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 src/semif_phase1/__init__.py from Meanblock/JEV-CPU: direct link, hf CLI and curl.
- Browser
- Download file 95 Bytes
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https://huggingface.co/Meanblock/JEV-CPU/resolve/main/src/semif_phase1/__init__.py
- Command line
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hf download hf://Meanblock/JEV-CPU/src/semif_phase1/__init__.py
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curl -L -o __init__.py https://huggingface.co/Meanblock/JEV-CPU/resolve/main/src/semif_phase1/__init__.py
95 Bytes
| """SemIf Phase 1: direct option-logit and native reranker baselines.""" | |
| __version__ = "0.1.0" | |