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
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -156,7 +156,7 @@ If you use this model in your research, please cite:
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```bibtex
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@article{ncpl2026,
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title = {Neural Configuration to Performance Scaling Law},
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author = {
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journal = {arXiv preprint arXiv:2602.10300},
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year = {2026},
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url = {https://www.arxiv.org/abs/2602.10300}
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```bibtex
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@article{ncpl2026,
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title = {Neural Configuration to Performance Scaling Law},
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author = {Huaqing Zhang and Kaiyue Wen and Tengyu Ma},
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journal = {arXiv preprint arXiv:2602.10300},
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year = {2026},
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url = {https://www.arxiv.org/abs/2602.10300}
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