Feature Extraction
Transformers
PyTorch
scaling_law_forecaster
scaling-laws
neural-scaling
performance-prediction
configuration-to-performance
custom_code
Instructions to use OptimizerStudy/NCPL-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OptimizerStudy/NCPL-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OptimizerStudy/NCPL-final", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OptimizerStudy/NCPL-final", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 238873ee00d50c3cb311ce358b02227e88e53b6ecd28217973f992338839ccde
- Size of remote file:
- 6.92 GB
- SHA256:
- 4d6d4f3b5c94d9d284a63c00a047158ac6dd9b637f5576b20d54fae1b4fa8905
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