Create README.md

README.md

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+---
+tags:
+- adaptive-sparse-training
+- energy-efficient
+- sustainability
+metrics:
+- accuracy
+- energy_savings
+license: mit
+language:
+- en
+---
+
+# resnet18 (AST-Trained)
+
+**Trained with 65% less energy than standard training** ⚡
+
+## Model Details
+- **Architecture:** resnet18
+- **Dataset:** CIFAR-10
+- **Training Method:** Adaptive Sparse Training (AST)
+- **Target Activation Rate:** 35%
+
+## Performance
+- **Accuracy:** 6809.00%
+- **Energy Savings:** 65%
+- **Training Epochs:** 10
+
+## Sustainability Report
+This model was trained using Adaptive Sparse Training, which dynamically selects
+the most important training samples. This resulted in:
+
+- ⚡ **65% energy savings** compared to standard training
+- 🌍 **Lower carbon footprint**
+- ⏱️ **Faster training time**
+- 🎯 **Maintained accuracy** (minimal degradation)
+
+## How to Use
+
+```python
+import torch
+from torchvision import models
+
+# Load model
+model = models.resnet18(num_classes=10)
+model.load_state_dict(torch.load("pytorch_model.bin"))
+model.eval()
+
+# Inference
+# ... (your inference code)
+```
+
+## Training Details
+
+**AST Configuration:**
+- Target Activation Rate: 35%
+- Adaptive PI Controller: Enabled
+- Mixed Precision (AMP): Enabled
+
+## Reproducing This Model
+
+```bash
+pip install adaptive-sparse-training
+
+python -c "
+from adaptive_sparse_training import AdaptiveSparseTrainer, ASTConfig
+config = ASTConfig(target_activation_rate=0.35)
+# ... (full training code)
+"
+```
+
+## Citation
+
+If you use this model or AST, please cite:
+
+```bibtex
+@software{adaptive_sparse_training,
+    title={Adaptive Sparse Training},
+    author={Idiakhoa, Oluwafemi},
+    year={2024},
+    url={https://github.com/oluwafemidiakhoa/adaptive-sparse-training}
+}
+```
+
+## Acknowledgments
+
+Trained using the `adaptive-sparse-training` package. Special thanks to the PyTorch and HuggingFace communities.
+
+---
+
+*This model card was auto-generated by the AST Training Dashboard.*