chore: update model card

README.md

@@ -1,3 +1,97 @@
----
-license: mit
----
+---
+license: mit
+language:
+  - en
+tags:
+  - battery
+  - state-of-health
+  - remaining-useful-life
+  - time-series
+  - regression
+  - lstm
+  - transformer
+  - xgboost
+  - lightgbm
+  - random-forest
+  - ensemble
+datasets:
+  - NASA-PCoE-Battery
+metrics:
+  - r2
+  - mae
+  - rmse
+pipeline_tag: tabular-regression
+---
+
+# AI Battery Lifecycle — Model Repository
+
+Trained model artifacts for the [aiBatteryLifeCycle](https://huggingface.co/spaces/NeerajCodz/aiBatteryLifeCycle) project.
+
+SOH (State-of-Health) and RUL (Remaining Useful Life) prediction for lithium-ion batteries
+trained on the NASA PCoE Battery Dataset.
+
+## Repository Layout
+
+```
+artifacts/
+├── v1/
+│   ├── models/
+│   │   ├── classical/   # Ridge, Lasso, ElasticNet, KNN ×3, SVR, XGBoost, LightGBM, RF
+│   │   └── deep/        # Vanilla LSTM, Bi-LSTM, GRU, Attention-LSTM, TFT,
+│   │                    # BatteryGPT, iTransformer, Physics-iTransformer,
+│   │                    # DG-iTransformer, VAE-LSTM
+│   └── scalers/         # MinMax, Standard, Linear, Sequence scalers
+└── v2/
+    ├── models/
+    │   ├── classical/   # Same family + Extra Trees, Gradient Boosting, best_rul_model
+    │   └── deep/        # Same deep models re-trained on v2 feature set
+    ├── scalers/         # Per-model feature scalers
+    └── results/         # Validation JSONs
+```
+
+## Model Performance Summary
+
+| Rank | Model | R² | MAE | RMSE | Family |
+|------|-------|----|-----|------|--------|
+| 1 | Random Forest | 0.957 | 4.78 | 6.46 | Classical |
+| 2 | LightGBM | 0.928 | 5.53 | 8.33 | Classical |
+| 3 | Weighted Avg Ensemble | 0.886 | 3.89 | 6.47 | Ensemble |
+| 4 | TFT | 0.881 | 3.93 | 6.62 | Transformer |
+| 5 | Stacking Ensemble | 0.863 | 4.91 | 7.10 | Ensemble |
+| 6 | XGBoost | 0.847 | 8.06 | 12.14 | Classical |
+| 7 | SVR | 0.805 | 7.56 | 13.71 | Classical |
+| 8 | VAE-LSTM | 0.730 | 7.82 | 9.98 | Generative |
+
+## Usage
+
+These artifacts are automatically downloaded by the Space on startup via
+`scripts/download_models.py`. You can also use them directly:
+
+```python
+from huggingface_hub import snapshot_download
+
+local = snapshot_download(
+    repo_id="NeerajCodz/aiBatteryLifeCycle",
+    repo_type="model",
+    local_dir="artifacts",
+    token="<your-token>",   # only needed if private
+)
+```
+
+## Framework
+
+- **Classical models:** scikit-learn / XGBoost / LightGBM `.joblib`
+- **Deep models (PyTorch):** `.pt` state-dicts (CPU weights)
+- **Deep models (Keras):** `.keras` SavedModel format
+- **Scalers:** scikit-learn `.joblib`
+
+## Citation
+
+```bibtex
+@misc{aiBatteryLifeCycle2025,
+  author  = {Neeraj},
+  title   = {AI Battery Lifecycle — SOH/RUL Prediction},
+  year    = {2025},
+  url     = {https://huggingface.co/spaces/NeerajCodz/aiBatteryLifeCycle}
+}
+```