBench | 55.2 | 55.6 | 54.2 |
-| CharadesSTA/mIoU | - | - | 38.8 |
-### Agent benchmark
-| Benchmarks              | Qwen2.5-VL-3B |
-|-------------------------|---------------|
-| ScreenSpot              |     55.5    |
-| ScreenSpot Pro          |     23.9    |
-| AITZ_EM                 |  	76.9    |
-| Android Control High_EM |    	63.7    |
-| Android Control Low_EM  |  	22.2    |
-| AndroidWorld_SR         | 	90.8  	|
-| MobileMiniWob++_SR      | 	67.9    |
-## Requirements
-The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
-```
-pip install git+https://github.com/huggingface/transformers accelerate
-```
-or you might encounter the following error:
-```
-KeyError: 'qwen2_5_vl'
-```
-## Quickstart
-Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers.
-The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
-```
-pip install git+https://github.com/huggingface/transformers accelerate
-```
-or you might encounter the following error:
-```
-KeyError: 'qwen2_5_vl'
-```
-We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
-```bash
-# It's highly recommanded to use `[decord]` feature for faster video loading.
-pip install qwen-vl-utils[decord]==0.0.8
-```
-If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video.
-### Using 🤗  Transformers to Chat
-Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
-```python
-from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
-from qwen_vl_utils import process_vision_info
-# default: Load the model on the available device(s)
-model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    "Qwen/Qwen2.5-VL-3B-Instruct", torch_dtype="auto", device_map="auto"
-)
-# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
-# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-#     "Qwen/Qwen2.5-VL-3B-Instruct",
-#     torch_dtype=torch.bfloat16,
-#     attn_implementation="flash_attention_2",
-#     device_map="auto",
-# )
-# default processer
-processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
-# The default range for the number of visual tokens per image in the model is 4-16384.
-# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
-# min_pixels = 256*28*28
-# max_pixels = 1280*28*28
-# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-# Inference: Generation of the output
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-<details>
-<summary>Multi image inference</summary>
-```python
-# Messages containing multiple images and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "file:///path/to/image1.jpg"},
-            {"type": "image", "image": "file:///path/to/image2.jpg"},
-            {"type": "text", "text": "Identify the similarities between these images."},
-        ],
-    }
-]
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-# Inference
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-</details>
-<details>
-<summary>Video inference</summary>
-```python
-# Messages containing a images list as a video and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": [
-                    "file:///path/to/frame1.jpg",
-                    "file:///path/to/frame2.jpg",
-                    "file:///path/to/frame3.jpg",
-                    "file:///path/to/frame4.jpg",
-                ],
-            },
-            {"type": "text", "text": "Describe this video."},
-        ],
-    }
-]
-# Messages containing a local video path and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": "file:///path/to/video1.mp4",
-                "max_pixels": 360 * 420,
-                "fps": 1.0,
-            },
-            {"type": "text", "text": "Describe this video."},
-        ],
-    }
-]
-# Messages containing a video url and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4",
-            },
-            {"type": "text", "text": "Describe this video."},
-        ],
-    }
-]
-#In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time.
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    fps=fps,
-    padding=True,
-    return_tensors="pt",
-    **video_kwargs,
-)
-inputs = inputs.to("cuda")
-# Inference
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one.
-| Backend     | HTTP | HTTPS |
-|-------------|------|-------|
-| torchvision >= 0.19.0 | ✅  | ✅   |
-| torchvision < 0.19.0  | ❌  | ❌   |
-| decord      | ✅  | ❌   |
-</details>
-<details>
-<summary>Batch inference</summary>
-```python
-# Sample messages for batch inference
-messages1 = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "file:///path/to/image1.jpg"},
-            {"type": "image", "image": "file:///path/to/image2.jpg"},
-            {"type": "text", "text": "What are the common elements in these pictures?"},
-        ],
-    }
-]
-messages2 = [
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "Who are you?"},
-]
-# Combine messages for batch processing
-messages = [messages1, messages2]
-# Preparation for batch inference
-texts = [
-    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
-    for msg in messages
-]
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=texts,
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-# Batch Inference
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_texts = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_texts)
-```
-</details>
-### 🤖 ModelScope
-We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints.
