Instructions to use lauraha/peptide_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lauraha/peptide_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lauraha/peptide_encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lauraha/peptide_encoder") model = AutoModelForMaskedLM.from_pretrained("lauraha/peptide_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scheduler.pt from lauraha/peptide_encoder: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/lauraha/peptide_encoder/resolve/main/scheduler.pt
- Command line
-
hf download hf://lauraha/peptide_encoder/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/lauraha/peptide_encoder/resolve/main/scheduler.pt
1.06 kB
- Xet hash:
- 753cc76519a645a967952cc5d150842f3d345e461e040bc66e131583b2f997a4
- Size of remote file:
- 1.06 kB
- SHA256:
- 6923632fef38472c44be2851ece58a12345893d12b58cd2a4290bcbf14e4e3e7
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