Update pipeline tag to visual-document-retrieval

This PR updates the `pipeline_tag` metadata to `visual-document-retrieval` which more accurately reflects the model's functionality. The current `image-text-to-text` tag is misleading.

README.md

@@ -1,22 +1,23 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen2-VL-7B
 language:
 - en
-pipeline_tag: image-text-to-text
+library_name: transformers
+license: apache-2.0
+pipeline_tag: visual-document-retrieval
 tags:
 - multimodal
-library_name: transformers
-base_model:
-- Qwen/Qwen2-VL-7B
 ---
 
+```markdown
 # UGround-V1-7B （Qwen2-VL-Based)
 
 UGround is a strong GUI visual grounding model trained with a simple recipe. Check our homepage and paper for more details. This work is a collaboration between [OSUNLP](https://x.com/osunlp) and [Orby AI](https://www.orby.ai/).
 ![radar](https://osu-nlp-group.github.io/UGround/static/images/radar.png)
 - **Homepage:** https://osu-nlp-group.github.io/UGround/
 - **Repository:** https://github.com/OSU-NLP-Group/UGround
-- **Paper (ICLR'25 Oral):** https://arxiv.org/abs/2410.05243
+- **Paper (ICLR'25 Oral):** https://arxiv.org/abs/2504.04716
 - **Demo:** https://huggingface.co/spaces/orby-osu/UGround
 - **Point of Contact:** [Boyu Gou](mailto:[email protected])
 
@@ -228,7 +229,7 @@ We're excited to unveil **Qwen2-VL**, the latest iteration of our Qwen-VL model,
 * **Naive Dynamic Resolution**: Unlike before, Qwen2-VL can handle arbitrary image resolutions, mapping them into a dynamic number of visual tokens, offering a more human-like visual processing experience.
 
 <p align="center">
-    <img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/qwen2_vl.jpg" width="80%"/>
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/qwen2_vl.jpg" width="80%"/>
 <p>
 
 * **Multimodal Rotary Position Embedding (M-ROPE)**: Decomposes positional embedding into parts to capture 1D textual, 2D visual, and 3D video positional information, enhancing its multimodal processing capabilities.
@@ -393,7 +394,12 @@ conversation = [
 
 # Preprocess the inputs
 text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
-# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>\n<|im_start|>assistant\n'
+# Excepted output: '<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>
+<|im_start|>assistant
+'
 
 inputs = processor(
     text=[text_prompt], images=[image], padding=True, return_tensors="pt"
@@ -403,7 +409,7 @@ inputs = inputs.to("cuda")
 # Inference: Generation of the output
 output_ids = model.generate(**inputs, max_new_tokens=128)
 generated_ids = [
-    output_ids[len(input_ids) :]
+    out_ids[len(input_ids) :]
     for input_ids, output_ids in zip(inputs.input_ids, output_ids)
 ]
 output_text = processor.batch_decode(
@@ -423,7 +429,7 @@ messages = [
         "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."},
+            {"type": "text", "text": "What are the common elements in these pictures?"},
         ],
     }
 ]
@@ -512,180 +518,4 @@ 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>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, messages1]
-
-# 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>
-
-### 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-VL-7B-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."},
-        ],
-    }
-]
-```
-
-## Limitations
-
-While Qwen2-VL are applicable to a wide range of visual tasks, it is equally important to understand its limitations. Here are some known restrictions:
-
-1. Lack of Audio Support: The current model does **not comprehend audio information** within videos.
-2. Data timeliness: Our image dataset is **updated until June 2023**, and information subsequent to this date may not be covered.
-3. Constraints in Individuals and Intellectual Property (IP): The model's capacity to recognize specific individuals or IPs is limited, potentially failing to comprehensively cover all well-known personalities or brands.
-4. Limited Capacity for Complex Instruction: When faced with intricate multi-step instructions, the model's understanding and execution capabilities require enhancement.
-5. Insufficient Counting Accuracy: Particularly in complex scenes, the accuracy of object counting is not high, necessitating further improvements.
-6. Weak Spatial Reasoning Skills: Especially in 3D spaces, the model's inference of object positional relationships is inadequate, making it difficult to precisely judge the relative positions of objects.
-
-These limitations serve as ongoing directions for model optimization and improvement, and we are committed to continually enhancing the model's performance and scope of application.
-
-
-## Citation
-
-If you find our work helpful, feel free to give us a cite.
-
-```
-@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}
-}
-```
+output_text = processor.