Record: Seed-Regenerated Random Model + Incremental N-gram Cache — val_bpb 0.0905

records/track_10min_16mb/2026-03-29_SeedRegen_IncrementalNgram/README.md

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+# Seed-Regenerated Random Model + Incremental N-gram Cache — val_bpb 0.0905
+
+**val_bpb = 0.0905** (1 seed, additional seeds pending H100 access) | **15.09 MB** | 8×H100 SXM
+
+## Results (8×H100 80GB SXM, PyTorch 2.7.1)
+
+| Seed | step_avg | steps | neural_bpb | blended_bpb | Artifact |
+|------|----------|-------|------------|-------------|----------|
+| 1337 | 67ms | 9,912 | 1.503 | **0.0905** | 15,093,968 |
+| 42 | — | — | — | — | pending |
+| 2025 | — | — | — | — | pending |
+
+> **Note**: Additional seeds pending H100 access.
+
+## Key Innovation: Seed-Regenerated Weights
+
+All weight matrices in the transformer blocks (Q, K, V, O-proj, MLP-up, MLP-down) use **frozen orthogonal random projections** regenerated from deterministic seeds at load time. The artifact stores only:
+
+- **LoRA adapters** (rank-64 A and B matrices): ~3.9 MB at INT8
+- **Embedding + control tensors**: ~1.0 MB at FP16
+- **N-gram cache** (INT16 counts, LZMA compressed): ~10.7 MB
+- **Code**: ~0.1 MB
+
+The random base weights cost **0 bytes** in the artifact — they are regenerated from 8-byte seeds per matrix via QR-decomposed orthogonal initialization.
+
+### Why Orthogonal (not Gaussian)
+
+Prior work (PR #874) used Gaussian random bases but could not train models deeper than 5 layers — gradients vanish through deep stacks of random projections. Our **orthogonal initialization via QR decomposition** preserves singular values at exactly 1.0, enabling stable training of 11-layer random models (though we use 5L here for throughput).
+
+```python
+@staticmethod
+def _generate_orthogonal_base(seed, rows, cols):
+    g = torch.Generator(device='cpu')
+    g.manual_seed(seed)
+    size = max(rows, cols)
+    raw = torch.randn(size, size, generator=g)
+    Q, _ = torch.linalg.qr(raw)
+    return Q[:rows, :cols] / math.sqrt(cols)
+```
+
+### Adapter Quantization: Nearly Lossless
+
+The LoRA adapters are quantized with simple per-row INT8 (no GPTQ needed). The quantization gap is only **+0.003 BPB** — dramatically better than INT6 GPTQ on full weight matrices (+0.006 for the baseline).
+
+## N-gram Cache: Incremental Build During Training
+
+The n-gram cache is built **incrementally during training** with zero overhead:
+
+```python
+# After each training microstep (cost: <1ms per call):
+ngram_counter.update_batch_fast(full_seq.cpu().numpy().astype(np.int32))
+```
+
+- **Orders**: 2-7 (hash-bucketed count tables)
+- **Counts**: INT16 (uint16), clipped to 65535
+- **Total counts**: 31.1 billion (from 9,912 steps × 524K tokens × 8 GPUs)
+- **Multi-GPU sync**: `dist.all_reduce(SUM)` across 8 GPUs before serialization
+- **Compression**: LZMA preset 9 → 10.7 MB
+
+At eval time, the cache is **frozen** — no TTT, no eval-time updates. Entropy-adaptive alpha blending:
+```
+alpha = min(alpha_max, log1p(count) / 10)
+P_blend = alpha * P_ngram + (1 - alpha) * P_neural
+```
+
+### Why Incremental > Pre-fill
+
+We tested pre-filling the cache from training shards at startup. This was **10× worse** (0.996 BPB vs 0.0905) because:
+1. Pre-fill consumed 24-33% of the training budget (650-880s for 10 shards)
+2. Numpy hash computation on 50M-token shards was catastrophically slow
+3. Only covered 10/80 shards vs incremental seeing ALL training tokens
+
+## Architecture
+
+| Parameter | Value |
+|-----------|-------|
+| Layers | 5 |
+| Model dim | 512 |
+| Heads / KV heads | 8 / 4 |
+| MLP multiplier | 3.0 (hidden=1536) |
+| Activation | LeakyReLU(0.5)² |
+| Adapter rank | 64 |
+| Random init | Orthogonal (QR decomposition) |
+| Vocab | 1024 BPE |
+| Sequence length | 2048 |
+
+## Training
+
+| Parameter | Value |
+|-----------|-------|
+| Optimizer (adapters) | Muon (NS5, momentum 0.99) |
+| Optimizer (embed/scalar) | AdamW |
+| Matrix LR | 0.04 |
+| Grad clip norm | 0.1 |
+| Weight decay | 0.04 |
+| Batch tokens | 524,288 |
+| EMA decay | 0.997 |
+
+## Ablation
+
+| Config | BPB | Notes |
+|--------|-----|-------|
+| Neural only (post-quant) | 1.503 | Adapter INT8, no cache |
+| Neural sliding window | 1.474 | stride=64 |
+| **Neural + n-gram blend** | **0.0905** | Entropy-adaptive alpha, frozen cache |
+| Improvement from cache | -1.413 | |
+
+## Artifact Budget
+
+```
+Neural model (INT8 adapters + FP16 embed):   4,401,588 bytes
+N-gram cache (INT16 counts, LZMA):          10,692,380 bytes
+Total:                                      15,093,968 bytes
+Remaining:                                     906,032 bytes
+```
+
+## Credits
+
+- PR #874 (@fielding) — Random linear maps concept
+- PR #931 (@AnirudhRahul) — Packed n-gram artifact approach
+- arXiv:2407.00957 — Expressivity with random weights and learned biases
+- PR #549 (@abaybektursun) — LeakyReLU² activation, score-first TTT protocol

records/track_10min_16mb/2026-03-29_SeedRegen_IncrementalNgram/submission.json

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+{
+  "name": "Seed-Regenerated Random Model + Incremental N-gram Cache",
+  "val_bpb": 0.0905,
+  "bytes_total": 15093968,
+  "blurb": "5L 512d model with ALL weight matrices as seeded orthogonal random projections (0 bytes in artifact) + rank-64 LoRA adapters (3.9 MB). The remaining 11 MB holds an incrementally-built INT16 n-gram cache (orders 2-7, 31B counts from 8-GPU all-reduce). The neural model achieves 1.50 BPB; entropy-adaptive blending with the n-gram cache yields 0.0905 BPB. No eval-time adaptation — the cache is frozen after training.",
+  "author": "Vilhelm Toivonen",
+  "github_id": "vimeto",
+  "date": "2026-03-29"
+}