…ean)

Key changes vs. existing entries:
- Rotary NTK-Scaling bug fix: train_seq_len=2048 now correctly propagated
  to both base_model and eval_model (previously hardcoded to 1024)
- BIGRAM vocabulary size 3072 (vs 1536)
- Late QAT threshold 0.57 for ~1700 QAT steps (vs ~525 steps at 0.15)
- torch.no_grad() instead of torch.inference_mode() in TTT scoring phase
  to prevent Autograd graph corruption when RoPE caches cross phase boundary

Results (8xH100 SXM, 600s training + ~425s TTT):
  Seed 1337: legal_ttt_bpb = 1.11877
  Seed 42:   legal_ttt_bpb = 1.11836
  Seed 2025: legal_ttt_bpb = 1.11893
  Mean: 1.11869 ± 0.00024

The previous commit only included records/ changes but left train_gpt.py
as the unmodified baseline script. This commit adds the actual modified
training script used to achieve val_bpb 1.11869 (3-seed mean).

Features included:
- Legal Score-First TTT (test-time training, causal, barrier-synced)
- BigramHash 3072 vocabulary
- LeakyReLU(0.5)^2 MLP activation
- ValueEmbedding (VE128) on layers 9-10
- XSA (Cross-Sequence Attention) on last 4 layers
- EMA (decay=0.997) + Tight SWA (every 50 steps)
- Late QAT (GPTQ-lite int6 + lzma), threshold=0.57
- Sliding Window Eval (stride=64)
- Partial RoPE (16/64 dims), train_seq_len=2048 (Rotary NTK bug fixed)
- LN Scale (1/sqrt(layer+1))
- Parameter Banking + Parallel Muon optimizer

MD5: 5926353668cf98f9c97b2ec171b59818