Instructions to use Zipeng365/WISP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Zipeng365/WISP with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Zipeng365/WISP", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Download environments/data.lock.txt from Zipeng365/WISP: direct link, hf CLI and curl.
- Browser
- Download file 165 Bytes
-
https://huggingface.co/Zipeng365/WISP/resolve/main/environments/data.lock.txt
- Command line
-
hf download hf://Zipeng365/WISP/environments/data.lock.txt
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curl -L -o data.lock.txt https://huggingface.co/Zipeng365/WISP/resolve/main/environments/data.lock.txt
165 Bytes
| # Optional benchmark-export dependencies; not needed for checkpoint inference. | |
| -r cpu.lock.txt | |
| pandas==2.2.3 | |
| datasets==4.0.0 | |
| huggingface-hub==0.34.4 | |
| pyarrow==21.0.0 | |