Feature Extraction
Transformers
Safetensors
esmfold2
biology
protein-structure
multimodal-protein-model
custom_code
Instructions to use Synthyra/ESMFold2-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESMFold2-Fast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Synthyra/ESMFold2-Fast", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Synthyra/ESMFold2-Fast", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
README.md
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---
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library_name: transformers
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tags:
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- biology
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- protein-structure
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- esmfold2
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- multimodal-protein-model
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---
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# FastPLMs ESMFold2
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model = AutoModel.from_pretrained(
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"Synthyra/ESMFold2-Fast",
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trust_remote_code=True,
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dtype=torch.
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).eval()
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```
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Use `Synthyra/ESMFold2` for the full model and `Synthyra/ESMFold2-Fast` for the faster release variant.
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## Fold One Protein
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---
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library_name: transformers
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tags:
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- biology
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- protein-structure
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- esmfold2
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- multimodal-protein-model
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---
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# FastPLMs ESMFold2
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model = AutoModel.from_pretrained(
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"Synthyra/ESMFold2-Fast",
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trust_remote_code=True,
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dtype=torch.float32,
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).eval().cuda()
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```
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Use `Synthyra/ESMFold2` for the full model and `Synthyra/ESMFold2-Fast` for the faster release variant.
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The folding trunk runs in fp32; the 6B ESMC backbone is loaded in bf16 by default via `esmc_precision="bf16"`.
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## Fold One Protein
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