Instructions to use facebook/esm2_t30_150M_UR50D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/esm2_t30_150M_UR50D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/esm2_t30_150M_UR50D")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t30_150M_UR50D") model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t30_150M_UR50D") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cde115348b762c17cd9f8882409a6bfa7300030fb9d209116abd955f21791f79
- Size of remote file:
- 595 MB
- SHA256:
- 6f43a2952a6e9bccd00c95d86c75ed38062a8b13ad7089b6739cc088c3421f56
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