Instructions to use multimolecule/progen2-xlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/progen2-xlarge with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/progen2-xlarge") model = AutoModel.from_pretrained("multimolecule/progen2-xlarge") inputs = tokenizer("MANLGCWMLVLFVATWSDLGLCKKRPKPGGWNTGGSRYPGQGSPGGNRYPPQGGGGWGQPHGGGWGQPHGGGWGQPHGGGWGQPHGGGWGQGGGTHSQWNKPSKPKTNMKHMAGAAAAGAVVGGLGGYMLGSAMSRPIIHFGSDYEDRYYRENMHRYPNQVYYRPMDEYSNQNNFVHDCVNITIKQHTVTTTTKGENFTETDVKMMERVVEQMCITQYERESQAYYQRGSSMV", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa3e0af6f7098ed572fd273dad7adf3d54630ea77f9ee3847e4015f35431b9d8
- Size of remote file:
- 12.9 GB
- SHA256:
- 966dffefb7f33eb86c382a4873d9bffad5c97d3a85593794d64f099bb44167ef
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