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.gitattributes

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LICENSE

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+Modified MIT License
+
+Copyright (c) 2026 Gewuzhiyan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the “Software”), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+Our only modification part is that, if the Model (or any derivative works
+thereof) is used for any of your commercial products or services that have
+more than 1 thousand monthly active users, or more than 10 thousand US dollars
+(or equivalent in other currencies) in monthly revenue, you shall prominently
+display "Suiren" on the user interface of such product or service.

README.md

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----
-license: mit
----
+---
+language: en
+tags:
+- chemistry
+- molecular-foundation-model
+- quantum-chemistry
+- equivariant-neural-networks
+license: mit
+---
+
+<div align="center" style="line-height:1">
+  <img src="./logo.svg" alt="main_flowchart" width="40%" />
+  <a href="https://github.com/Gewu-Intelligence/Suiren-Foundation-Model" target="_blank"><img alt="github" src="https://img.shields.io/badge/Github-Gewu-blue?logo=github"/></a>
+  <a href="https://github.com/Gewu-Intelligence/Huntianling"><img alt="Homepage" src="https://img.shields.io/badge/🤖Skills-Huntianling-blue"/></a>
+  <a href="https://drive.google.com/file/d/1vUMYzhmhCeNU18WE5D_xV4gQWxfU7kI7/view?usp=sharing"><img alt="slides" src="https://img.shields.io/badge/Slides-Suiren-white?logo=slideshare"/></a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/ajy112/Suiren-Base/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Modified_MIT-f5de53?&color=f5de53"/></a>
+</div>
+
+
+
+# Suiren-Base (1.8B)
+
+Suiren-Base is a large-scale molecular domain foundation model developed by **Gewu-Intelligence (SAIS Physics Lab)**. With 1.8 billion parameters, it is designed to consolidate quantum chemical knowledge into a unified framework through massive self-supervised pre-training, enabling direct prediction of various quantum properties for small molecules.
+
+## Key Features
+
+- **Core Foundation Model**: Serves as the backbone of the Suiren series, encoding broad chemical knowledge for predicting energy, forces, and generating high-quality atomic-level embeddings.
+- **Advanced Architecture**: Utilizes a hybrid architecture combining EquiformerV2 with the Equivariant Spherical Transformer (EST). It captures deep inter-atomic interactions through high-order equivariant representations while significantly improving inference speed compared to traditional point-cloud networks.
+- **Self-Supervised Pre-training**: Employs Equivariant Masked Position Prediction (EMPP), a task that forces the model to understand atomic interactions by reconstructing deleted atomic nodes and positions.
+- **Large-scale Dataset**: Trained on the Full Qo2mol dataset (not been fully open-sourced), which contains about 100 million high-precision DFT calculation points, covering an extensive chemical space and various molecular sizes.
+
+Suiren-Base model is the foundation model in Suiren family.
+
+<div align="center">
+<img src="./suiren-family.jpg" alt="main_flowchart" width="100%" />
+</div>
+
+## Usage
+
+You can load the model using the provided API in the [GitHub repository](https://github.com/Gewu-Intelligence/Suiren-Foundation-Model).
+
+```
+import torch
+from suiren_models import ModelLoader
+
+# Initialize loader
+loader = ModelLoader(config_path='config_name.yml')
+# example: loader = ModelLoader(config_path='suiren-base.yml')
+
+# Load model architecture
+model = loader.load_model()
+
+# Load pre-trained weights
+loader.load_weights(model, 'path/to/checkpoint')
+
+# Load normalizer from config
+loader.load_normalizer()
+
+# Load normalizer from checkpoint (optional)
+#  loader.load_normalizer('path/to/normalizer')
+
+# Move model to device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+model.eval()
+```