Instructions to use Synthyra/ESMFold2-Experimental-Fast-Cutoff2025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESMFold2-Experimental-Fast-Cutoff2025 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Synthyra/ESMFold2-Experimental-Fast-Cutoff2025", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Synthyra/ESMFold2-Experimental-Fast-Cutoff2025", trust_remote_code=True) model = AutoModel.from_pretrained("Synthyra/ESMFold2-Experimental-Fast-Cutoff2025", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 336 Bytes
1217504 203b56b 1217504 | 1 2 3 4 5 6 7 8 9 10 11 12 | """Lazy model-family namespace for FastPLMs.
Model classes are resolved through Transformers AutoClasses and the typed
registry. Importing this package therefore does not load checkpoints, create
tokenizers, compile kernels, or initialize an accelerator runtime.
"""
from __future__ import annotations
__all__: tuple[str, ...] = ()
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