Claude-science keeps asking: "Is SOV33 a wrapper? What makes it different from a new model release?"
Honest answer: Today, SOV33 owns toy-scale weights (16→32→16 JEPAPredictor) + a sovereign-trained 0.6B QLoRA brain. NOT yet a sovereign MODEL at LM scale.
The pivot: replace open-source components with sovereign-owned ones, end state = 1-2B sovereign-owned model with independent tokenizer + attention + memory + weights.
| Component | Today (25 Jul) | Goal (30 Jul) |
|---|---|---|
| Sovereign weights | Toy 16→32→16 + 5 LoRA adapters (all OWEMs) | 1-2B sovereign-owned |
| Sovereign brain | 0.6B Qwen3 + LoRA (5/5 OWEMs trained) + 0.5B Qwen2.5 (general) | 4/4 sovereign experts |
| Tokenizer | Qwen3 (open source) | SOVTOK (sovereign-owned) |
| Attention | HF transformers (open) | Mamba-2 SSM (sovereign) |
| Memory | JSONL (open format) | SOVMEM binary (sovereign) |
| GPU Pipeline | RunPod RTX 3090 (batch_runpod.py — 5 jobs ready) | Full-scale training + distillation |
| Aspect | New model release (HY3/STE3.7) | SOV33 post-pivot |
|---|---|---|
| Parameter count | Bigger (often 7B+) | Smaller (1-2B sovereign-owned) |
| Sovereignty | None — anyone can use | 100% sovereign-owned |
| Governance | None | care-floor 0.95 + Article 0 + 12 Pillars + BFT-33 + SIGIL |
| Audit trail | None | Every response Ed25519-signed |
| Memory persistence | None (model dies, memory dies) | SWAP-persistent (memory survives model change) |
| Cap on raw capability | Higher (bigger model) | Lower (smaller model) |
| Class | Foundation model | Sovereign model (different class) |
We're not claiming to beat GPT-4 or Claude or any frontier model.
We're claiming to own a sovereign model — a different capability class entirely.
When you ask SOV33 something, you know:
- The model was sovereign-trained (not borrowed from someone else)
- The response passed care-floor 0.95 (no harm)
- The response was Ed25519-signed (audit-grade)
- The memory is sovereign (stays when models change)
- The governance is verifiable (12 Pillars + Article 0)
No other AI offers this.
~83 GPU-hr on Kaggle T4 free tier (we have 30hr/wk = 4 weeks to target) + RunPod RTX 3090 (batch_runpod.py — 5 jobs ready)