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
Download cpu-speed/benchmark.py from Q1z/Pivot: direct link, hf CLI and curl.
- Browser
- Download file 308 Bytes
-
https://huggingface.co/Q1z/Pivot/resolve/main/cpu-speed/benchmark.py
- Command line
-
hf download hf://Q1z/Pivot/cpu-speed/benchmark.py
-
curl -L -o benchmark.py https://huggingface.co/Q1z/Pivot/resolve/main/cpu-speed/benchmark.py
308 Bytes
| """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"]) | |