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 configuration_pivot.py from Q1z/Pivot: direct link, hf CLI and curl.
- Browser
- Download file 374 Bytes
-
https://huggingface.co/Q1z/Pivot/resolve/main/configuration_pivot.py
- Command line
-
hf download hf://Q1z/Pivot/configuration_pivot.py
-
curl -L -o configuration_pivot.py https://huggingface.co/Q1z/Pivot/resolve/main/configuration_pivot.py
374 Bytes
| """Configuration for the Pivot set-scored decision encoder.""" | |
| from transformers import PretrainedConfig | |
| class PivotConfig(PretrainedConfig): | |
| model_type = "pivot" | |
| def __init__(self, dsbt_config=None, encoder_config=None, **kwargs): | |
| super().__init__(**kwargs) | |
| self.dsbt_config = dsbt_config or {} | |
| self.encoder_config = encoder_config or {} | |