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
File size: 227 Bytes
867babb | 1 2 3 4 5 6 7 8 9 10 11 | {
"model_type": "scaling_law_forecaster",
"base_model_name": "Qwen/Qwen3-1.7B",
"architectures": [
"ScalingLawForecaster"
],
"hidden_size": 2048,
"auto_map": {
"AutoModel": "model.ScalingLawForecaster"
}
} |