Improve model card metadata and content

README.md

@@ -1,7 +1,8 @@
 ---
-license: apache-2.0
 language:
 - en
+license: apache-2.0
+pipeline_tag: other
 tags:
 - physics
 - PDEs
@@ -9,10 +10,10 @@ tags:
 - FoundationModels
 - AI4Sci
 ---
+
 <p align="center">
 <a href="https://github.com/tum-pbs/Tadpole">
-  <img src="assets/images/tadpole_colorful.png" width="100"/>
-</a>
+  <img src="https://huggingface.co/thuerey-group/Tadpole/resolve/main/assets/images/tadpole_colorful.png" width="100"/>
 </a>
 </p>
 <h4 align="center">Autoencoders as Foundation Models for 3D PDEs with Online Learning</h4>
@@ -20,7 +21,6 @@ tags:
 <a href="https://arxiv.org/abs/2605.15284">
   <img src="https://img.shields.io/badge/arXiv-2605.15284-b31b1b?logo=arxiv" alt="Read on arXiv"/>
 </a>
-</a>
 <a href="https://github.com/tum-pbs/Tadpole">
   <img src="https://img.shields.io/badge/Github-Tadpole-181717?logo=github" alt="Github project"/>
 </a>
@@ -28,17 +28,15 @@ tags:
 
 ## About
 
-This repository contains pre-trained weights of **Tadpole**, a foundation model for three-dimensional partial differential equations (PDEs). 
+This repository contains pre-trained weights of **Tadpole**, a foundation model for three-dimensional partial differential equations (PDEs) introduced in the paper [Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning](https://huggingface.co/papers/2605.15284). 
 
 Tadpole distinguishes itself from existing PDE foundation models in three key aspects: 
 
-(1) Tadpole is pre-trained as an autoencoder to learn the inherent representation of PDE solutions, which is more generalizable than the traditional paradigm of training PDE foundation models directly on the dynamics evolution of PDE solutions.
+(1) **Autoencoder Pre-training**: Tadpole is pre-trained to learn the inherent representation of PDE solutions, which is more generalizable than the traditional paradigm of training models directly on temporal dynamics.
 
-(2) Tadpole is pretrained with online learning. which utilize a GPU-based solver to generate diverse data distribution without IO or storage bottlenecks induced by 3D PDE data.
+(2) **Online Learning**: It utilizes a GPU-based solver to generate diverse data on-the-fly, avoiding storage and I/O bottlenecks associated with massive 3D PDE datasets.
 
-(3) Tadpole is able to process multiple downstream tasks, including autoencoding, dynamics prediction, and generative modeling.
-
-For more details, please refer to our [research paper](https://arxiv.org/abs/2605.15284):
+(3) **Multi-functionality**: Tadpole can be applied to multiple downstream tasks, including autoencoding, dynamics prediction, and generative modeling.
 
 ## Installation and Loading Pre-trained Weights
 
@@ -48,18 +46,33 @@ Tadpole can be installed via pip:
 pip install git+https://github.com/tum-pbs/Tadpole
 ```
 
-The pre-trained weights are named as `tadpole_{SIZE}_{TYPE}.safetensors`, where `{SIZE}` can be `S`, `B`, or `L` indicating the model size, and `{TYPE}` can be `encoder` or `decoder` indicating the model type. Weights can be loaded through `weight_{TYPE}` arguments in Tadpole model classes:
+The pre-trained weights are named as `tadpole_{SIZE}_{TYPE}.safetensors`, where `{SIZE}` can be `S`, `B`, or `L` indicating the model size, and `{TYPE}` can be `encoder` or `decoder`. Weights can be loaded through `weight_{TYPE}` arguments in Tadpole model classes:
 
 ```python
 from huggingface_hub import hf_hub_download
-ae=TadpoleAutoencoder(
+
+# Example: loading the B-size model
+ae = TadpoleAutoencoder(
     size="B",
-    weight_encoder=hf_hub_download(repo_id="thuerey-group/Tadpole",filename="tadpole_b_encoder.safetensors"),
+    weight_encoder=hf_hub_download(repo_id="thuerey-group/Tadpole", filename="tadpole_b_encoder.safetensors"),
     # or you can also download the weights from Hugging Face and load it locally
-    weight_decoder=hf_hub_download(repo_id="thuerey-group/Tadpole",filename="tadpole_b_decoder.safetensors"),
+    weight_decoder=hf_hub_download(repo_id="thuerey-group/Tadpole", filename="tadpole_b_decoder.safetensors"),
     )
 ```
 
-Please refer to our [github repository](https://github.com/tum-pbs/Tadpole) for more details and tutorials.
+Please refer to the [GitHub repository](https://github.com/tum-pbs/Tadpole) for more details and tutorials.
+
+Note: Currently, pre-trained weights for the B-size model are provided; S- and L-size models will be released in the future.
+
+## Citation
 
-Note: Currently, we only provide pre-trained weights for the B-size model; S- and L-size models will be released in the near future. Please stay tuned😊!
+```latex
+@article{Liu2026Tadpole,
+  author = {Qiang Liu, Felix Koehler, Benjamin Holzschuh, and Nils Thuerey},
+  title = {Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning},
+  eprint={2605.15284},
+  archivePrefix={arXiv},
+  primaryClass={cs.LG},
+  url={https://arxiv.org/abs/2605.15284} 
+}
+```