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
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Download assets/README.md from Meanblock/JEV-CPU: direct link, hf CLI and curl.
- Browser
- Download file 692 Bytes
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https://huggingface.co/Meanblock/JEV-CPU/resolve/main/assets/README.md
- Command line
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hf download hf://Meanblock/JEV-CPU/assets/README.md
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curl -L -o README.md https://huggingface.co/Meanblock/JEV-CPU/resolve/main/assets/README.md
692 Bytes
Visual assets
semif-hero.png was generated for this project with the built-in image-generation tool. It depicts one unstructured input stream passing through a shared semantic core into many parallel typed decision branches. It contains no third-party media, logos, or text.
Prompt summary: a premium, minimal, wide technical illustration on a near-black grid; dense unstructured information entering from the left; one luminous semantic core; twenty-one crisp cyan/lime/amber decision branches and probability nodes on the right; no people, robots, brains, brands, UI screenshots, pseudo-text, or watermark.
SHA-256: 53f2d48564785f5bf52a8fcaa1f6dcb94e5d73054b115f68b792b4de7b08f104