Add project page link to model card

This PR enhances the model card by adding a link to the project page (`https://internvl.github.io/blog/2025-09-29-SDLM/`) in the introductory section. This improves discoverability for users seeking additional context and information about the project.

The existing metadata and content (including the paper link to arXiv and the sample usage) remain unchanged as they are already accurate and meet the documentation guidelines.

README.md

@@ -1,18 +1,6 @@
 ---
-license: apache-2.0
-license_name: qwen
-license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
-pipeline_tag: text-generation
-library_name: transformers
 base_model:
 - Qwen/Qwen2.5-3B
-base_model_relation: finetune
-language:
-- en
-tags:
-- sdlm
-- diffusion language model
-- custom_code
 datasets:
 - dyyyyyyyy/ScaleQuest-Math
 - OpenCoder-LLM/opc-sft-stage2
@@ -20,15 +8,27 @@ datasets:
 - HuggingFaceTB/smoltalk2
 - LipengCS/Table-GPT
 - allenai/SciRIFF
+language:
+- en
+library_name: transformers
+license: apache-2.0
+license_name: qwen
+license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
+pipeline_tag: text-generation
+tags:
+- sdlm
+- diffusion language model
+- custom_code
+base_model_relation: finetune
 ---
 
 # SDLM-3B-D8
 
-[\[📂 GitHub\]](https://github.com/OpenGVLab/SDLM)  [\[📜 Tech Report\]](https://arxiv.org/abs/2509.24007)  [\[🤗 HuggingFace\]](https://huggingface.co/collections/OpenGVLab/sdlm-68ac82709d7c343ad36aa552)  
+[\[📂 GitHub\]](https://github.com/OpenGVLab/SDLM)  [\[📜 Tech Report\]](https://arxiv.org/abs/2509.24007)  [\\[🚀 Project Page\\]](https://internvl.github.io/blog/2025-09-29-SDLM/)  [\[🤗 HuggingFace\]](https://huggingface.co/collections/OpenGVLab/sdlm-68ac82709d7c343ad36aa552)  
 
 ## Introduction
 
-We propose a <b>S</b>equential <b>D</b>iffusion <b>L</b>anguage <b>M</b>odel (<b>SDLM</b>), to cheaply stimulate the parallel prediction capabilities of diffusion models. Specifically, SDLM reduces distribution shift by limiting the prediction range to a fixed block length and enforces decoding order through the longest prefix decoding method, thereby significantly improving prediction efficiency while ensuring generation quality. Our method can be viewed as a further generalization of the autoregressive (AR) paradigm. Therefore, it is possible to use pre-trained AR weights and quickly migrate to the diffusion framework with only minimal instruction fine-tuning.
+We propose a **S**equential **D**iffusion **L**anguage **M**odel (**SDLM**), to cheaply stimulate the parallel prediction capabilities of diffusion models. Specifically, SDLM reduces distribution shift by limiting the prediction range to a fixed block length and enforces decoding order through the longest prefix decoding method, thereby significantly improving prediction efficiency while ensuring generation quality. Our method can be viewed as a further generalization of the autoregressive (AR) paradigm. Therefore, it is possible to use pre-trained AR weights and quickly migrate to the diffusion framework with only minimal instruction fine-tuning.
 
 ![image/png](https://huggingface.co/OpenGVLab/SDLM-3B-D8/resolve/main/assets/three_framework.png)
 
@@ -151,4 +151,4 @@ If you find this project useful in your research, please consider citing:
   journal={arXiv preprint arXiv:2509.24007},
   year={2025}
 }
-```
+```