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 training/train_report.json from Q1z/Pivot: direct link, hf CLI and curl.
- Browser
- Download file 4.1 kB
-
https://huggingface.co/Q1z/Pivot/resolve/main/training/train_report.json
- Command line
-
hf download hf://Q1z/Pivot/training/train_report.json
-
curl -L -o train_report.json https://huggingface.co/Q1z/Pivot/resolve/main/training/train_report.json
4.1 kB
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