Instructions to use lauraha/peptide_encoder_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lauraha/peptide_encoder_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lauraha/peptide_encoder_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lauraha/peptide_encoder_2") model = AutoModelForMaskedLM.from_pretrained("lauraha/peptide_encoder_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lauraha/peptide_encoder_2: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/lauraha/peptide_encoder_2/resolve/main/training_args.bin
- Command line
-
hf download hf://lauraha/peptide_encoder_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lauraha/peptide_encoder_2/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- d95ec82cab76e9533bd3e3759898b9453091695a2239e0a6347db8009c63851e
- Size of remote file:
- 5.18 kB
- SHA256:
- ea41cb6bb5e80f684c6d857671176261650556f05c01e8d2c43346202381464d
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