Instructions to use Meanblock/JEV-CPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Meanblock/JEV-CPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Meanblock/JEV-CPU")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Meanblock/JEV-CPU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/jev-cpu-demo.gif from Meanblock/JEV-CPU: direct link, hf CLI and curl.
- Browser
- Download file 1.09 MB
-
https://huggingface.co/Meanblock/JEV-CPU/resolve/main/assets/jev-cpu-demo.gif
- Command line
-
hf download hf://Meanblock/JEV-CPU/assets/jev-cpu-demo.gif
-
curl -L -o jev-cpu-demo.gif https://huggingface.co/Meanblock/JEV-CPU/resolve/main/assets/jev-cpu-demo.gif
1.09 MB

- Xet hash:
- 16268a4e2870f59d12b93f4770c962d05840382d048226fcd33c3e8a6c9738c4
- Size of remote file:
- 1.09 MB
- SHA256:
- 596a11d2546a76f3b2d5576e910376a44bfb7426cb8ab75e0aa7e80327a2b49b
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