diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/README.md b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/README.md
new file mode 100644
index 0000000000..e62dca5ff8
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/README.md
@@ -0,0 +1,261 @@
+# Non-record: Quinary quantization + SP16384 + per-group lrzip + TTT - bpb 1.1384
+
+**Quinary {-2,-1,0,+1,+2} weights (5-state, base-5 packed) + 10L (5 Encoder + 5 Decoder) 576d U-Net + Muon + 4× relu² MLP + Tied Embed (380→576) + Poly5 Softcap + YaRN 2048 + SP16384 BPE + FP8 QAT + 5-bit Scale Quant + Layout-Aware Per-Stream Archive + Score-First TTT (3 epochs, fp16-calibration-only)**
+
+**bpb 1.1384 ± 0.0009 std** (3-seed TTT mean) | **15.72 MB** total artifact max (all 3 seeds FIT) | 8×H100 SXM, 7,800 steps in 599s + ~3.6 min TTT-eval
+
+## Results (3 seeds, 8×H100 SXM)
+
+| Seed | TTT BPB    | RT BPB | Total bytes |
+|------|------------|--------|-------------|
+| 42   | **1.1381** | 1.1626 | 15,714,938  |
+| 1337 | **1.1394** | 1.1633 | 15,721,124  |
+| 7    | **1.1378** | 1.1622 | 15,724,839  |
+| **Mean ± std** | **1.1384 ± 0.0009** | 1.1627 ± 0.0006 |  |
+
+## Motivation
+
+Quinary {-2,-1,0,+1,+2} is one step above ternary {-1,0,+1}: 5 levels per parameter instead of 3. Per-symbol entropy floor: log₂5 ≈ 2.32 bits/param vs log₂3 ≈ 1.59. Fewer parameters fit in 16 MB, but each is finer-grained. We ran the comparison to see which side wins at this budget.
+
+Beyond the formal comparison, there was the sport of it: how far can the extreme-quantization regime actually be pushed under the restrictions of the competition (16 MB total artifact, 10-min training, 10-min eval on 8×H100 SXM)? The current leaderboard frontier has drifted far away from it (PR #1855, PR #1851, et al. use int6 + LQER rank-4 corrections rather than ternary/binary), and quinary is the smallest step away from ternary that still stays squarely inside the discrete regime. We wanted to test whether that smallest step buys enough to be worth taking.
+
+Empirically: chunked roundtrip BPB drops from **1.1842** (ternary record, PR #640) to **1.1627** (this submission, 3-seed mean) — a **−0.022 bpb** architectural win — and score-first TTT on the calibration parameters adds another **−0.024 bpb** on top, landing at **1.1384**.
+
+## Diff from the ternary record (PR #640)
+
+This submission is a direct quinary fork of [@CiprianFlorin-Ifrim's ternary record](https://github.com/openai/parameter-golf/pull/640). Everything not listed in the table below was inherited unchanged: U-Net topology with per-block residual mix and learned skip weights, 4× relu² MLP, Muon (3 NS steps), factored tied embedding, polynomial-5 softcap with z-loss, YaRN (max_len=2048, base=5000), fused QKV, FP8 QAT for non-quantized linears, FlashAttention-3, 599s wallclock cap.
+
+| | Ternary record | This submission |
+|---|---|---|
+| Quantization | ternary {-1,0,+1}, log₂3 ≈ 1.585 bpw entropy floor | quinary {-2,-1,0,+1,+2}, log₂5 ≈ 2.322 bpw entropy floor; raw storage ≈ 2.667 bits/param via 3 quins per byte (post-archive cost depends on the entropy coder) |
+| Group size | 128 | 192 (3 groups per c_qkv row) |
+| Scale storage | fp16 | 5-bit log-delta scale quant (−141 KB, +2.1 mBPB TTT cost) |
+| Tokenizer | SP8192 | SP16384 |
+| Model dim | 768 | 576 |
+| GQA (Q:KV) | 8:4 | 6:3 |
+| Embed bottleneck | 254 | 380 |
+| Compression | single-blob LZMA | layout-aware per-stream v2 archive (header `0x03`) |
+| Eval | stride-16 sliding + temperature scaling | score-first TTT (3 epochs, fp16 calibration params, 42,364 ≈ 0.08% of model) |
+| Param count | 73.7M | 52.8M |
+
+## Architecture (config)
+
+| | |
+|---|---|
+| Layers | 10 (5 encoder + 5 decoder, symmetric U-Net) |
+| Model dim | 576 |
+| Heads | 6 query / 3 KV (GQA), head_dim=96 |
+| MLP | 4× expansion, hidden=2304, relu² activation |
+| Embed | tied, 16384 vocab, 380→576 bottleneck |
+| RoPE | YaRN, base=5000, max_len=2048 |
+| Softcap | poly5, cap=10 |
+| Quinary group size | 192, per-group absmean (`scale_correction` exists per group but is inert by design — see Score-first TTT) |
+| Optimizer | Muon (matrix params), Adam (scalars + tied embed) |
+| Batch / seq | 524 288 tok / 1024 |
+| Wallclock cap | 599 s |
+
+## Tokenizer (SP16384)
+
+Custom-trained SentencePiece BPE with `vocab_size=16384`, trained from scratch on FineWeb-10B using the upstream [`data/download_hf_docs_and_tokenize.py`](https://github.com/openai/parameter-golf/blob/main/data/download_hf_docs_and_tokenize.py) pipeline (a thin wrapper around `sentencepiece` BPE training). The tokenizer model + pre-tokenized shards are published on the HF dataset repo [`deniskurlov/parameter-golf-fineweb-sp16384`](https://huggingface.co/datasets/deniskurlov/parameter-golf-fineweb-sp16384); `setup.sh` pulls the `canonical/` subset (~23 GB total — 117 train shards + 1 val shard + the tokenizer `.model`/`.vocab`) and that is sufficient to reproduce the run. The competition's 16 MB artifact cap applies only to the model bundle; the tokenizer + tokenized shards are pre-published infrastructure that any reproducer downloads once.
