Instructions to use Angshul/higher-order-pruner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angshul/higher-order-pruner with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Angshul/higher-order-pruner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 332 Bytes
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"format": "higher-order-pruner-v1",
"method": "diagonal-higher-order-kfac",
"defaults": {
"sparsity": 0.5,
"probes": 2,
"finite_difference_relative_step": 0.02,
"cubic_cap": 0.5,
"damping_ratio": 0.01,
"trust_ratio": 0.01,
"backtracking": [1.0, 0.5, 0.25, 0.125, 0.0625],
"seed": 91000
}
}
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