Instructions to use multimolecule/rinalmo-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/rinalmo-micro with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/rinalmo-micro") model = AutoModel.from_pretrained("multimolecule/rinalmo-micro") 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-micro") output = predictor("UAGCUUAUCAG<mask>CUGAUGUUGA") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from multimolecule/rinalmo-micro: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/multimolecule/rinalmo-micro/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://multimolecule/rinalmo-micro/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/multimolecule/rinalmo-micro/resolve/main/pytorch_model.bin
134 MB
- Xet hash:
- 84658e58f77e2ef3b9c0013bd17e167589cc7f2387288cdd8b5506ff34047049
- Size of remote file:
- 134 MB
- SHA256:
- 2d5688ae79317b991ec7215437136b705b1e56f4b26b5db6537e082973c76831
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