Spaces:
Running
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Running
on
Zero
Update README and app.py to enhance project description and technical details for BASIS-China iGEM 2025 deployment of Tranception
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| title: Transeption IGEM BASISCHINA 2025 | |
| emoji: 🧬 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 5.34.2 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| suggested_hardware: zero-a10g | |
| models: | |
| - PascalNotin/Tranception_Small | |
| - PascalNotin/Tranception_Medium | |
| - PascalNotin/Tranception_Large | |
| # Tranception Protein Fitness Prediction - BASIS-China iGEM 2025 | |
| Welcome to BASIS-China iGEM Team's deployment of Tranception on Hugging Face Spaces! | |
| ## About This Project | |
| This is an implementation of the Tranception model for protein fitness prediction, deployed by the BASIS-China iGEM Team 2025. Our goal is to make advanced protein engineering tools accessible to the synthetic biology community. | |
| ### Features | |
| - **In silico directed evolution**: Iteratively improve protein fitness through single amino acid substitutions | |
| - **Comprehensive fitness analysis**: Generate heatmaps showing fitness scores for all possible mutations | |
| - **Zero GPU support**: Leverages Hugging Face's dynamic GPU allocation for efficient inference | |
| - **Multiple model sizes**: Choose between Small, Medium, and Large models based on your needs | |
| ### Technical Implementation | |
| This deployment utilizes Hugging Face's Zero GPU infrastructure, which: | |
| - Dynamically allocates H200 GPU resources when available | |
| - Seamlessly falls back to CPU processing when GPUs are unavailable | |
| - Ensures efficient resource management for all users | |
| ## About BASIS-China iGEM Team | |
| We are a high school synthetic biology team participating in the International Genetically Engineered Machine (iGEM) competition. Our 2025 project focuses on protein engineering and computational biology applications. | |
| ## Credits | |
| This implementation is based on: | |
| **Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval** | |
| by Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks, and Yarin Gal. | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |