A frontier protein-language generative model designed to decode the molecular language of proteins.

Quickly try our online server (Evolla-10B) here.

We have 2 PhD positions for international students at Westlake University, China! see here.

• 2025/01/06 We released our paper Decoding the Molecular Language of Proteins with Evolla.
• 2024/12/06 We uploaded the Evolla-10B model to huggingface hub.

conda create -n Evolla python=3.10
conda activate Evolla

bash environment.sh

We provide the pre-trained Evolla-10B model in huggingface hub. You can download the model by running the following command:

cd ckpt/huggingface

git lfs install

git clone https://huggingface.co/westlake-repl/Evolla-10B

git clone https://huggingface.co/westlake-repl/SaProt_650M_AF2

git clone https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct

We provide a sample input file examples/inputs.tsv for you to test the Evolla model. The input file should be a tab-separated file, where each line represents (protein_id, aa_sequence, foldseek_sequence, question_in_json_string).

Note: protein_id is the identifier of the line, aa_sequence is the amino acid sequence of the protein, foldseek_sequence is the sequence of the protein in FoldSeek format. question_in_json_string is the question which is dumped by json.dumps function.

The following provides script to run inference based on tsv file.

You should replace the /your/path/to/Evolla to your own path to Evolla directory.

cd /your/path/to/Evolla
python scripts/inference.py --config_path config/Evolla_10B.yaml --input_path examples/inputs.tsv

If you find this repository useful, please cite our paper:

@article{zhou2025decoding,
  title={Decoding the Molecular Language of Proteins with Evolla},
  author={Zhou, Xibin and Han, Chenchen and Zhang, Yingqi and Su, Jin and Zhuang, Kai and Jiang, Shiyu and Yuan, Zichen and Zheng, Wei and Dai, Fengyuan and Zhou, Yuyang and others},
  journal={bioRxiv},
  pages={2025--01},
  year={2025},
  publisher={Cold Spring Harbor Laboratory}
}

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