---
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