Instructions to use multimolecule/rinalmo-mega with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/rinalmo-mega with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/rinalmo-mega") model = AutoModel.from_pretrained("multimolecule/rinalmo-mega") inputs = tokenizer("UAGCUUAUCAGACUGAUGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/rinalmo-mega") output = predictor("UAGCUUAUCAG<mask>CUGAUGUUGA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c339d0aa0bd38a14039fdc900901dc5bb2305bdb7d7ccb6a163744d2e30bd900
- Size of remote file:
- 592 MB
- SHA256:
- a0e74d5cb944c9cfed51800e30f1af19239f9f92a036558ec132181c927c962f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.