Instructions to use mgh6/TCS_MLM_SaProt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/TCS_MLM_SaProt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/TCS_MLM_SaProt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/TCS_MLM_SaProt") model = AutoModelForMaskedLM.from_pretrained("mgh6/TCS_MLM_SaProt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from mgh6/TCS_MLM_SaProt: direct link, hf CLI and curl.
- Browser
- Download file 16.4 MB
-
https://huggingface.co/mgh6/TCS_MLM_SaProt/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://mgh6/TCS_MLM_SaProt/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/mgh6/TCS_MLM_SaProt/resolve/main/last-checkpoint/optimizer.pt
16.4 MB
- Xet hash:
- 1b9e10727790d74ad220f1e38b6405a79731cf05ba43b6a29c1bb52149fe9e2c
- Size of remote file:
- 16.4 MB
- SHA256:
- 71b67f31952b3ecf63d396c77002ec5b7f12d69c0d298d33e626c92e4236c805
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