Instructions to use facebook/esm2_t12_35M_UR50D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/esm2_t12_35M_UR50D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/esm2_t12_35M_UR50D")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t12_35M_UR50D") model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t12_35M_UR50D", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/esm2_t12_35M_UR50D: direct link, hf CLI and curl.
- Browser
- Download file 136 MB
-
https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/esm2_t12_35M_UR50D/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/pytorch_model.bin
136 MB
- Xet hash:
- 1420e6c3fdfb8ef8a4c46be45e86a3f5d5f1f4aecfa404e019d079aa96c4673c
- Size of remote file:
- 136 MB
- SHA256:
- 4e62e632f8e625ec86d5f82925afdab71fdf9f1378666a953ad3ab22f5f51b3e
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