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/code-review.gif from Meanblock/JEV-CPU: direct link, hf CLI and curl.
- Browser
- Download file 211 kB
-
https://huggingface.co/Meanblock/JEV-CPU/resolve/main/assets/domains/code-review.gif
- Command line
-
hf download hf://Meanblock/JEV-CPU/assets/domains/code-review.gif
-
curl -L -o code-review.gif https://huggingface.co/Meanblock/JEV-CPU/resolve/main/assets/domains/code-review.gif
211 kB

- Xet hash:
- 6e4c15f3691bfd48a8e33b11238349e98b79144a9f42f62707916da93981f711
- Size of remote file:
- 211 kB
- SHA256:
- a1395be33943a28c42f654549b31d863a65f859625b2e1756cdca7cff7491030
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