+
+Doubling the vocab vs the ternary record's SP8192 reduces tokens-per-byte from ~0.30 to ~0.246 (fewer cross-entropy terms in the BPB sum) at the cost of a 2× larger embedding matrix — bounded here by the 380→576 factored-tied-embedding bottleneck.
+
+## Per-stream compression (header byte `0x03`)
+
+For each quinary tensor, two-stage choose-min:
+
+1. **Layout selection** (LZMA-screened): generate 4 candidate byte-layouts and pick the layout with the smallest LZMA9-compressed size.
+   - **base5** (canonical, 3 symbols/byte) and **base5_T** (transpose, then base-5 pack — wins when columns are more locally similar than rows, common on MLP projections).
+   - **bitmask** = three bit-planes: `zero_mask | sign_bits[over nonzeros] | mag2_bits[over nonzeros]`, giving the entropy coder homogeneous planes to model independently. Plus **bitmask_T**.
+2. **Compressor selection**: compress the winning layout with both `lzma9` and `lrzip-zpaq -L9`; keep whichever is smaller.
+
+(LZMA-screen rather than full 4×2 keeps serialize wallclock bounded — `lrzip` can be slow on bad streams; we only invoke it on the chosen layout.)
+
+For `c_qkv.weight`, the row block is split into independent Q / K / V sub-payloads first (Q, K, V have different trained weight distributions, so optimal layouts differ per part). The legacy single-blob lrzip path was the source of the seed-7 OVER cliff (~33% of seeds went OVER 16 MB); per-stream v2 FITS at ~15.65 MB (model only) / ~15.72 MB (model+code) across all 3 seeds we tried.
+
+## Score-first TTT
+
+After loading the artifact, freeze all quantized and FP8 weights. Adapt only the **fp16 calibration parameters** — `attn_scale`, `mlp_scale`, `resid_mix`, `q_gain`, `skip_weights`, `vocab_bias` — **42,364 values, ≈0.08% of the model**. Process the val stream in 1,134 chunks of 32k tokens with sliding-window stride=16; for each chunk, **grade tokens first, then train** (legal under the rules). 3 SGD epochs, lr=0.005, momentum 0.9. Eval time ~215 s of the 600 s eval budget. (These params are tagged `CTP` in the code — a holdover constant name from the ternary base.)
+
+> **Note on `scale_correction`:** the inherited `QuinaryLinear` carries a per-group fp32 `scale_correction` parameter (~190k values across all quinary layers, init=1.0). Its gradient is blocked by the STE detach in the forward pass, so it never updates from 1.0 in either training or TTT. We tested fixing the STE on 2026-05-01 (one seed): no TTT benefit, ~2 mBPB training-side regression. Reverted. The parameter is therefore inert by design; it remains in the state-dict at value 1.0 (compresses to a few KB) and is excluded from the TTT optimizer's adapted-parameter set.
+
+### Validation accounting
+
+The validation manifest has **37,147,047** token IDs, hence **37,147,046** possible next-token target positions. The eval loop truncates the stream to a multiple of `train_seq_len=1024`, scoring **37,146,624** target tokens — the final partial 1024-window (the trailing **422** target tokens) is omitted. From the `verify_bpb.py` output:
+
+| | targets | bytes |
+|---|---:|---:|
+| Full untruncated stream | 37,147,046 | 151,080,891 |
+| Eval-scored slice (`(N-1)//1024 × 1024`) | 37,146,624 | 151,078,879 |
+| Dropped tail (last partial window) | 422 | 2,012 |
+
+The dropped fraction of the byte denominator is **2,012 / 151,080,891 ≈ 1.33 × 10^{-5}**.
+
+Bound on the resulting BPB bias: BPB = NLL / byte_count, with both numerator and denominator scaling proportionally with token count, so the bias only comes from any *deviation* of the dropped-tail's average loss from the global average. For a 422-token sample of 37M, that worst-plausible deviation gives **|ΔBPB| ≲ 2 × 10^{-5} BPB** — well below the current 3-seed std of **0.0009 BPB** and far below the 0.005 BPB record-submission improvement threshold. The bias is also identical across all 3 seeds (same val stream, same truncation), so seed-to-seed variance is unaffected; only absolute BPB carries the constant offset.
+
+Fresh logs report `eval_tokens` and `eval_bytes` after both the roundtrip eval and TTT eval so the actual scored slice is auditable line-by-line, and `verify_bpb.py`'s `exact eval slice` `lut_bytes` cross-checks the `eval_bytes:N` value `train_gpt.py` writes at runtime.
+
+### Verifying the BPB denominator (custom tokenizer)
+
+Because we use a custom-trained SentencePiece BPE (SP16384), the per-token byte LUTs in `build_luts` are reviewer-auditable. Run:
+
+```bash
+python3 verify_bpb.py
+```
+
+The script independently rebuilds the `(base_bytes, has_leading_space, is_boundary_token)` LUTs from the `.model` file using the **same shard loader as `train_gpt.py`** (256-int32 / 1024-byte header, magic `20240520`, version `1`), then for each of several stream slices it compares **two byte counts**:
+
+- the LUT-based sum used by `eval_val` and TTT eval (`base_bytes_lut[tgt] + has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]`);
+- the SentencePiece decoder's UTF-8 byte count for the same tokens, with documents decoded BOS-by-BOS so the LUT's "no leading-space charge after a boundary" rule and the decoder's "no synthetic leading space at sequence start" behavior are aligned.
