Add library_name and project page link

README.md

@@ -1,15 +1,17 @@
 ---
 license: other
 license_name: youtu-vl
-extra_gated_eu_disallowed: true
 license_link: https://huggingface.co/tencent/Youtu-VL-4B-Instruct/blob/main/LICENSE.txt
 pipeline_tag: image-text-to-text
+extra_gated_eu_disallowed: true
+library_name: transformers
 ---
+
 <div align="center">
 
 # <img src="assets/youtu-vl-logo.png" alt="Youtu-VL Logo" height="100px">
 
-[📃 License](LICENSE.txt) • [💻 Code](https://github.com/TencentCloudADP/youtu-vl) • [📑 Technical Report](https://arxiv.org/abs/2601.19798) • [📊 Benchmarks](#benchmarks) • [🚀 Getting Started](#quickstart)
+[🏠 Project Page](https://youtu-tip.com/#llm) • [📃 License](LICENSE.txt) • [💻 Code](https://github.com/TencentCloudADP/youtu-vl) • [📑 Technical Report](https://arxiv.org/abs/2601.19798) • [📊 Benchmarks](#benchmarks) • [🚀 Getting Started](#quickstart)
 </div>
 
 ## 🎯 Introduction
@@ -23,7 +25,7 @@ pipeline_tag: image-text-to-text
 
   - **Promising Performance with High Efficiency**: Despite its compact 4B-parameter architecture, the model achieves competitive results across a wide range of general multimodal tasks, including general visual question answering (VQA), multimodal reasoning and mathematics, optical character recognition (OCR), multi-image and real-world understanding, hallucination evaluation, and GUI agent tasks.
 
-  <p align="center">
+  <p align="center\">
       <img src="assets/youtu-vl-overview.png" width="90%"/>
   <p>
 
@@ -40,7 +42,7 @@ pipeline_tag: image-text-to-text
 
   - **Vision-Centric Prediction with a Standard Architecture (no task-specific modules)**: Youtu-VL treats image and text tokens with equivalent autoregressive status, empowering it to perform vision-centric tasks for both dense vision prediction (e.g., segmentation, depth) and text-based prediction (e.g., grounding, detection) within a standard VLM architecture, eliminating the need for task-specific additions. This design yields a versitile general-purpose VLM, allowing a single model to flexibly accommodate a wide range of vision-centric and vsion-language requirements.
 
-  <p align="center">
+  <p align="center\">
       <img src="assets/architecture.png" width="90%"/>
   <p>
 
@@ -49,7 +51,7 @@ pipeline_tag: image-text-to-text
 
 ### Vision-Centric Tasks
 
-  <p align="center">
+  <p align="center\">
       <img src="assets/vision-centric-performance.png" width="90%"/>
   <p>
 
@@ -57,7 +59,7 @@ pipeline_tag: image-text-to-text
 ### General Multimodal Tasks
 
 
-  <p align="center">
+  <p align="center\">
       <img src="assets/general-multimodal-performance.png" width="90%"/>
   <p>
 
@@ -122,7 +124,8 @@ outputs = processor.batch_decode(
     generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
 )
 generated_text = outputs[0]
-print(f"Youtu-VL output:\n{generated_text}")
+print(f"Youtu-VL output:
+{generated_text}")
 ```
 
 ## 🎉 Citation