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 optimizer.pt from lauraha/peptide_encoder: direct link, hf CLI and curl.
- Browser
- Download file 1.19 GB
-
https://huggingface.co/lauraha/peptide_encoder/resolve/main/optimizer.pt
- Command line
-
hf download hf://lauraha/peptide_encoder/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/lauraha/peptide_encoder/resolve/main/optimizer.pt
1.19 GB
- Xet hash:
- 741c17b667593b09e2efe5982e3cd63d8af63a3f83c44bf6554a85dce8830e9c
- Size of remote file:
- 1.19 GB
- SHA256:
- 8284446979e39209fd3ae1b1456572a29a226c37b368fa8c05b449c7dffcd2fb
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