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/domains/support.gif from Meanblock/JEV-CPU: direct link, hf CLI and curl.
- Browser
- Download file 185 kB
-
https://huggingface.co/Meanblock/JEV-CPU/resolve/main/assets/domains/support.gif
- Command line
-
hf download hf://Meanblock/JEV-CPU/assets/domains/support.gif
-
curl -L -o support.gif https://huggingface.co/Meanblock/JEV-CPU/resolve/main/assets/domains/support.gif
185 kB

- Xet hash:
- 15f30a6af2bd8de6c8fc1f87d216507f9e38baa9d3662339d00442d01bcdc80d
- Size of remote file:
- 185 kB
- SHA256:
- b9b6dc94546c2b694e987f7302e3c73586efdfb691716681a1e7e6f7f5263195
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.