+
+The slices checked are: **the exact slice scored by `eval_val`** (truncated to a `train_seq_len=1024` target multiple — this is the slice whose byte count drives the headline BPB), the full untruncated stream, and BOS-delimited document prefixes (single doc, first 10/100/1000 docs). The script also reports the tokenizer's SHA-256 for provenance and counts UNK tokens (must be 0 — the LUT assigns 0 bytes to UNK, which would silently inflate BPB).
+
+If every slice reports `delta = +0`, the LUT denominator matches SentencePiece decoding on the scored tokenized validation slice — which means BPB reduces to `cross_entropy_in_nats / (decoded_bytes × ln 2)`. **Note**: this proves *internal consistency* between the LUT and the SP decoder for the supplied tokenizer + supplied tokenized shards. It does not by itself prove that the shards correspond to the original canonical FineWeb validation bytes under the intended tokenizer — that is a separate provenance question, addressed by the tokenizer SHA-256 line below and by hosting the tokenized shards at the public HF repo `deniskurlov/parameter-golf-fineweb-sp16384` so reviewers can compare against an independent retokenization.
+
+**Verified output, on the canonical sp16384 stack (2026-05-01)**:
+
+```
+tokenizer    : data/canonical/tokenizers/fineweb_16384_bpe.model (vocab=16384)
+tokenizer sha: abaec140336563026d65c1b7192d47a2b8c81a3bbad0f4d1cd1d852364ac432a
+BOS id=1 ('<s>')  EOS id=2 ('</s>')  UNK id=3
+val shards   : 1 (data/canonical/datasets/fineweb10B_sp16384/fineweb_val_000000.bin ... )
+LUT stats    : byte-fallback=256  control/unknown/unused=4  with-leading-space=11664  boundary=4
+shard tokens : 37,147,047
+BOS positions: 50,000; first=0
+UNK count    : 0
+
+eval slice   : train_seq_len=1024 target_count=37,146,624 omitted_tail_targets=422
+  PASS  exact eval slice                     targets=37,146,624 lut_bytes= 151,078,879 decoded_bytes= 151,078,879 delta=+0 start_boundary=True
+  PASS  full untruncated stream              targets=37,147,046 lut_bytes= 151,080,891 decoded_bytes= 151,080,891 delta=+0 start_boundary=True
+  PASS  doc at first BOS                     targets=       295 lut_bytes=       1,339 decoded_bytes=       1,339 delta=+0 start_boundary=True
+  PASS  first 10 BOS docs                    targets=     5,592 lut_bytes=      22,919 decoded_bytes=      22,919 delta=+0 start_boundary=True
+  PASS  first 100 BOS docs                   targets=    72,484 lut_bytes=     291,960 decoded_bytes=     291,960 delta=+0 start_boundary=True
+  PASS  first 1000 BOS docs                  targets=   706,682 lut_bytes=   2,851,493 decoded_bytes=   2,851,493 delta=+0 start_boundary=True
+
+ALL CHECKS PASS — LUT bytes match SentencePiece decoder bytes on the eval slice.
+```
+
+End-to-end audit cross-check: the verifier's `exact eval slice lut_bytes = 151,078,879` is **bit-identical** to the runtime `eval_bytes:151,078,879` printed by `train_gpt.py:eval_val` and TTT eval for every seed. Cross-tokenizer sanity: rerunning the same verifier against the upstream openai/parameter-golf SP1024 stack on the same FineWeb val source gives identical byte counts of `1,339 / 22,919 / 291,960 / 2,851,493` for the first 1/10/100/1000 documents (different tokenizer, same source bytes — the decoder agrees).
+
+## Setup and Run
+
+```bash
+# Environment setup (lrzip + Python deps + FlashAttention-3 + dataset)
+bash setup.sh
+
+# Single seed
+SEED=42 bash run.sh
+
+# 3-seed sweep
+for SEED in 42 1337 7; do
+  RUN_ID=quinary_seed${SEED} SEED=$SEED bash run.sh
+done
+```
+
+<details>
+<summary>Representative run command (subset of <code>run.sh</code> env vars — see <code>run.sh</code> for the authoritative full set)</summary>
+
+The block below mirrors the env vars `run.sh` actually passes (model shape, optimizer, TTT, etc.). A few minor knobs that `run.sh` also passes through (`ADAM_LR`, `ADAM_WD`, `BATCH_TOKENS_START`, `BATCH_SCHEDULE_FRACTION`, `SEQ_LEN_START`, `SEQ_SCHEDULE_FRACTION`, `VAL_LOSS_EVERY`, `TRAIN_LOG_EVERY`, `CHURN_LOG_EVERY`, `VAL_MAX_TOKENS`) are not duplicated here — `run.sh` is the authoritative source. As of Tier-4, all defaults in `train_gpt.py:Hyperparameters` also match the canonical SP16384 config, so a bare `torchrun --standalone --nproc_per_node=8 train_gpt.py` (no env vars) reproduces the submission.
