Add project page link to model card (#1)

- Add project page link to model card (305dff10dddfd9beae65a6b8a1b6c40c5e9eb558)


Co-authored-by: Niels Rogge <[email protected]>

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}
 }
-```
+```