Upload SpatialGT pretrained model

README.md

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+---
+license: mit
+tags:
+  - spatial-transcriptomics
+  - graph-transformer
+  - gene-expression
+  - pretrained
+  - pytorch
+language:
+  - en
+library_name: transformers
+pipeline_tag: feature-extraction
+---
+
+# SpatialGT Pretrained Model
+
+## Model Description
+
+This is the **pretrained checkpoint** of SpatialGT (Spatial Graph Transformer), a graph transformer model designed for spatial transcriptomics data analysis.
+
+SpatialGT leverages spatial context through neighbor-aware attention mechanisms for:
+- 🗺️ Spatial context learning from large-scale spatial transcriptomics data
+- 🧬 Gene expression reconstruction
+- 🔬 Perturbation simulation
+
+## Model Details
+
+- **Architecture**: Graph Transformer with spatial neighbor attention
+- **Parameters**: ~600M
+- **Training Data**: Large-scale spatial transcriptomics atlas
+- **Input**: Gene expression vectors with spatial coordinates
+- **Output**: Contextualized gene expression representations
+
+## Usage
+
+```python
+import torch
+from pretrain.model_spatialpt import SpatialNeighborTransformer
+from pretrain.Config import Config
+
+# Load configuration
+config = Config()
+
+# Initialize model
+model = SpatialNeighborTransformer(config)
+
+# Load pretrained weights
+from safetensors.torch import load_file
+state_dict = load_file("model.safetensors")
+model.load_state_dict(state_dict)
+
+model.eval()
+```
+
+## Files
+
+- `model.safetensors`: Model weights in safetensors format
+- `training_args.bin`: Training arguments
+- `trainer_state.json`: Training state information
+
+## Citation
+
+If you use this model, please cite our paper (details to be added upon publication).
+
+## License
+
+MIT License

checkpoint/model.safetensors

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