+
+```bash
+RUN_ID=quinary_seed42 \
+DATA_PATH=./data/canonical/datasets/fineweb10B_sp16384 \
+TOKENIZER_PATH=./data/canonical/tokenizers/fineweb_16384_bpe.model \
+VOCAB_SIZE=16384 \
+BITNET_GROUP_SIZE=192 \
+EMBED_DIM=380 \
+NUM_LAYERS=10 \
+MODEL_DIM=576 \
+NUM_KV_HEADS=3 \
+NUM_HEADS=6 \
+MLP_MULT=4 \
+MATRIX_OPTIMIZER=muon \
+MUON_BACKEND_STEPS=3 \
+MUON_MOMENTUM=0.95 \
+MUON_MOMENTUM_WARMUP_START=0.85 \
+MUON_MOMENTUM_WARMUP_STEPS=500 \
+MUON_WD=0.0 \
+MATRIX_LR=0.035 \
+SCALAR_LR=0.02 \
+TIED_EMBED_LR=0.02 \
+WARMDOWN_FRACTION=0.2 \
+LOGIT_SOFTCAP=10 \
+QK_GAIN_INIT=5.0 \
+ROPE_TYPE=yarn \
+YARN_MAX_LEN=2048 \
+ROPE_BASE=5000 \
+TRAIN_BATCH_TOKENS=524288 \
+TRAIN_SEQ_LEN=1024 \
+ITERATIONS=10000 \
+WARMUP_STEPS=5 \
+MAX_WALLCLOCK_SECONDS=599 \
+TIE_EMBEDDINGS=1 \
+HEAD_LR=0.02 \
+ACTIVATION=relu2 \
+SOFTCAP_TYPE=poly \
+TTT_STEPS=3 \
+TTT_LR=0.005 \
+TTT_TOKENS=32768 \
+SCALE_QUANT_BITS=5 \
+SEED=42 \
+COMPILE_MODE=default \
+OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+</details>
+
+## File Layout
+
+```
+.
+├── README.md                          # this file
+├── submission.json                    # OpenAI-format submission metadata
+├── setup.sh                           # apt + pip + FlashAttention-3 + HF dataset download
+├── run.sh                             # canonical training+eval entry point
+├── train_gpt.py                       # complete training, compression, and TTT pipeline
+├── verify_bpb.py                      # standalone reviewer-runnable BPB-LUT check
+├── requirements.txt                   # Python dependency pin
+├── fineweb_16384_bpe.model            # bundled tokenizer (sha256 abaec140…), provenance copy
+├── fineweb_16384_bpe.vocab            # bundled vocab (provenance copy)
+├── quinary_seed42.txt                 # 3-seed training/TTT logs (one per seed)
+├── quinary_seed1337.txt
+└── quinary_seed7.txt
+```
+
+The bundled `fineweb_16384_bpe.{model,vocab}` files are inspection / provenance copies — they should be byte-identical to what `setup.sh` downloads from `deniskurlov/parameter-golf-fineweb-sp16384` into `./data/canonical/tokenizers/`. Tokenizer files are *data*, not code, so they don't count toward the 16 MB cap (`bytes_code + compressed_model_bytes`); their SHA-256 (`abaec140336563026d65c1b7192d47a2b8c81a3bbad0f4d1cd1d852364ac432a`) is also reported by `verify_bpb.py` for cross-checking.
+
+## Compliance
+
+- [x] Artifact ≤ 16,000,000 bytes (15,724,839 — max across the 3 verified seeds, seed=7; per-seed values in the Results table; margin = 275,161 bytes)
+- [x] Training ≤ 10 minutes (599,436–599,772 ms wallclock across 3 seeds)
+- [x] Evaluation ≤ 10 minutes (TTT eval 212,250–215,427 ms; non-TTT roundtrip ~80 s)
+- [x] Score-first TTT (CTP params only adapt on tokens *already* graded)
+- [x] No network calls during evaluation
+- [x] No external compute
+- [x] No access to validation data during training. Validation shards are loaded into memory at startup but are consumed only by the post-training eval / TTT functions; under the canonical run `VAL_LOSS_EVERY=0`, so no validation tokens enter gradient updates before they have been scored under the score-first TTT pattern.
+- [x] Reproducibly runs end-to-end from `bash setup.sh && bash run.sh` on a fresh 8×H100 SXM pod
+- [x] BPB byte-count LUTs match the SentencePiece decoder's UTF-8 output exactly on the slice scored by `eval_val` (`exact eval slice` check) and on BOS-aligned document slices (run `python3 verify_bpb.py` to reproduce; see "Verifying the BPB denominator" above)
+
+## Our contribution
+
+All adaptations to the quinary case are ours. Concretely:
+
+- **Quinary `QuinaryLinear`** — 5-level absmean-scaled STE quantization (`clamp(-2, 2)`) replacing the ternary `clamp(-1, 1)`. Per-group (192) absmean scaling. The inherited `scale_correction` per-group multiplier is kept in the state-dict for backwards-compatibility but is inert by design (see Score-first TTT note).
+- **Base-5 packing** — three quins per byte (max symbol value 4·1 + 4·5 + 4·25 = 124, fits in `uint8`), with a paired unpacker. **Raw storage 8/3 ≈ 2.667 bits/param**; the entropy floor for 5 equiprobable symbols is log₂5 ≈ 2.322 bits/param, so the raw packing leaves ~0.34 bits/param of headroom for the downstream entropy coder. Ternary base-3 packing is the analogous 5/3 ≈ 1.667 raw bits / log₂3 ≈ 1.585 floor.
+- **{-2,-1,0,+1,+2} bitmask plane decomposition** — alternative encoding as three concatenated bit-planes: `zero_mask | sign_bits[over nonzeros] | mag2_bits[over nonzeros]`. Each plane has homogeneous bit statistics so the entropy coder models them independently rather than fighting a multimodal mixture.
+- **LZMA-screened layout selection** — for every quinary tensor, materialize all 4 layouts (`base5`, `base5_T`, `bitmask`, `bitmask_T`), screen them by LZMA9-compressed size, then run LZMA9 vs lrzip-zpaq only on the selected layout. Bounded-cost heuristic with an LZMA floor — *not* an exhaustive 4×2 search; in principle could miss a (layout, lrzip) pair that beats (best-LZMA-layout, lrzip), but in practice the LZMA floor caps the worst case at the canonical base5+LZMA encoding.
