resouer · resouer · Apr 6, 2026
diff --git a/...ds/track_10min_16mb/2026-04-06_SP8192_ParallelResiduals_CoprimeStride/README.md b/...ds/track_10min_16mb/2026-04-06_SP8192_ParallelResiduals_CoprimeStride/README.md
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+# Record: SP8192 + Parallel Residuals + Coprime-Stride Loader
+
+**val_bpb = 1.08459** (3-seed mean, std 0.00069) | 15.99 MB | 8xH100 SXM | ~115s eval
+
+## Results (3-seed)
+
+| Seed | BPB | val_loss (nats) | Artifact |
+|------|-----|-----------------|----------|
+| 1337 | **1.08414** | 2.80045 | 15,985,531 |
+| 42 | **1.08424** | 2.80070 | 15,989,295 |
+| 2025 | **1.08538** | 2.80365 | 15,986,932 |
+| **Mean** | **1.08459** | **2.80160** | |
+
+Merged SOTA (PR #1019, 3-seed mean): **2.88218 nats** (1.1147 BPB). This run: **2.80160 nats**. Delta: **-0.0806 nats**. Clears the 0.005-nat threshold.
+
+## Changes from Base (PR #1394)
+
+### 1. Parallel Residuals (from layer 7)
+Layers 7-10 execute attention and MLP in parallel (PaLM-style) instead of sequential. The normalized input feeds both branches simultaneously, with learned per-channel scales (`attn_scale`, `mlp_scale`) controlling the contribution of each. Zero additional parameters beyond the existing scale vectors. Nearest PR: #1334 (parallel residuals on SP4096). Different: applied to SP8192 stack with depth recurrence, where the parallel execution interacts with the looped layers 4-5 differently than on SP4096.
+
+### 2. Coprime-Stride Data Loader
+Replaces standard sequential shard traversal with coprime-stride ordering. For each shard, a stride coprime to the number of sequences is selected, ensuring all sequences are visited exactly once in a pseudo-random order without repetition. This provides better data diversity within each epoch without additional compute cost. Not present in any SP8192 submission.
+
+### Architecture
+- SP8192 vocabulary (8192 BPE tokens via SentencePiece)
+- 11 transformer layers, dim 512, MLP 4x, 8 heads / 4 KV heads (GQA)
+- Depth recurrence: layers 4-5 looped 2x (effective 13 layers)
+- XSA-all (exclusive self-attention on all 11 layers)
+- Skip gates, RMSNorm, LeakyReLU(0.5)^2 activation
+- MuonEq-R optimizer (row-normalized Newton-Schulz)
+- GPTQ int6 weights + int8 embeddings + brotli compression
+- SDClip (std-dev based quantization clipping)
+- EMA (decay 0.997)
+
+### Compression
+- Code: lzma+base85 self-extracting (43KB -> 15.8KB)
+- Model: GPTQ int6 + brotli-11 (~15.97MB)
+- Total artifact: ~15.99MB (under 16MB limit)
+
+## Compliance
+- All techniques are training-side architecture changes. No eval-time adaptation.
+- No SLOT, no TTT, no n-gram caches.
+- Eval uses `torch.inference_mode()` for scoring. Model weights frozen at eval time.
+- GPTQ calibration uses AR self-generated training data (not validation data).
+- Sliding window evaluation with stride 64, standard BPB calculation.
+
+## Reproduction
+
+```bash
+pip install brotli
+pip install flash_attn_3 --no-deps --find-links https://windreamer.github.io/flash-attention3-wheels/cu128_torch291/
+torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+No env vars needed. Code defaults are the submission config. SP8192 data downloads automatically from `kevclark/parameter-golf` on first run.
+
+## Credits
+Base: PR #1394 (@clarkkev) — SP8192 + Depth Recurrence + MuonEq-R + SDClip + GPTQ int6.
+Parallel residuals pattern: PR #1334 (@aryanbhosale) — first demonstrated on SP4096.
diff --git a/records/track_10min_16mb/2026-04-06_SP8192_ParallelResiduals_CoprimeStride/requirements.txt b/records/track_10min_16mb/2026-04-06_SP8192_ParallelResiduals_CoprimeStride/requirements.txt
@@ -0,0 +1 @@
+brotli
diff --git a/records/track_10min_16mb/2026-04-06_SP8192_ParallelResiduals_CoprimeStride/submission.json b/records/track_10min_16mb/2026-04-06_SP8192_ParallelResiduals_CoprimeStride/submission.json
@@ -0,0 +1,15 @@
+{
+  "track": "10min_16mb",
+  "val_bpb_mean": 1.08459,
+  "val_bpb_std": 0.00069,
+  "seeds": [1337, 42, 2025],
+  "results": {
+    "1337": {"val_bpb": 1.08414061, "val_loss": 2.80044779, "bytes_total": 15985531},
+    "42": {"val_bpb": 1.08423848, "val_loss": 2.80070059, "bytes_total": 15989295},
+    "2025": {"val_bpb": 1.08538096, "val_loss": 2.80365174, "bytes_total": 15986932}
+  },
+  "base_pr": 1394,
+  "hardware": "8xH100 SXM",
+  "training_time_seconds": 588,
+  "eval_method": "sliding_window"
+}
diff --git a/records/track_10min_16mb/2026-04-06_SP8192_ParallelResiduals_CoprimeStride/train_gpt.py b/records/track_10min_16mb/2026-04-06_SP8192_ParallelResiduals_CoprimeStride/train_gpt.py
@@ -0,0 +1,2 @@
+# VOCAB_SIZE, 8192  # for evaluate.py auto-detection
+import 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		# VOCAB_SIZE, 8192 # for evaluate.py auto-detection
		import 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