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-base600M-step500k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMFold2-Experimental-Fast-base600M-step500k with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("biohub/ESMFold2-Experimental-Fast-base600M-step500k") model = AutoModel.from_pretrained("biohub/ESMFold2-Experimental-Fast-base600M-step500k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from biohub/ESMFold2-Experimental-Fast-base600M-step500k: direct link, hf CLI and curl.
- Browser
- Download file 810 Bytes
-
https://huggingface.co/biohub/ESMFold2-Experimental-Fast-base600M-step500k/resolve/main/README.md
- Command line
-
hf download hf://biohub/ESMFold2-Experimental-Fast-base600M-step500k/README.md
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curl -L -o README.md https://huggingface.co/biohub/ESMFold2-Experimental-Fast-base600M-step500k/resolve/main/README.md
810 Bytes
metadata
license:
- mit
license_link: https://github.com/Biohub/esm/blob/main/THIRD_PARTY_NOTICE.md
language: en
tags:
- biology
- esm
- protein
- protein-structure-prediction
- structure-prediction
- protein-design
- 3d-structure
- confidence-estimation
- molecular-dynamics
- transformers
The ESMFold2 Experimental Models are being released to support reproducibility of the findings in our paper, please refer to the paper and github for details. Please use 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.