Upload djmgnn-pfas-finetuned model

finetuned_model/README.md

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+---
+license: mit
+tags:
+- molecular-property-prediction
+- graph-neural-network
+- chemistry
+- pytorch
+datasets:
+- qm9
+- spice
+- pfas
+metrics:
+- mse
+- mae
+pipeline_tag: graph-ml
+library_name: moml
+---
+
+# djmgnn-pfas-finetuned
+
+## Model Description
+
+This is a fine-tuned **DJMGNN** (Dense Jump Multi-Graph Neural Network) model for molecular property prediction. The model is designed to predict various molecular properties from graph representations of molecules.
+
+### Architecture
+
+- **Model Type**: Dense Jump Multi-Graph Neural Network (DJMGNN)
+- **Framework**: PyTorch
+- **Library**: MoML (Molecular Machine Learning)
+- **Task**: Molecular Property Prediction
+
+### Model Architecture Details
+
+- **Hidden Dimensions**: 128
+- **Number of Blocks**: 3
+- **Layers per Block**: 6
+- **Input Node Dimensions**: 11
+- **Input Edge Dimensions**: 0
+- **Node Output Dimensions**: 3
+- **Graph Output Dimensions**: 19
+- **Energy Output Dimensions**: 1
+- **Jumping Knowledge Mode**: cat
+- **Dropout Rate**: 0.2
+- **Uses Supernode**: True
+- **Uses RBF Features**: True
+- **RBF K**: 32
+
+## Training Details
+
+### Datasets
+
+The model was trained on the following datasets:
+- **QM9**: Quantum mechanical properties of small molecules
+- **SPICE**: Molecular dynamics data with forces and energies
+- **PFAS**: Per- and polyfluoroalkyl substances dataset
+
+### Training Configuration
+
+```yaml
+batch_size: 32
+early_stopping: true
+epochs: 100
+learning_rate: 0.001
+optimizer: Adam
+patience: 10
+validation_split: 0.2
+
+```
+
+## Usage
+
+### Loading the Model
+
+```python
+import torch
+from moml.models.mgnn.djmgnn import DJMGNN
+
+# Load the model
+model = DJMGNN(
+    in_node_dim=11,
+    in_edge_dim=0,
+    hidden_dim=128,
+    n_blocks=3,
+    layers_per_block=6,
+    node_output_dims=3,
+    graph_output_dims=19,
+    energy_output_dims=1,
+    jk_mode="cat",
+    dropout=0.2,
+    use_supernode=true,
+    use_rbf=true,
+    rbf_K=32
+)
+
+# Load the checkpoint
+checkpoint = torch.load("path/to/checkpoint.pt", map_location="cpu")
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+```
+
+### Making Predictions
+
+```python
+# Assuming you have a molecular graph 'data' (torch_geometric.data.Data object)
+with torch.no_grad():
+    output = model(
+        x=data.x,
+        edge_index=data.edge_index,
+        edge_attr=data.edge_attr,
+        batch=data.batch
+    )
+    
+    # Extract predictions
+    node_predictions = output["node_pred"]      # Node-level predictions
+    graph_predictions = output["graph_pred"]    # Graph-level predictions
+    energy_predictions = output["energy_pred"]  # Energy predictions
+```
+
+## Model Performance
+
+This model was fine-tuned from a base DJMGNN model on PFAS-specific data.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{djmgnn_model,
+  title={DJMGNN: Dense Jump Multi-Graph Neural Network for Molecular Property Prediction},
+  author={Your Name},
+  year={2024},
+  url={https://github.com/SAKETH11111/MoML-CA}
+}
+```
+
+## License
+
+This model is released under the MIT License.
+
+## Contact
+
+For questions or issues, please contact [email protected] or open an issue in the [GitHub repository](https://github.com/SAKETH11111/MoML-CA).

finetuned_model/config.json

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+{
+  "model_type": "djmgnn",
+  "architecture": "Dense Jump Multi-Graph Neural Network",
+  "task": "molecular_property_prediction",
+  "framework": "pytorch",
+  "library_name": "moml",
+  "hidden_dim": 128,
+  "n_blocks": 3,
+  "layers_per_block": 6,
+  "in_node_dim": 11,
+  "in_edge_dim": 0,
+  "node_output_dims": 3,
+  "graph_output_dims": 19,
+  "energy_output_dims": 1,
+  "jk_mode": "cat",
+  "dropout": 0.2,
+  "use_supernode": true,
+  "use_rbf": true,
+  "rbf_K": 32,
+  "training": {
+    "epochs": 100,
+    "batch_size": 32,
+    "learning_rate": 0.001,
+    "optimizer": "Adam",
+    "early_stopping": true,
+    "patience": 10,
+    "validation_split": 0.2
+  },
+  "checkpoint_path": "checkpoints/final_pfas_best.pt",
+  "model_name": "djmgnn-pfas-finetuned",
+  "license": "mit",
+  "tags": [
+    "molecular-property-prediction",
+    "graph-neural-network",
+    "chemistry",
+    "pytorch"
+  ],
+  "datasets": [
+    "qm9",
+    "spice",
+    "pfas"
+  ],
+  "metrics": [
+    "mse",
+    "mae"
+  ],
+  "pipeline_tag": "graph-ml"
+}

finetuned_model/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bac5dcb7ab9f9d59fd77b4ab02f4ce1380cf78d730e5541bdefd7d7d244a2f32
+size 130213414

finetuned_model/upload.py

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+#!/usr/bin/env python3
+'''
+Upload script for djmgnn-pfas-finetuned
+'''
+
+from huggingface_hub import HfApi, create_repo
+
+def upload_model():
+    # Initialize API
+    api = HfApi()
+    
+    # Create repository (if it doesn't exist)
+    repo_id = "saketh11/MoML-CA"
+    
+    try:
+        create_repo(repo_id, exist_ok=True)
+        print(f"Repository {repo_id} created/verified")
+    except Exception as e:
+        print(f"Error creating repository: {e}")
+        return
+    
+    # Upload files to finetuned_model subdirectory
+    api.upload_folder(
+        folder_path="hf_models/djmgnn_pfas_finetuned",
+        repo_id=repo_id,
+        path_in_repo="finetuned_model",
+        commit_message="Upload djmgnn-pfas-finetuned model"
+    )
+    
+    print(f"Model uploaded successfully to https://huggingface.co/{repo_id}")
+
+if __name__ == "__main__":
+    upload_model()