+- **5-bit log-delta scale quantization** — per-group fp16 scales replaced with anchor + 5-bit log-delta; saves ~141 KB at +2.1 mBPB TTT cost. Net Pareto-positive.
+- **Quinary fork of the architecture itself** — the rebalancing of `model_dim` (768→576), GQA ratio (8:4 → 6:3), and embedding bottleneck (254 → 380) for the higher per-param bit cost of quinary, plus the SP8192→SP16384 tokenizer choice and `group_size` 128→192 to fit the new model dim cleanly.
+
+Empirical effect of the bundle is in the Diff and Results tables above. The seed-7 OVER cliff (single-blob lrzip OVERed on ~33% of seeds, including seed=7 at 17.23 MB) goes away under the per-stream v2 archive — all 3 seeds we tried FIT at 15.71–15.73 MB total.
+
+Note: with the v2 archive, seed=7 is now actually the **best**-fitting and best-scoring seed (TTT BPB = 1.1378), not the worst — the prior cliff was an artifact of single-blob compression interacting with seed=7's specific weight distribution, fully absorbed by the per-stream layout choose-min.
+
+## Acknowledgements
+
+This submission stands on others' work. The architectural foundation, the compression pipeline's core ideas, and the score-first TTT pattern are all upstream of us; see "Our contribution" above for the quinary-specific extensions.
+
+- **@CiprianFlorin-Ifrim** — [PR #640: Ternary U-Net record (1.1570 BPB)](https://github.com/openai/parameter-golf/pull/640) — architectural base. 
+Inherited: the U-Net topology (5+5 enc/dec with learned skip weights and per-block residual mix), Muon optimizer settings, FP8 QAT for non-quantized linears, factored tied embedding (with a narrower bottleneck here), polynomial-5 softcap with z-loss, YaRN positional encoding, fused QKV projection, and FlashAttention-3 wiring. 
+- **@codemath3000** — [PR #1855: per-group lrzip+brotli compression pipeline (1.06108 BPB)](https://github.com/openai/parameter-golf/pull/1855) — compression pipeline core ideas. Inherited: splitting the artifact into multiple compressed payloads instead of one monolithic blob, treating per-tensor byte layout as an optimization axis, and using lrzip's ZPAQ back-end for long-range cross-payload deduplication.
+- **Score-first TTT lineage** — [@abaybektursun's PR #549](https://github.com/openai/parameter-golf/pull/549) and [@clarkkev's PR #1394 SP8192 stack](https://github.com/openai/parameter-golf/pull/1394) — origin of the legal "train only on tokens already graded" pattern.
\ No newline at end of file
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/fineweb_16384_bpe.model b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/fineweb_16384_bpe.model
new file mode 100644
index 0000000000..bd92a6a846
Binary files /dev/null and b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/fineweb_16384_bpe.model differ
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/fineweb_16384_bpe.vocab b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/fineweb_16384_bpe.vocab
new file mode 100644
index 0000000000..a56f9b51a9
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/fineweb_16384_bpe.vocab
@@ -0,0 +1,16384 @@
+<pad>	0
+<s>	0
+</s>	0
+<unk>	0
+<0x00>	0
+<0x01>	0
+<0x02>	0
+<0x03>	0
+<0x04>	0
+<0x05>	0
+<0x06>	0
+<0x07>	0
+<0x08>	0
+<0x09>	0
+<0x0A>	0
+<0x0B>	0
+<0x0C>	0