-### More Usage Tips
-For input images, we support local files, base64, and URLs. For videos, we currently only support local files.
-```python
-# You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text.
-## Local file path
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "file:///path/to/your/image.jpg"},
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-## Image URL
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "http://path/to/your/image.jpg"},
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-## Base64 encoded image
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "data:image;base64,/9j/..."},
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-```
-#### Image Resolution for performance boost
-The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage.
-```python
-min_pixels = 256 * 28 * 28
-max_pixels = 1280 * 28 * 28
-processor = AutoProcessor.from_pretrained(
-    "Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels
-)
-```
-Besides, We provide two methods for fine-grained control over the image size input to the model:
-1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels.
-2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28.
-```python
-# min_pixels and max_pixels
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "file:///path/to/your/image.jpg",
-                "resized_height": 280,
-                "resized_width": 420,
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-# resized_height and resized_width
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "file:///path/to/your/image.jpg",
-                "min_pixels": 50176,
-                "max_pixels": 50176,
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-```
-### Processing Long Texts
-The current `config.json` is set for context length up to 32,768 tokens.
-To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
-For supported frameworks, you could add the following to `config.json` to enable YaRN:
-```
-{
-	...,
-    "type": "yarn",
-    "mrope_section": [
-        16,
-        24,
-        24
-    ],
-    "factor": 4,
-    "original_max_position_embeddings": 32768
 }
 ```
-However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use.
-At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k.
-## Citation
-If you find our work helpful, feel free to give us a cite.
-```
-@misc{qwen2.5-VL,
-    title = {Qwen2.5-VL},
-    url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
-    author = {Qwen Team},
-    month = {January},
-    year = {2025}
-}
-@article{Qwen2VL,
-  title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
-  author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
-  journal={arXiv preprint arXiv:2409.12191},
-  year={2024}
-}
-@article{Qwen-VL,
-  title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
-  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
-  journal={arXiv preprint arXiv:2308.12966},
-  year={2023}
-}
-```

 ---
+license: mit
 language:
+  - zh
+  - en
 tags:
+  - document-parsing
+  - document-understanding
+  - document-intelligence
+  - ocr
+  - layout-analysis
+  - table-extraction
+  - formula-recognition
+  - code-extraction
+  - multimodal
+  - vision-language-model
+datasets:
+  - custom
+pipeline_tag: image-text-to-text
 library_name: transformers
 ---
+# Dolphin-v2: Universal Document Parsing via Scalable Anchor Prompting
+<a href="https://github.com/bytedance/Dolphin"><img src="https://img.shields.io/badge/Code-Github-blue"></a>
+## Model Description
+Dolphin-v2 is an enhanced universal document parsing model that substantially improves upon the original Dolphin. It seamlessly handles any document type—whether digital-born or photographed—through a document-type-aware two-stage architecture with scalable anchor prompting.
