Instructions to use nvidia/esm2_t48_15B_UR50D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/esm2_t48_15B_UR50D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nvidia/esm2_t48_15B_UR50D", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("nvidia/esm2_t48_15B_UR50D", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5bd74e1f5a69d11f63cb50197b247be38333c8d2014a205f753ec46200dbd0e
- Size of remote file:
- 4.72 GB
- SHA256:
- 42ff07e64114ca97a43a2bd11750983cc03954d512edd6c0f63f8dcfd0cbb2b7
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