+<0x0D>	0
+<0x0E>	0
+<0x0F>	0
+<0x10>	0
+<0x11>	0
+<0x12>	0
+<0x13>	0
+<0x14>	0
+<0x15>	0
+<0x16>	0
+<0x17>	0
+<0x18>	0
+<0x19>	0
+<0x1A>	0
+<0x1B>	0
+<0x1C>	0
+<0x1D>	0
+<0x1E>	0
+<0x1F>	0
+<0x20>	0
+<0x21>	0
+<0x22>	0
+<0x23>	0
+<0x24>	0
+<0x25>	0
+<0x26>	0
+<0x27>	0
+<0x28>	0
+<0x29>	0
+<0x2A>	0
+<0x2B>	0
+<0x2C>	0
+<0x2D>	0
+<0x2E>	0
+<0x2F>	0
+<0x30>	0
+<0x31>	0
+<0x32>	0
+<0x33>	0
+<0x34>	0
+<0x35>	0
+<0x36>	0
+<0x37>	0
+<0x38>	0
+<0x39>	0
+<0x3A>	0
+<0x3B>	0
+<0x3C>	0
+<0x3D>	0
+<0x3E>	0
+<0x3F>	0
+<0x40>	0
+<0x41>	0
+<0x42>	0
+<0x43>	0
+<0x44>	0
+<0x45>	0
+<0x46>	0
+<0x47>	0
+<0x48>	0
+<0x49>	0
+<0x4A>	0
+<0x4B>	0
+<0x4C>	0
+<0x4D>	0
+<0x4E>	0
+<0x4F>	0
+<0x50>	0
+<0x51>	0
+<0x52>	0
+<0x53>	0
+<0x54>	0
+<0x55>	0
+<0x56>	0
+<0x57>	0
+<0x58>	0
+<0x59>	0
+<0x5A>	0
+<0x5B>	0
+<0x5C>	0
+<0x5D>	0
+<0x5E>	0
+<0x5F>	0
+<0x60>	0
+<0x61>	0
+<0x62>	0
+<0x63>	0
+<0x64>	0
+<0x65>	0
+<0x66>	0
+<0x67>	0
+<0x68>	0
+<0x69>	0
+<0x6A>	0
+<0x6B>	0
+<0x6C>	0
+<0x6D>	0
+<0x6E>	0
+<0x6F>	0
+<0x70>	0
+<0x71>	0
+<0x72>	0
+<0x73>	0
+<0x74>	0
+<0x75>	0
+<0x76>	0
+<0x77>	0
+<0x78>	0
+<0x79>	0
+<0x7A>	0
+<0x7B>	0
+<0x7C>	0
+<0x7D>	0
+<0x7E>	0
+<0x7F>	0
+<0x80>	0
+<0x81>	0
+<0x82>	0
+<0x83>	0
+<0x84>	0
+<0x85>	0
+<0x86>	0
+<0x87>	0
+<0x88>	0
+<0x89>	0
+<0x8A>	0
+<0x8B>	0
+<0x8C>	0
+<0x8D>	0
+<0x8E>	0
+<0x8F>	0
+<0x90>	0
+<0x91>	0
+<0x92>	0
+<0x93>	0
+<0x94>	0
+<0x95>	0
+<0x96>	0
+<0x97>	0
+<0x98>	0
+<0x99>	0
+<0x9A>	0
+<0x9B>	0
+<0x9C>	0
+<0x9D>	0
+<0x9E>	0
+<0x9F>	0
+<0xA0>	0
+<0xA1>	0
+<0xA2>	0
+<0xA3>	0
+<0xA4>	0
+<0xA5>	0
+<0xA6>	0
+<0xA7>	0
+<0xA8>	0
+<0xA9>	0
+<0xAA>	0
+<0xAB>	0
+<0xAC>	0
+<0xAD>	0
+<0xAE>	0
+<0xAF>	0
+<0xB0>	0
+<0xB1>	0
+<0xB2>	0
+<0xB3>	0
+<0xB4>	0
+<0xB5>	0
+<0xB6>	0
+<0xB7>	0
+<0xB8>	0
+<0xB9>	0
+<0xBA>	0
+<0xBB>	0
+<0xBC>	0
+<0xBD>	0
+<0xBE>	0
+<0xBF>	0
+<0xC0>	0
+<0xC1>	0
+<0xC2>	0
+<0xC3>	0
+<0xC4>	0
+<0xC5>	0
+<0xC6>	0
+<0xC7>	0
+<0xC8>	0
+<0xC9>	0
+<0xCA>	0
+<0xCB>	0
+<0xCC>	0
+<0xCD>	0
+<0xCE>	0
+<0xCF>	0
+<0xD0>	0
+<0xD1>	0
+<0xD2>	0
+<0xD3>	0
+<0xD4>	0
+<0xD5>	0
+<0xD6>	0
+<0xD7>	0
+<0xD8>	0
+<0xD9>	0
+<0xDA>	0
+<0xDB>	0
+<0xDC>	0
+<0xDD>	0
+<0xDE>	0
+<0xDF>	0
+<0xE0>	0
+<0xE1>	0
+<0xE2>	0
+<0xE3>	0
+<0xE4>	0
+<0xE5>	0
+<0xE6>	0
+<0xE7>	0
+<0xE8>	0
+<0xE9>	0
+<0xEA>	0
+<0xEB>	0
+<0xEC>	0
+<0xED>	0
+<0xEE>	0
+<0xEF>	0
+<0xF0>	0
+<0xF1>	0
+<0xF2>	0
+<0xF3>	0
+<0xF4>	0
+<0xF5>	0
+<0xF6>	0
+<0xF7>	0
+<0xF8>	0
+<0xF9>	0
+<0xFA>	0
+<0xFB>	0
+<0xFC>	0
+<0xFD>	0
+<0xFE>	0
+<0xFF>	0
+▁t	-0
+▁a	-1
+in	-2
+he	-3
+re	-4
+on	-5
+er	-6
+▁the	-7
+▁s	-8
+▁w	-9
+or	-10
+at	-11
+nd	-12
+ou	-13
+▁c	-14
+it	-15
+es	-16
+▁f	-17
+is	-18
+en	-19
+ing	-20
+▁b	-21
+▁p	-22
+▁o	-23
+an	-24
+ed	-25
+al	-26
+▁to	-27
+▁m	-28
+ar	-29
+▁and	-30
+▁in	-31
+▁of	-32
+▁d	-33
+le	-34
+ic	-35
+as	-36
+om	-37
+▁h	-38
+ion	-39
+▁th	-40
+il	-41
+▁T	-42
+ent	-43
+▁l	-44
+ve	-45
+▁y	-46
+ro	-47
+st	-48
+▁I	-49
+▁e	-50
+▁re	-51
+▁n	-52
+▁S	-53
+▁g	-54
+et	-55
+ct	-56
+▁A	-57
+▁you	-58
+▁C	-59
+ly	-60
+▁for	-61
+id	-62
+▁is	-63
+ay	-64
+▁on	-65
+▁be	-66
+ot	-67
+ow	-68
+ol	-69
+am	-70
+ce	-71
+ig	-72
+us	-73
+ad	-74
+im	-75
+▁M	-76
+ch	-77
+el	-78
+ver	-79
+ith	-80
+ut	-81
+▁st	-82
+ation	-83
+ur	-84
+▁P	-85
+▁with	-86
+▁that	-87
+ir	-88
+▁B	-89
+▁W	-90
+▁The	-91
+▁it	-92
+▁he	-93
+ra	-94
+ill	-95
+ers	-96
+▁al	-97
+un	-98
+ul	-99
