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 training_args.bin from lauraha/peptide_encoder: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/lauraha/peptide_encoder/resolve/main/training_args.bin
- Command line
-
hf download hf://lauraha/peptide_encoder/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lauraha/peptide_encoder/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- cba2271030a924536f5e0fab1dffa888e6fa3e8d2bf4c018799526bbdf8c0a28
- Size of remote file:
- 5.11 kB
- SHA256:
- fc19761260cea9773d4cd42ffe0bd9c26f789398a1f24834476305c834edf36d
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