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 rng_state.pth from lauraha/peptide_encoder: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/lauraha/peptide_encoder/resolve/main/rng_state.pth
- Command line
-
hf download hf://lauraha/peptide_encoder/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/lauraha/peptide_encoder/resolve/main/rng_state.pth
14.2 kB
- Xet hash:
- 1c7fad6a821c6c0fc3b3f483d4158378bb0337fd18ef4038537353eadbb40002
- Size of remote file:
- 14.2 kB
- SHA256:
- b260d7c78d903417ccd15c6d9a4c8b6fe4fcf9670ca87a95c940cb594e83a109
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