+▁an	-100
+▁D	-101
+▁H	-102
+▁F	-103
+out	-104
+▁pro	-105
+▁as	-106
+▁wh	-107
+▁are	-108
+ke	-109
+se	-110
+ter	-111
+▁we	-112
+if	-113
+▁ha	-114
+ge	-115
+oo	-116
+▁R	-117
+our	-118
+pp	-119
+ck	-120
+ate	-121
+ess	-122
+▁at	-123
+▁con	-124
+▁com	-125
+▁or	-126
+▁L	-127
+est	-128
+her	-129
+ore	-130
+ment	-131
+▁fr	-132
+ab	-133
+igh	-134
+▁-	-135
+▁ne	-136
+▁N	-137
+ort	-138
+▁se	-139
+▁G	-140
+▁your	-141
+ld	-142
+▁E	-143
+ist	-144
+ri	-145
+op	-146
+▁(	-147
+▁ex	-148
+ity	-149
+ure	-150
+▁O	-151
+em	-152
+▁v	-153
+qu	-154
+ant	-155
+art	-156
+ive	-157
+ust	-158
+um	-159
+▁was	-160
+▁have	-161
+pe	-162
+▁from	-163
+▁this	-164
+▁de	-165
+▁r	-166
+▁sh	-167
+th	-168
+ain	-169
+ies	-170
+▁can	-171
+up	-172
+▁will	-173
+▁ch	-174
+and	-175
+▁by	-176
+os	-177
+ight	-178
+nt	-179
+ie	-180
+▁us	-181
+ome	-182
+all	-183
+ard	-184
+▁not	-185
+ud	-186
+res	-187
+▁le	-188
+▁J	-189
+ast	-190
+▁pl	-191
+ost	-192
+▁su	-193
+▁ab	-194
+iv	-195
+ear	-196
+▁wor	-197
+ide	-198
+ial	-199
+rou	-200
+▁all	-201
+gh	-202
+od	-203
+oc	-204
+ak	-205
+te	-206
+ine	-207
+ould	-208
+▁j	-209
+red	-210
+ag	-211
+▁has	-212
+..	-213
+ice	-214
+▁Th	-215
+ell	-216
+▁U	-217
+age	-218
+▁do	-219
+▁k	-220
+ack	-221
+fe	-222
+ook	-223
+ac	-224
+▁ad	-225
+per	-226
+▁In	-227
+ip	-228
+▁comp	-229
+ake	-230
+▁out	-231
+ions	-232
+ally	-233
+▁up	-234
+are	-235
+▁but	-236
+▁me	-237
+▁whe	-238
+pt	-239
+lo	-240
+ry	-241
+able	-242
+▁our	-243
+▁“	-244
+one	-245
+ind	-246
+▁en	-247
+▁more	-248
+ail	-249
+ite	-250
+ther	-251
+▁their	-252
+▁Y	-253
+ich	-254
+▁so	-255
+very	-256
+ime	-257
+cc	-258
+ood	-259
+ated	-260
+ong	-261
+▁K	-262
+▁my	-263
+▁sa	-264
+for	-265
+iz	-266
+ame	-267
+ber	-268
+▁they	-269
+▁St	-270
+▁te	-271
+so	-272
+ous	-273
+▁one	-274
+ans	-275
+act	-276
+▁about	-277
+ll	-278
+ike	-279
+du	-280
+▁cont	-281
+ase	-282
+og	-283
+▁V	-284
+▁im	-285
+ick	-286
+▁cl	-287
+ia	-288
+ance	-289
+▁work	-290
+▁inc	-291
+ign	-292
+▁un	-293
+ire	-294
+ree	-295
+▁off	-296
+▁fe	-297
+▁who	-298
+▁man	-299
+ue	-300
+ace	-301
+ach	-302
+reat	-303
+ub	-304
+▁It	-305
+ction	-306
+▁go	-307
+ne	-308
+▁app	-309
+▁year	-310
+▁new	-311
+ep	-312
+ult	-313
+ib	-314
+ap	-315
+▁his	-316
+ays	-317
+erv	-318
+▁Ch	-319
+▁We	-320
+▁res	-321
+und	-322
+▁"	-323
+▁sp	-324
+ass	-325
+ark	-326
+ations	-327
+ff	-328
+▁qu	-329
+ary	-330
+▁per	-331
+▁also	-332
+ile	-333
+▁which	-334
+▁int	-335
+▁time	-336
+ove	-337
+form	-338
+ven	-339
+ount	-340
+▁get	-341
+▁tr	-342
+own	-343
+▁like	-344
+▁some	-345
+▁other	-346
+ond	-347
+ents	-348
+ings	-349
+vel	-350
+▁any	-351
+ical	-352
+ence	-353
+▁part	-354
+av	-355
+▁been	-356
+▁dis	-357
+▁This	-358
+▁over	-359
+ition	-360
+ress	-361
+pl	-362
+ors	-363
+▁rec	-364
+▁them	-365
+▁He	-366
+▁sc	-367
+▁ar	-368
+ild	-369
+▁pe	-370
+port	-371
+ink	-372
+low	-373
+▁ag	-374
+▁ro	-375
+▁her	-376
+▁when	-377
+ound	-378
+▁kn	-379
+ord	-380
+mer	-381
+int	-382
+▁need	-383
+ish	-384
+▁pr	-385
+irst	-386
+ens	-387
+ough	-388
+▁said	-389
+ru	-390
+▁pre	-391
+▁spe	-392
+▁just	-393
+wn	-394
+ren	-395
+▁what	-396
+▁there	-397
+▁if	-398
+▁acc	-399
+▁than	-400
+▁its	-401
+ov	-402
+▁Re	-403
+day	-404
+vers	-405
+▁would	-406
+ater	-407
+fter	-408
+▁had	-409
+ade	-410
+ning	-411
+lud	-412
+▁hel	-413
+▁–	-414
+▁were	-415
+▁am	-416
+old	-417
+rough	-418
