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license: mit |
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language: |
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- en |
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# Evolla |
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<a href="https://doi.org/10.1101/2025.01.05.630192"><img src="https://img.shields.io/badge/Paper-bioRxiv-green" style="max-width: 100%;"></a> |
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<a href="https://x.com/duguyuan/status/1876446845951492221"><img src="https://img.shields.io/badge/Post-X-black" style="max-width: 100%;"></a> |
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A frontier protein-language generative model designed to decode the molecular language of proteins. |
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## Comparative analysis |
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In our paper, we conducted a comparative analysis against two state-of-the-art general-purpose language models: *Deepseek-v3* and *gpt-4o-2024-11-20*. |
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| **Model** | **Mean GPT score (± 95% confidence interval)** | |
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| *Evolla* | 74.10 ± 0.81 | |
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| *Deepseek-v3* | 40.49 ± 0.56 | |
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| *gpt-4o-2024-11-20* | 37.07 ± 0.54 | |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/643bb8ba6eeb746f5ad0a2db/k3WaRvNm_FDcF1ITWMQE6. |
