# Reproducibility Notes

## Checkpoint Conversion

The released files were converted from training checkpoints with:

```python
ckpt = torch.load("final.pt", map_location="cpu")
state = {k.removeprefix("module."): v.detach().cpu().contiguous() for k, v in ckpt["model"].items()}
safetensors.torch.save_file(state, "model.safetensors")
```

The original `.pt` files contain optimizer and training state. The released
`.safetensors` files contain only the model state dict required for inference.

## Verification

Each converted checkpoint was reloaded with `safetensors.torch.load_file`, and
the tensor key set was checked against the source PyTorch checkpoint. A forward
pass sanity check was also run using the TeX-1500 codebase and a synthetic
`[1, 64, 64, 64]` HSI tensor.

## Data Access

The TeX-1500 dataset is gated on Hugging Face. Users need to log in and accept
the dataset access conditions before downloading samples for reproduction.