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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMFold2-Experimental-Fast with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMFold2-Experimental-Fast", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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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://biohub.ai/papers/esm_protein.pdf) and [github](https://github.com/Biohub/esm) for details. Please use [ESMFold2](https://huggingface.co/biohub/
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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://biohub.ai/papers/esm_protein.pdf) 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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