Instructions to use yikx/classwork with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yikx/classwork with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="yikx/classwork", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yikx/classwork", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab14ab902b97924df5512dee4f41561521655b2af64acbb274b5bee03baf513e
- Size of remote file:
- 117 MB
- SHA256:
- 236ed554cb33aace4e227befa3a8713862a82fc6d4a9907ab90b490498e7d935
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.