Instructions to use facebook/esm-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/esm-1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/esm-1b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/esm-1b") model = AutoModelForMaskedLM.from_pretrained("facebook/esm-1b", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/esm-1b: direct link, hf CLI and curl.
- Browser
- Download file 2.61 GB
-
https://huggingface.co/facebook/esm-1b/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/esm-1b/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/esm-1b/resolve/main/pytorch_model.bin
2.61 GB
- Xet hash:
- f58cdb6328ab617ef2e70073ca8100ec8d6b94b5787c342370c597df1a0e00b3
- Size of remote file:
- 2.61 GB
- SHA256:
- d6f6ebe6b10b362ae7405a2bea082b90091c8eeba456c4c3ac64e952041ea9e8
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