Upload LRA-Image Softmax (RoPE, causal) (test_acc=68.88%)

README.md

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+---
+license: apache-2.0
+library_name: pytorch
+tags:
+  - image-classification
+  - cifar-10
+  - lra
+  - sequence-model
+  - interdomain-attention
+datasets:
+  - cifar10
+metrics:
+  - accuracy
+---
+
+# LRA-Image Softmax (RoPE, causal)
+
+Softmax (RoPE, causal) model trained on the Long Range Arena (LRA) sCIFAR-10 benchmark.
+
+## Model Details
+
+- **Architecture**: Softmax (RoPE, causal)
+- **Task**: Sequential CIFAR-10 (grayscale, 1024 tokens)
+- **Parameters**: ~4.1M
+- **Position encoding**: RoPE
+- **Causal**: True
+- **Test Accuracy**: 68.88%
+
+## Training
+
+- **Protocol**: S4 paper LRA-Image (200 epochs, lr=1e-3, batch=64, warmup=18k steps)
+- **Backbone**: Llama-style (6 layers, d=512, 8 heads, RMSNorm, SwiGLU)
+- **Seed**: 2222
+- **WandB run**: [harrisonzhu/InterdomainAttention/x437k0ed](https://wandb.ai/harrisonzhu/InterdomainAttention/runs/x437k0ed)
+
+## Usage
+
+```python
+import torch
+from model import LlamaLRAImage  # requires interdomain-attention repo
+
+state_dict = torch.load("lra_image_best.pt", weights_only=True)
+model = LlamaLRAImage(...)  # match config
+model.load_state_dict(state_dict["model"])
+```
+
+## Citation
+
+```bibtex
+@article{interdomain2026,
+  title={Interdomain Attention},
+  author={...},
+  year={2026}
+}
+```
+
+## License
+
+Apache 2.0

lra_image_best.pt

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