AI Model Pruner
This tool helps optimize AI model architecture by pruning unnecessary weights and connections, resulting in a more efficient and lightweight model. It's useful for deploying models on edge devices or in resource-constrained environments. The tool uses techniques like gradient-based pruning and magnitude-based pruning to identify and remove redundant model parameters.
Usage
python model_pruner.py --input_model model.pt --pruning_ratio 0.5 --pruning_method magnitude-based --output_model pruned_model.ptSource Code
import argparse
import torch
from sklearn.metrics import accuracy_score
def load_model(model_path):
if model_path.endswith('.pt'):
return torch.load(model_path)
else:
raise ValueError('Unsupported model file format')
def prune_model(model, pruning_ratio, pruning_method):
if pruning_method == 'gradient-based':
# Gradient-based pruning implementation
pass
elif pruning_method == 'magnitude-based':
# Magnitude-based pruning implementation
pass
return model
def save_model(model, output_path):
torch.save(model, output_path)
def main():
parser = argparse.ArgumentParser(description='AI Model Pruner')
parser.add_argument('--input_model', required=True, help='Trained model file')
parser.add_argument('--pruning_ratio', type=float, required=True, help='Pruning ratio')
parser.add_argument('--pruning_method', choices=['gradient-based', 'magnitude-based'], required=True, help='Pruning method')
parser.add_argument('--output_model', required=True, help='Pruned model file')
args = parser.parse_args()
model = load_model(args.input_model)
pruned_model = prune_model(model, args.pruning_ratio, args.pruning_method)
save_model(pruned_model, args.output_model)
if __name__ == '__main__':
main()README
Model Pruner
This tool helps optimize AI model architecture by pruning unnecessary weights and connections, resulting in a more efficient and lightweight model.
Usage
To use the model pruner, simply run the script with the following arguments:
* --input_model: The path to the trained model file
* --pruning_ratio: The pruning ratio
* --pruning_method: The pruning method (either gradient-based or magnitude-based)
* --output_model: The path to the pruned model file
Example
python model_pruner.py --input_model model.pt --pruning_ratio 0.5 --pruning_method magnitude-based --output_model pruned_model.ptCommunity
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Details
- Tool Name
- model_pruner
- Category
- AI Model Optimization
- Generated
- August 16, 2026
- Tests
- Passing ✅
- Fix Loops
- 2
Quick Install
Clone just this tool:
git clone --depth 1 --filter=blob:none --sparse \ https://github.com/ptulin/autoaiforge.git cd autoaiforge git sparse-checkout set generated_tools/2026-08-16/model_pruner cd generated_tools/2026-08-16/model_pruner pip install -r requirements.txt 2>/dev/null || true python model_pruner.py