Feature Extraction
Transformers
PyTorch
scaling_law_forecaster
scaling-laws
neural-scaling
performance-prediction
configuration-to-performance
custom_code
Instructions to use OptimizerStudy/NCPL-intermediate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OptimizerStudy/NCPL-intermediate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OptimizerStudy/NCPL-intermediate", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OptimizerStudy/NCPL-intermediate", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9393b90cd6e69504057ad9fb0926e82201879705d8f519da807280320b6212d
- Size of remote file:
- 6.92 GB
- SHA256:
- d4a2c1fb93f2824e48d36c49abbcfa0fd661006f97bcd786f49504199d9d3c0a
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