Feature Extraction
PyTorch
timm
English
biosignals
ecg
emg
eeg
embedding
mixture-of-experts
lightweight
Instructions to use stefanosgikas/TinyBioMoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use stefanosgikas/TinyBioMoE with timm:
import timm model = timm.create_model("hf-hub:stefanosgikas/TinyBioMoE", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download docs/encoders.png from stefanosgikas/TinyBioMoE: direct link, hf CLI and curl.
- Browser
- Download file 221 kB
-
https://huggingface.co/stefanosgikas/TinyBioMoE/resolve/main/docs/encoders.png
- Command line
-
hf download hf://stefanosgikas/TinyBioMoE/docs/encoders.png
-
curl -L -o encoders.png https://huggingface.co/stefanosgikas/TinyBioMoE/resolve/main/docs/encoders.png
221 kB

- Xet hash:
- 4da21c0d573109893919eafcb0e7a38c8ec54dd177e2d7612856a170296b7942
- Size of remote file:
- 221 kB
- SHA256:
- e976c31898896932aafa9ff58779df58cd7350732c4dd7c4358a4449ef43f013
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.