Merge pull request openai#63 from yahya010/submission/seq2048-fp16emb

Record: 10L Int6 QAT + Zstd MLP2.6x Muon0.99 Sliding Window (val_bpb 1.1598)

Author: cocohearts

records/track_10min_16mb/2026-03-19_Seq2048_FP16Emb_TunedLR/README.md

@@ -0,0 +1,68 @@
+# 10L Int6 QAT + Zstd MLP2.6x Muon0.99 Sliding Window
+
+## Summary
+
+Stacked improvements on the Naive Baseline:
+
+1. **10 transformer layers** (from 9).
+
+2. **STE int6 QAT**: Straight-through estimator fake quantization during training. Each CastedLinear forward pass applies `fake_quantize_int6(w)` — quantize to [-31,31], dequantize, with gradients flowing through via STE. This teaches the model to be robust to int6 quantization, **completely eliminating the quant gap** (pre-quant = post-quant loss).
+
+3. **Full int6 quantization**: All 2D block weights quantized to [-31,31] (63 levels) in int8 container.
+
+4. **zstd-22 compression**: Better than zlib for int6 data.
+
+5. **MLP hidden 1344** (2.625x model_dim): Wider MLP enabled by int6+zstd savings.
+
+6. **FP16 tied embedding passthrough**.
+
+7. **Sequence length 2048**.
+
+8. **Muon momentum 0.99**, warmup from 0.92 over 1500 steps.
+
+9. **MATRIX_LR=0.02, SCALAR_LR=0.02, TIED_EMBED_LR=0.04**.
+
+10. **Gradient clipping** GRAD_CLIP_NORM=0.3.
+
+11. **Sliding window evaluation** stride=64.
+
+## Configuration
+
+```bash
+MLP_HIDDEN=1344 \
+DATA_PATH=./data/datasets/fineweb10B_sp1024/ \
+TOKENIZER_PATH=./data/tokenizers/fineweb_1024_bpe.model \
+VOCAB_SIZE=1024 \
+MAX_WALLCLOCK_SECONDS=600 \
+EVAL_STRIDE=64 \
+torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+Requires: `pip install zstandard`
+
+## Results
+
+| Seed | Steps | val_bpb (standard) | val_bpb (sliding) | Artifact size |
+|------|-------|--------------------|--------------------|---------------|
+| 1337 | 8,319 | 1.1821 | 1.1610 | 15,558,319 |
+| 42 | ~8,300 | ~1.1815 | 1.1598 | ~15,558,000 |
+| 3 | ~8,300 | ~1.1810 | 1.1586 | ~15,558,000 |
+
+**Mean val_bpb (sliding): 1.1598** (std: 0.00120)
+**Mean val_loss (sliding): 1.9583** (std: 0.00203)
+
+Quant gap: **0.0000** — STE QAT completely eliminated quantization loss.
+
+Statistical significance vs SOTA (1.2244 BPB / 2.0727 val_loss):
+- Improvement: 0.1144 nats (threshold: 0.005)
+- t-statistic: -93.6, df=2, p << 0.01
+
+Hardware: 8xH100 80GB HBM3, PyTorch 2.8.0+cu128, ~72ms/step avg.
+QAT overhead: ~28% (72ms vs 69ms without QAT).
+Sliding window eval time: ~370s.
+
+## Included Files
+
+- `train_gpt.py` (modified training script)
+- `train_seed1337.log`, `train_seed42.log`, `train_seed3.log`
+- `submission.json`

records/track_10min_16mb/2026-03-19_Seq2048_FP16Emb_TunedLR/submission.json

@@ -0,0 +1,11 @@
+{
+  "author": "yahya010",
+  "github_id": "yahya010",
+  "name": "10L Int6 QAT + Zstd MLP2.6x Muon0.99 Sliding Window",
+  "blurb": "10-layer 512dim SP-1024, STE int6 QAT (zero quant gap), full int6 [-31,31] + zstd-22, MLP hidden=1344, fp16 tied embedding, Muon 0.99, LR 0.02, grad clip 0.3, sliding window stride=64.",
+  "date": "2026-03-19",
+  "val_loss": 1.95627871,
+  "val_bpb": 1.15861696,
+  "bytes_total": 15558319,
+  "bytes_code": 56005
+}