* Add Lower LR submission: val_bpb=1.2230 (MATRIX_LR=0.02)

Systematic LR sweep showed default Muon/Adam learning rates (0.04) were
too high. MATRIX_LR=0.02, SCALAR_LR=0.02, TIED_EMBED_LR=0.03 gives
consistent improvement. Same 9L/512d architecture, no other changes.

* Add 10L Mixed Precision submission: val_bpb=1.2147

10 transformer layers (vs baseline 9) with mixed int8/int6 compression:
- Full int8 for first/last 3 layers (precision-sensitive)
- Int6 (step=4 rounding) for middle layers 3-6 (compression-friendly)
- Lower LR: MATRIX_LR=0.02, SCALAR_LR=0.02, TIED_EMBED_LR=0.03
- Artifact: 15,928,974 bytes (under 16MB cap)
- Improvement: 0.0097 bpb / 0.0217 nats over baseline (1.2244)

Also adds PRUNE_RATIO and INT4_LAYERS/INT4_STEP support to train_gpt.py
for mixed-precision post-training quantization.

* Revert root train_gpt.py to upstream baseline

The root script should remain the baseline. Submission-specific
modifications (PRUNE_RATIO, INT4_LAYERS, INT4_STEP) only belong
in the records/ folder copy.

* Warmdown-quantization co-optimization, val_bpb=1.2154

Novel finding: aggressive LR decay (WARMDOWN_ITERS=20000) reduces int8 quantization
penalty from 0.014 to 0.005 BPB. Combined with FP16 tied embeddings and moderate
NTK-RoPE extrapolation (eval@1408).

Full warmdown sweep across 10 values and detailed analysis in README.

* breakthrough: 1.1574 BPB via int6 + MLP 3x + sliding window stride=256

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Co-authored-by: Sam Larson <saml212@users.noreply.github.com>