# Artifacts

| File | What |
|---|---|
| `spira_model.npz` (72 MB) | full trained v2 checkpoint: EPT stats, reservoir + slow area (W_out, W_out_G, homeostatic state incl. β), fusion calibration, hippocampus (exact derivable-slot round-trip) — resume with `SPIRA.load()` |
| `spira_model.npz.raster.npz` | spike raster recorded over the last 1500 ticks of training (feeds the Brain tab) |
| `metrics.json` | full v2 evaluation: coverage curve, compression, prediction + hierarchy ablation, one-shot, **replay consolidation experiment**, correction (two load regimes), generation, efficiency |
| `spira_model_v3_ab.npz` (45.9 MB) | **v3 A/B checkpoint — vocab 4096**, trained under the controlled A/B protocol (200k tokens × 2 epochs, AM 50k contexts) for the cortex-representation-repair experiment; it is **not** a replacement for the main 16k-vocab v2 checkpoint `spira_model.npz` above. Load with `SPIRA.load()` using the v3 code in `../spira/`. |
| `metrics_v3_ab.json` | v3 A/B results: protocol, both arms' full metrics, the matched-vocab bigram reference (4.94%), and the headline comparison (SNN alone, in-vocab, held-out: v2 0.40% → v3 2.07%) — see `../../V3_REPORT.md` |
| `chunks.jsonl` | training-chunk log (historical) |
| `web_*.png` | browser-demo screenshots |

`spira_deploy.json` (29 MB, browser-inference export, format v2) is **not duplicated
here** — it lives at `web/public/spira_deploy.json`, where the demo fetches it.
Regenerate it with:

```bash
python3 -m spira.export_deploy --model artifacts/spira_model.npz \
    --out ../web/public/spira_deploy.json --am-cap 100000 --am-min-count 1
```

Figures are generated by `scripts/make_figures.py` and stored with the paper in
`../paper/figures/` (incl. `fig_vocab.pdf` and `fig_replay.pdf`).