Instructions to use ShuklaGroupIllinois/LassoESM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShuklaGroupIllinois/LassoESM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ShuklaGroupIllinois/LassoESM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ShuklaGroupIllinois/LassoESM") model = AutoModelForMaskedLM.from_pretrained("ShuklaGroupIllinois/LassoESM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ShuklaGroupIllinois/LassoESM: direct link, hf CLI and curl.
- Browser
- Download file 1.83 GB
-
https://huggingface.co/ShuklaGroupIllinois/LassoESM/resolve/main/optimizer.pt
- Command line
-
hf download hf://ShuklaGroupIllinois/LassoESM/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ShuklaGroupIllinois/LassoESM/resolve/main/optimizer.pt
1.83 GB
- Xet hash:
- 7005947433068908da1bf3ebdf3a2d40b7ac5c9abc4afbb7b1afc0502236d122
- Size of remote file:
- 1.83 GB
- SHA256:
- 1317aad92f6acf5b25b11b608503819a21471039670e48eee2a613e78f3d0f4c
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