Add MnemoDyn checkpoint

Orion_424/README.md

@@ -0,0 +1,33 @@
+---
+license: cc-by-nc-4.0
+library_name: pytorch-lightning
+tags:
+  - neuroscience
+  - fmri
+  - time-series
+  - pytorch-lightning
+---
+
+# MnemoDyn Checkpoint
+
+This repository contains a MnemoDyn checkpoint exported from this codebase.
+
+## Checkpoint
+
+- Source checkpoint: `model-epoch=68-val_mae=0.0001.ckpt`
+- Dataset: `GordonHCP`
+- Model class: `coe.light.model.main:LitORionModelOptimized`
+- Remote directory: `Orion_424`
+
+## Files
+
+- `Orion_424/model.ckpt`: Lightning checkpoint
+- `Orion_424/hparams.yaml`: training hyperparameters (if available)
+- `Orion_424/metrics.csv`: training/validation metrics (if available)
+- `Orion_424/load_from_hf.py`: minimal loading script
+
+## Usage
+
+```bash
+python Orion_424/load_from_hf.py --repo_id vhluong/MnemoDyn
+```

Orion_424/hparams.yaml

@@ -0,0 +1,52 @@
+D: 424
+D_out:
+- 424
+- 424
+- 424
+- 424
+K_LC:
+- 2
+- 2
+- 2
+- 2
+- 2
+- 2
+K_dense: 4
+LC_rank: 4
+conv_bias: true
+dataset_type: GordonHCP
+dense_rank: 8
+dim_d: 100
+dim_k: 300
+duration: 1
+epoch: 1000
+exp: debug
+h_rank: 3
+huber_delta: 1.0
+interpol: spline
+linear_bias: true
+loss_type: total
+lr: 0.001
+masked_modelling: false
+model_pred_save_freq: 100
+n_levels: 5
+nb: 5
+nonlinearity: tanh
+normalize: robust_scaler
+num_classes: 1
+num_sparse_LC: 4
+one_channel: -1
+original_length: 1200
+predict: false
+profile: false
+scale: false
+seed: 2025
+storage: /mnt/vhluong/Result/Orion_424/
+subset: true
+test_bs: 16
+train_bs: 8
+use_mRLoss: false
+use_mixed_precision: false
+use_residual: true
+valid_bs: 16
+wave: db2

Orion_424/load_from_hf.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+import argparse
+from huggingface_hub import hf_hub_download
+from coe.light.model.main import LitORionModelOptimized
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--repo_id", required=True, help="e.g. your-name/mnemodyn-gordon333")
+    parser.add_argument("--revision", default="main")
+    args = parser.parse_args()
+
+    ckpt_path = hf_hub_download(repo_id=args.repo_id, filename="Orion_424/model.ckpt", revision=args.revision)
+    model = LitORionModelOptimized.load_from_checkpoint(ckpt_path, map_location="cpu")
+    model.eval()
+    print("Loaded model from", ckpt_path)
+
+
+if __name__ == "__main__":
+    main()

Orion_424/metrics.csv

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Orion_424/model.ckpt

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