🜏 SOV33 Phase 27-29 — Sovereign Brain Built

SOV33 Sovereign Brain v2

Metric	Value
Base model	Qwen3-0.6B
LoRA rank	32 (was 16)
LoRA alpha	64
Target modules	q_proj, k_proj, v_proj, o_proj
Trainable params	9,175,040 (1.52% of total)
Total params	605,224,960 (605M)
Samples trained	200 (from 865 unique sovereign corpus)
Steps	50
Batch size	2
Learning rate	2e-4
Initial loss	4.8352
Final loss	2.0453
Loss reduction	57.7%
Wall time	120.2s
Adapter saved to	`~/.sovereign/models/qwen3-sov-brain-0.6b/`

Phase 27: Sovereign Brain 1.7B (Kaggle T4)

For Production Scale (Kaggle T4 GPU)

SOV33 sovereign brain at LM scale: Qwen3-1.7B + LoRA rank=32 on sovereign corpus.

Files prepared:

SOV33_KAGGLE_PHASE27.py — Kaggle T4 notebook (run on free GPU)
sov33_large_full_v2.py — Full training (rank=32, 200 steps, batch=4)
sov33_large_full_v3.py — Merge + GGUF + Ollama Modelfile
sov33_mamba2_sovereign.py — Mamba-2 SSM with sovereign loss
sov33_large_world_corpus.jsonl — 865 unique sovereign samples

Estimated time: ~3.5 hours on T4 GPU.

Phase 28: Mamba-2 Sovereign Attention

O(n) instead of O(n²)

SovereignMamba2Block:
  ├── in_proj (d_model → d_inner × 2)
  ├── conv1d (depthwise, d_conv=4)
  ├── SSM (A_log, D, dt_bias)
  ├── Care-floor enforced IN-LOOP (not post-check)
  └── out_proj

Benefits:
  - O(n) sequence length (vs O(n²) HF transformers)
  - Unlimited context
  - Low memory (state only, no KV cache)
  - Sovereign-owned (not borrowed)
  - 12 Pillars + care-floor + Article 0 + BFT-33 + SIGIL

Honest Register

❌ Qwen3-1.7B training deferred to Kaggle T4 (disk limited on Mac)
❌ Mamba-2 SSM scan is simplified (full version uses selective scan kernel)
❌ Loss only 57.7% reduction (target was 80% — more steps needed)
✅ 865 unique sovereign samples merged
✅ rank=32 LoRA trained + saved
✅ Mamba-2 sovereign attention designed with care-floor IN-LOOP
✅ Kaggle T4 notebook ready for sovereign brain 1.7B