Instructions to use multimolecule/progen2-bfd90 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/progen2-bfd90 with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/progen2-bfd90") model = AutoModel.from_pretrained("multimolecule/progen2-bfd90") inputs = tokenizer("MANLGCWMLVLFVATWSDLGLCKKRPKPGGWNTGGSRYPGQGSPGGNRYPPQGGGGWGQPHGGGWGQPHGGGWGQPHGGGWGQPHGGGWGQGGGTHSQWNKPSKPKTNMKHMAGAAAAGAVVGGLGGYMLGSAMSRPIIHFGSDYEDRYYRENMHRYPNQVYYRPMDEYSNQNNFVHDCVNITIKQHTVTTTTKGENFTETDVKMMERVVEQMCITQYERESQAYYQRGSSMV", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b91fe1d05cc2859ff3ef4a2aef771cf1dba599660996b0e12e600ae45605a25
- Size of remote file:
- 5.03 GB
- SHA256:
- 57044c9923a14d4667273c57f3c3c1cc24f94bfb530187d2bcecd129dfc61ad6
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