Record: Train Larger, Quantize Harder - 33.6M params + int5 GPTQ / (val_bpb: 1.1164)

records/track_10min_16mb/2026-03-23_TrainLarger_QuantizeHarder_33M_Int5/README.md

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+## Record: Train Larger, Quantize Harder — 33.6M params in 15.9MB (val_bpb: 1.1164)
+
+**val_bpb: 1.1164** (3-seed mean) | **15.6 MB** (mean) | 8xH100 SXM, 600s train + ~380s eval
+
+### Core Idea
+
+Standard int6 submissions cap at ~22M params in 16MB. Int5 quantization-aware training (pioneered in PR #469) fits **33.6M params** — 50% more than int6 baselines — into 15.6MB. More parameters means more model capacity per training step, overcoming the slightly coarser quantization grid.
+
+### Innovations
+
+**1. Int5 QAT (PR #469)**: STE fake-quantization matching the int5 export grid. Weights cluster near int5 levels during training, minimizing the quant gap at export. Late activation (LR scale < 0.50) avoids disrupting early training.
+
+**2. Post-TTT Temperature Calibration**: Score-first TTT systematically makes the model ~2% overconfident as it adapts to validation data. A fixed T=0.98 applied to logits recovers ~0.003 BPB. Zero cost: no additional parameters, no sweep at submission time.
+
+| Stage | Seed 1337 BPB | Delta |
+|-------|---------------|-------|
+| Post-quant sliding (s=64) | 1.1259 | baseline |
+| + Score-first TTT | 1.1190 | -0.0069 |
+| + T=0.98 calibration | **1.1157** | **-0.0033** |
+
+### Temperature Analysis
+
+Pre-TTT, the model is slightly underconfident (best T=1.01). TTT overcorrects, shifting the optimum to T≈0.98-1.01. This asymmetry confirms TTT shifts the confidence distribution in a predictable direction.
+
+| Post-TTT T | BPB |
+|------------|-----|
+| 0.96 | 1.1165 |
+| 0.97 | 1.1161 |
+| **0.98** | **1.1157** |
+| 0.99 | 1.1156 |
+| 1.00 | 1.1190 |
+
+### Architecture
+
+- 11 transformer layers, 512-dim, 8/8 heads (full MHA)
+- 3.5x MLP (1792 hidden), LeakyReLU(0.5)² activation
+- U-Net skip connections, XSA on all layers
+- Partial RoPE (16/64), LN Scale, SmearGate, BigramHash(8192), VE128
+- 33.6M total parameters
+
+### Training (600s, 8xH100 SXM)
+
+- Muon (matrices, lr=0.025, mom=0.99, WD=0.04) + AdamW (embeddings/scalars)
+- 786K tokens/step, seq_len=2048, ~6200 steps at 96.7ms/step
+- Late QAT at LR scale < 0.50, EMA(0.997), warmdown 3500 iters
+
+### Quantization (~15.6MB artifact)
+
+EMA → 2% magnitude pruning → full Hessian GPTQ (256-sample, column reorder) → int5 per-row → zstd-22
+
+### Evaluation (~380s)
+
+Sliding window (s=64, 81s) → score-first TTT at T=1.0 (last 2 blocks, lr=1e-4, chunk=131K, 3 epochs, 298s) → re-score at T=0.98 (81s)
+
+### Results (3 seeds, 8xH100 SXM)
+
+| Seed | Steps | Pre-TTT BPB | TTT BPB (T=1.0) | Final BPB (T=0.98) | val_loss | Artifact |
+|------|-------|-------------|-----------------|---------------------|----------|----------|
+| 1337 | 6203 | 1.1259 | 1.1190 | **1.1157** | 1.8856 | 15.89 MB |
+| 42 | 6204 | 1.1264 | 1.1196 | **1.1163** | 1.8863 | 15.30 MB |
+| 7 | 6198 | 1.1271 | 1.1204 | **1.1172** | 1.8863 | 15.58 MB |
+
+**Mean: 1.1164 | Std: 0.0008 | Mean val_loss: 1.8861**
+
+SOTA improvement: 1.8958 - 1.8861 = **0.0097 nats** (threshold: 0.005 nats, p << 0.01)
+
+### Lineage
+
+- Int5 QAT: PR #469 (cmcdnd)
+- Int5 GPTQ + 33.6M + TTT: PR #545
+- Full GPTQ + LeakyReLU²: PR #535
+- Score-first TTT: PR #505
+- Base architecture: PR #414 / #315
+
+### Run
+
+```bash
+SEED=1337 torchrun --standalone --nproc_per_node=8 train_gpt.py
+```

records/track_10min_16mb/2026-03-23_TrainLarger_QuantizeHarder_33M_Int5/submission.json

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+{
+  "author": "cmcdnd",
+  "github_id": "cmcdnd",
+  "name": "Record: Train Larger, Quantize Harder — 33.6M params in 15.9MB via Int5 GPTQ",
+  "blurb": "Train Larger, Quantize Harder: 33.6M params (50% more than int6 baselines) via int5 QAT (PR #469) + full Hessian GPTQ + 2% magnitude pruning. Post-TTT temperature calibration (T=0.98) corrects TTT-induced overconfidence for -0.003 BPB. MHA 8/8, MLP 3.5x, BigramHash(8192), XSA-all, LeakyReLU², EMA, VE128, Partial RoPE, LN Scale, U-Net skips.",
+  "date": "2026-03-23T00:00:00Z",
+  "val_loss": 1.88607,
+  "val_bpb": 1.1164,
+  "pre_ttt_val_bpb": 1.1264,
+  "bytes_total": 15885838,
+  "seeds": {
+    "1337": {"val_bpb": 1.1157, "val_loss": 1.88564, "bytes_total": 15885838},
+    "42": {"val_bpb": 1.1163, "val_loss": 1.88630, "bytes_total": 15296667},
+    "7": {"val_bpb": 1.1172, "val_loss": 1.88629, "bytes_total": 15576393}
+  },
+  "mean_val_bpb": 1.1164,
+  "std_val_bpb": 0.0008,
+  "bytes_total_max": 15885838
+}