Feature Extraction
Transformers
Safetensors
pivot
decision-making
classification
scoring
custom_code
Instructions to use Q1z/Pivot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q1z/Pivot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Q1z/Pivot", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Q1z/Pivot", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 308 Bytes
7c85c7e | 1 2 3 4 5 6 7 8 9 10 | """CPU-only wrapper for the pinned public JevBench speed protocol."""
from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "benchmarks"))
from jevbench_public import main
if __name__ == "__main__":
main([*sys.argv[1:], "--device", "cpu", "--mode", "speed"])
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