Feature Extraction
Transformers
Safetensors
English
pivot
custom_code
decision-making
classification
routing
scoring
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)# 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: 454 Bytes
2d8be88 | 1 2 3 4 5 6 7 8 9 10 | from pathlib import Path
import json
from transformers import AutoModel, AutoTokenizer
root = Path(__file__).resolve().parent
tokenizer = AutoTokenizer.from_pretrained(root, trust_remote_code=True, local_files_only=True)
model = AutoModel.from_pretrained(root, trust_remote_code=True, local_files_only=True).eval()
request = json.loads((root / "serving/example_request.json").read_text())
print(json.dumps(model.decide(tokenizer, **request), indent=2))
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