update readme

.github/README.md

@@ -3,31 +3,33 @@
     <a href="https://zenodo.org/doi/10.5281/zenodo.13704399"><img src="https://zenodo.org/badge/776930320.svg" alt="DOI"></a>
 </div>
 
-
 > [!CAUTION]
 > MLIP Arena is currently in pre-alpha. The results are not stable. Please intepret them with care. 
 
 > [!NOTE]
-> If you're interested in joining the effort, please reach out to Yuan at [[email protected]](mailto:[email protected]). 
+> If you're interested in joining the effort, please reach out to Yuan at [[email protected]](mailto:[email protected]). See [project page](https://github.com/orgs/atomind-ai/projects/1) for some outstanding tasks. 
 
 MLIP Arena is an open-source platform for benchmarking machine learning interatomic potentials (MLIPs). The platform provides a unified interface for users to evaluate the performance of their models on a variety of tasks, including single-point density functional theory calculations and molecular dynamics simulations. The platform is designed to be extensible, allowing users to contribute new models, benchmarks, and training data to the platform.
 
 ## Contribute
 
-MLIP Arena is now in pre-alpha. If you're interested in joining the effort, please reach out to Yuan at [[email protected]](mailto:[email protected]). 
+MLIP Arena is now in pre-alpha. If you're interested in joining the effort, please reach out to Yuan at [[email protected]](mailto:[email protected]). See [project page](https://github.com/orgs/atomind-ai/projects/1) for some outstanding tasks. 
 
 ### Add new MLIP models
 
-If you have pretrained MLIP models that you would like to contribute to the MLIP Arena and show benchmark in real-time, please follow these steps:
+If you have pretrained MLIP models that you would like to contribute to the MLIP Arena and show benchmark in real-time, there are two ways:
+
+#### Hugging Face Model
 
-1. Create a new [Hugging Face Model](https://huggingface.co/new) repository and upload the model file.
-2. Follow the template to code the I/O interface for your model, and upload the script along with metadata to the MLIP Arena [here]().
+0. Inherit Hugging Face [ModelHubMixin](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins) class to your awesome model class definition. We recommend [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin).
+1. Create a new [Hugging Face Model](https://huggingface.co/new) repository and upload the model file using [push_to_hub function](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins#huggingface_hub.ModelHubMixin.push_to_hub).
+2. Follow the template to code the I/O interface for your model, and upload the script along with metadata to the MLIP Arena [here](../mlip_arena/models/README.md).
 3. CPU benchmarking will be performed automatically. Due to the limited amount GPU compute, if you would like to be considered for GPU benchmarking, please create a pull request to demonstrate the offline performance of your model (published paper or preprint). We will review and select the models to be benchmarked on GPU.
 
 ### Add new benchmark tasks
 
 1. Create a new [Hugging Face Dataset](https://huggingface.co/new-dataset) repository and upload the reference data (e.g. DFT, AIMD, experimental measurements such as RDF).
-2. Follow the task template to implement the task class and upload the script along with metadata to the MLIP Arena [here]().
+2. Follow the task template to implement the task class and upload the script along with metadata to the MLIP Arena [here](../mlip_arena/tasks/README.md).
 3. Code a benchmark script to evaluate the performance of your model on the task. The script should be able to load the model and the dataset, and output the evaluation metrics.
 
 #### Molecular dynamics calculations
@@ -44,6 +46,4 @@ If you have pretrained MLIP models that you would like to contribute to the MLIP
 
 ### Add new training datasets
 
-[Hugging Face Auto-Train](https://huggingface.co/docs/hub/webhooks-guide-auto-retrain)
-
-
+[Hugging Face Auto-Train](https://huggingface.co/docs/hub/webhooks-guide-auto-retrain)

README.md

@@ -6,51 +6,4 @@ sdk_version: 1.36.0 # The latest supported version
 app_file: serve/app.py
 ---
 
-<div align="center">
-    <h1>MLIP Arena</h1>
-    <a href="https://zenodo.org/doi/10.5281/zenodo.13704399"><img src="https://zenodo.org/badge/776930320.svg" alt="DOI"></a>
-</div>
-
-> [!CAUTION]
-> MLIP Arena is currently in pre-alpha. The results are not stable. Please intepret them with care. 
-
-> [!NOTE]
-> If you're interested in joining the effort, please reach out to Yuan at [[email protected]](mailto:[email protected]). 
-
-MLIP Arena is an open-source platform for benchmarking machine learning interatomic potentials (MLIPs). The platform provides a unified interface for users to evaluate the performance of their models on a variety of tasks, including single-point density functional theory calculations and molecular dynamics simulations. The platform is designed to be extensible, allowing users to contribute new models, benchmarks, and training data to the platform.
-
-## Contribute
-
-MLIP Arena is now in pre-alpha. If you're interested in joining the effort, please reach out to Yuan at [[email protected]](mailto:[email protected]). 
-
-### Add new MLIP models
-
-If you have pretrained MLIP models that you would like to contribute to the MLIP Arena and show benchmark in real-time, please follow these steps:
-
-1. Create a new [Hugging Face Model](https://huggingface.co/new) repository and upload the model file.
-2. Follow the template to code the I/O interface for your model, and upload the script along with metadata to the MLIP Arena [here]().
-3. CPU benchmarking will be performed automatically. Due to the limited amount GPU compute, if you would like to be considered for GPU benchmarking, please create a pull request to demonstrate the offline performance of your model (published paper or preprint). We will review and select the models to be benchmarked on GPU.
-
-### Add new benchmark tasks
-
-1. Create a new [Hugging Face Dataset](https://huggingface.co/new-dataset) repository and upload the reference data (e.g. DFT, AIMD, experimental measurements such as RDF).
-2. Follow the task template to implement the task class and upload the script along with metadata to the MLIP Arena [here]().
-3. Code a benchmark script to evaluate the performance of your model on the task. The script should be able to load the model and the dataset, and output the evaluation metrics.
-
-#### Molecular dynamics calculations
-
-- [ ] [MD17](http://www.sgdml.org/#datasets)
-- [ ] [MD22](http://www.sgdml.org/#datasets)
-
-
-#### Single-point density functional theory calculations
-
-- [ ] MPTrj
-- [ ] QM9
-- [ ] [Alexandria](https://alexandria.icams.rub.de/)
-
-### Add new training datasets
-
-[Hugging Face Auto-Train](https://huggingface.co/docs/hub/webhooks-guide-auto-retrain)
-