Initial release: 3 episodic-memory checkpoints (v1/v2/v4) + model card

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+  - state-space-model
+  - sparse-attention
+  - episodic-memory
+  - long-context
+  - interpretability
+language:
+  - en
+---
+
+# SynapNet — Episodic Memory Checkpoints
+
+Hybrid **SSM + sparse-attention + episodic-memory** architecture. This repo holds the base architectural checkpoints; the edge-deployment variants live at [`Vineetha00/synapnet-edge`](https://huggingface.co/Vineetha00/synapnet-edge).
+
+📦 **Code:** https://github.com/vineetha00/SynapNet_Exp
+🛠️ **Deployment companion:** https://github.com/vineetha00/SynapNet-Edge · 🤗 https://huggingface.co/Vineetha00/synapnet-edge
+
+---
+
+## Checkpoints in this repo
+
+| File | Variant | Size | Notes |
+|---|---|---|---|
+| [`synapnet_memory_v4.pt`](synapnet_memory_v4.pt) | Track 3 v4 (current best) | 5.7 MB | Trained with salience supervision λ=0.01; near-perfect episodic recall |
+| [`synapnet_memory_v2.pt`](synapnet_memory_v2.pt) | Track 3 v2 | 6.8 MB | Earlier episodic-recall checkpoint |
+| [`synapnet_memory.pt`](synapnet_memory.pt) | Original episodic memory | 18.2 MB | Initial release of the WriteableMemory variant |
+
+---
+
+## Architecture (Track 3 v4 — recommended)
+
+- `SimpleSSM` (depthwise conv, kernel_size=9) for local temporal dynamics
+- `SparseEventAttention` (salience-gated top-K mixing)
+- `WriteableMemory` (top-K hidden states written to a fixed-size bank, read via cross-attention)
+- Gated fusion (α, β) over the three pathways
+- 4 stacked blocks, dim=128, heads=4
+
+---
+
+## Key finding (λ-sweep, 5 seeds, ctx=2048, 32-class recall)
+
+A small dose of salience supervision flips episodic recall from chance to near-perfect:
+
+| λ (salience-supervision strength) | Accuracy | Write hit-rate (target token written?) |
+|---|---|---|
+| 0.00 | 0.723 ± 0.329 | 0.125 ± 0.109 |
+| **0.01** | **0.968 ± 0.022** | **0.993 ± 0.002** |
+| 0.10 | 0.970 ± 0.021 | 0.995 ± 0.001 |
+| 1.00 | 0.838 ± 0.125 | 0.995 ± 0.002 |
+
+The v4 checkpoint was trained at the sweet-spot λ=0.01.
+
+---
+
+## Loading
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Make sure SynapNet_Exp is on your path:
+#   git clone https://github.com/vineetha00/SynapNet_Exp
+#   cd SynapNet_Exp
+import sys; sys.path.insert(0, "/path/to/SynapNet_Exp")
+
+from synapnet_memory import SynapEpisodicNet
+
+ckpt_path = hf_hub_download(
+    repo_id="Vineetha00/synapnet",
+    filename="synapnet_memory_v4.pt",
+)
+state = torch.load(ckpt_path, map_location="cpu")
+
+model = SynapEpisodicNet(
+    dim=128, depth=4, vocab_size=2048,
+    max_len=2048, num_classes=32, heads=4,
+)
+model.load_state_dict(state)
+model.eval()
+```
+
+---
+
+## Training tasks (from the companion code)
+
+- **Track 1** — LRA-style long-range classification (`train_track1_lra.py`)
+- **Track 2** — biosignal regression / ECG reconstruction (`train_track2_biosignal.py`)
+- **Track 3** — episodic recall (`train_track3_memory_v2/v3/v4.py`)
+- **Track 4** — faithful-salience supervision for interpretability (`train_track4_interpret.py`)
+
+---
+
+## License
+
+MIT — see [LICENSE](https://github.com/vineetha00/SynapNet_Exp/blob/main/LICENSE).
+
+## Citation
+
+```bibtex
+@article{synapnet_2026,
+  title={SynapNet: Hybrid SSM + Sparse-Attention + Episodic Memory for Long-Range Sequence Modelling},
+  author={Vallish Kumar, Vineetha},
+  year={2026},
+}
+```

synapnet_memory.pt

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+size 19115405

synapnet_memory_v2.pt

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synapnet_memory_v4.pt

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