+## 📑 Key Improvements
+Dolphin-v2 introduces several major enhancements over the original Dolphin:
+- **🌐 Universal Document Support**: Handles both digital-born and photographed documents with realistic distortions
+- **📊 Expanded Element Coverage**: Supports 21 element categories (up from 14), including dedicated code blocks and formulas
+- **🎯 Enhanced Precision**: Uses absolute pixel coordinates for more accurate spatial localization
+- **⚡ Hybrid Parsing Strategy**: Element-wise parallel parsing for digital documents + holistic parsing for photographed documents
+- **🔬 Specialized Modules**: Dedicated parsing for code blocks with indentation preservation
+## 🏗️ Model Architecture
+Dolphin-v2 follows a document-type-aware two-stage paradigm:
+### Stage 1: Joint Classification and Layout Analysis
+- **Document Type Classification**: Distinguishes between digital-born and photographed documents
+- **Layout Analysis**: Generates element sequences in reading order with 21 supported categories
+- **Precise Localization**: Absolute coordinate system for pixel-level accuracy
+### Stage 2: Hybrid Content Parsing
+- **Photographed Documents**: Holistic page-level parsing to handle distortions
+- **Digital Documents**: Efficient element-wise parallel parsing with type-specific prompts
+  - `P_formula`: Specialized LaTeX generation for formulas
+  - `P_code`: Code block parsing with indentation preservation
+  - `P_table`: HTML representation for tables
+  - `P_paragraph`: Text recognition for paragraphs
+Built on **Qwen2.5-VL-3B** backbone with:
+- Vision encoder based on Native Resolution Vision Transformer (NaViT)
+- Autoregressive decoder for structured output generation
+## 📈 Performance
+Dolphin-v2 achieves superior performance on comprehensive benchmarks:
+**OmniDocBench (v1.5):**
+- Overall Score: **89.45** (+14.78 over original Dolphin)
+- Text Recognition: **0.054** Edit Distance
+- Formula Parsing: **86.72** CDM
+- Table Structure: **87.02** TEDS / **90.48** TEDS-S
+- Reading Order: **0.054** Edit Distance
+## 🎯 Supported Element Types
+Dolphin-v2 supports 21 document element categories:
+| Element Type | Description |
+|--------------|-------------|
+| `sec_0` - `sec_5` | Hierarchical headings (title, level 1-5) |
+| `para` | Regular paragraphs |
+| `half_para` | Spanning paragraphs |
+| `equ` | Mathematical formulas (LaTeX) |
+| `tab` | Tables (HTML) |
+| `code` | Code blocks (with indentation) |
+| `fig` | Figures |
+| `cap` | Captions |
+| `list` | Lists |
+| `catalogue` | Catalogs |
+| `reference` | References |
+| `header` / `foot` | Headers/Footers |
+| `fnote` | Footnotes |
+| `watermark` | Watermarks |
+| `anno` | Annotations |
+## 💻 Usage
+Please refer to our [GitHub repository](https://github.com/bytedance/Dolphin) for detailed usage:
+- Page-wise parsing for complete document images
+- Element-wise parsing for specific regions
+- Examples for digital and photographed documents
+## 🔧 Training Details
+- **Backbone**: Qwen2.5-VL-3B
+- **Training Data**:
+  - 200K photographed documents with realistic distortions
+  - 200K code images (C++, Python, Go, JavaScript)
+  - 200K catalog images with hierarchical structures
+- **Optimizer**: AdamW (lr=8e-5, weight decay=0)
+- **Training**: 10 epochs on 40 A100 GPUs
+- **Max Sequence Length**: 131,072 tokens
+## 📊 Benchmarks
+We evaluate on two complementary benchmarks:
+- **OmniDocBench**: Diverse document types (academic papers, textbooks, slides, reports)
+- **RealDoc-160**: Real-world photographed documents with authentic distortions
+## 🚀 Key Features
+✅ Handles both digital and photographed documents seamlessly
+✅ 21 element categories with fine-grained detection
+✅ Precise LaTeX formula recognition
+✅ Code block parsing with indentation preservation
+✅ Robust to distortions, lighting variations, and perspective changes
+✅ Efficient parallel processing for digital documents
+✅ Lightweight 3B parameter model
+## 📚 Citation
+```bibtex
+@inproceedings{dolphin2025,
+  title={Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting},
+  author={Feng, Hao and Wei, Shu and Fei, Xiang and Shi, Wei and Han, Yingdong and Liao, Lei and Lu, Jinghui and Wu, Binghong and Liu, Qi and Lin, Chunhui and Tang, Jingqun and Liu, Hao and Huang, Can},
+  booktitle={Proceedings of the 65th Annual Meeting of the Association for Computational Linguistics (ACL)},
+  year={2025}
 }
 ```
+## 🙏 Acknowledgements
+This model builds upon:
+- [Hugging Face Transformers](https://github.com/huggingface/transformers)
+- [Qwen2.5-VL](https://github.com/QwenLM/Qwen2-VL)
+- [Donut](https://github.com/clovaai/donut/)
+- [Nougat](https://github.com/facebookresearch/nougat)