Transformers
Safetensors
English
esmfold2
biology
esm
protein
protein-structure-prediction
structure-prediction
protein-design
3d-structure
confidence-estimation
molecular-dynamics
Instructions to use biohub/ESMFold2-Experimental-Fast-Cutoff2025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMFold2-Experimental-Fast-Cutoff2025 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("biohub/ESMFold2-Experimental-Fast-Cutoff2025") model = AutoModel.from_pretrained("biohub/ESMFold2-Experimental-Fast-Cutoff2025", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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license:
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license_link: https://github.com/Biohub/esm/blob/main/THIRD_PARTY_NOTICE.md
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language: en
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tags:
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The ESMFold2 Experimental Models are being released to support reproducibility of the findings in our paper, please refer to [the paper](https://
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license:
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- mit
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license_link: https://github.com/Biohub/esm/blob/main/THIRD_PARTY_NOTICE.md
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language: en
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tags:
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- transformers
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The ESMFold2 Experimental Models are being released to support reproducibility of the findings in our paper, please refer to [the paper](https://www.biorxiv.org/content/10.64898/2026.06.03.729735) and [github](https://github.com/Biohub/esm) for details. Please use [ESMFold2](https://huggingface.co/biohub/ESMFold2) model for research work. ESMFold2 predicts high-resolution, all-atom 3D protein structures from either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets.
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