+▁into	-419
+▁des	-420
+ory	-421
+ople	-422
+itt	-423
+ang	-424
+▁help	-425
+▁tw	-426
+▁how	-427
+use	-428
+lic	-429
+ool	-430
+▁bec	-431
+▁add	-432
+anc	-433
+▁first	-434
+ose	-435
+▁make	-436
+▁comm	-437
+ons	-438
+amp	-439
+ob	-440
+hed	-441
+▁prov	-442
+▁Wh	-443
+▁tra	-444
+...	-445
+ft	-446
+▁look	-447
+▁You	-448
+▁includ	-449
+ual	-450
+▁people	-451
+les	-452
+▁serv	-453
+gr	-454
+▁col	-455
+ian	-456
+ments	-457
+ful	-458
+▁know	-459
+▁produ	-460
+ates	-461
+iew	-462
+▁Ne	-463
+▁em	-464
+rent	-465
+ious	-466
+tern	-467
+▁she	-468
+round	-469
+ek	-470
+▁every	-471
+▁through	-472
+▁may	-473
+ating	-474
+▁no	-475
+▁only	-476
+pport	-477
+▁back	-478
+▁most	-479
+ect	-480
+▁bu	-481
+▁want	-482
+ict	-483
+ices	-484
+▁As	-485
+▁If	-486
+▁well	-487
+ities	-488
+▁ind	-489
+we	-490
+▁bet	-491
+▁ph	-492
+ise	-493
+▁use	-494
+▁two	-495
+▁co	-496
+xt	-497
+ont	-498
+com	-499
+▁act	-500
+▁und	-501
+ph	-502
+iness	-503
+lect	-504
+iss	-505
+▁after	-506
+oy	-507
+▁Se	-508
+ife	-509
+ause	-510
+▁play	-511
+fect	-512
+▁|	-513
+oth	-514
+▁&	-515
+ily	-516
+row	-517
+ork	-518
+enc	-519
+▁exper	-520
+ject	-521
+▁cons	-522
+hen	-523
+cial	-524
+urn	-525
+ert	-526
+▁years	-527
+als	-528
+▁these	-529
+ank	-530
+ting	-531
+▁$	-532
+▁Com	-533
+aw	-534
+▁bus	-535
+▁An	-536
+▁Un	-537
+▁stud	-538
+any	-539
+bs	-540
+ange	-541
+▁For	-542
+ures	-543
+vent	-544
+▁good	-545
+ational	-546
+aking	-547
+▁see	-548
+▁ke	-549
+ased	-550
+ific	-551
+▁Pro	-552
+▁now	-553
+fore	-554
+▁under	-555
+▁very	-556
+▁many	-557
+▁reg	-558
+▁sm	-559
+ward	-560
+hing	-561
+▁imp	-562
+get	-563
+oint	-564
+▁dif	-565
+▁ra	-566
+▁way	-567
+erson	-568
+ience	-569
+▁start	-570
+ts	-571
+pect	-572
+▁fin	-573
+▁great	-574
+▁And	-575
+yst	-576
+uring	-577
+▁De	-578
+▁rel	-579
+formation	-580
+▁gu	-581
+ility	-582
+ible	-583
+▁rem	-584
+▁could	-585
+oss	-586
+hip	-587
+▁dec	-588
+uch	-589
+▁even	-590
+▁inv	-591
+).	-592
+ty	-593
+ics	-594
+rit	-595
+ract	-596
+▁own	-597
+▁sec	-598
+cess	-599
+velop	-600
+▁day	-601
+▁where	-602
+▁show	-603
+ident	-604
+elf	-605
+hes	-606
+alth	-607
+▁high	-608
+its	-609
+▁loc	-610
+air	-611
+▁find	-612
+olog	-613
+▁ac	-614
+ull	-615
+nds	-616
+▁Al	-617
+▁don	-618
+▁ass	-619
+▁home	-620
+▁should	-621
+line	-622
+ath	-623
+▁ent	-624
+▁best	-625
+▁here	-626
+▁down	-627
+lease	-628
+▁then	-629
+▁Sh	-630
+ied	-631
+ble	-632
+ular	-633
+||	-634
+▁right	-635
+The	-636
+arch	-637
+▁set	-638
+chool	-639
+ited	-640
+▁car	-641
+▁av	-642
+▁read	-643
+▁New	-644
+▁mon	-645
+gan	-646
+▁min	-647
+▁take	-648
+▁business	-649
+erm	-650
+▁fam	-651
+▁ins	-652
+ner	-653
+ix	-654
+▁inst	-655
+▁fl	-656
+ys	-657
+▁design	-658
+▁att	-659
+ystem	-660
+▁br	-661
+alk	-662
+▁too	-663
+.”	-664
+▁che	-665
+▁bl	-666
+io	-667
+▁long	-668
+▁much	-669
+ative	-670
+▁information	-671
+▁Be	-672
+▁made	-673
+▁last	-674
+ollow	-675
+ason	-676
+other	-677
+ues	-678
+gram	-679
+arket	-680
+▁product	-681
+omet	-682
+▁because	-683
+ock	-684
+ax	-685
+▁Fr	-686
+),	-687
+rib	-688
+▁week	-689
+▁call	-690
+▁did	-691
+▁before	-692
+▁think	-693
+▁Cl	-694
+▁team	-695
+▁world	-696
+atch	-697
+me	-698
+▁cre	-699
+ale	-700
+pen	-701
+oun	-702
+▁again	-703
+▁sur	-704
+ower	-705
+▁Ad	-706
+▁vis	-707
+ient	-708
+▁But	-709
+chn	-710
+pr	-711
+az	-712
+ustom	-713
+land	-714
+▁requ	-715
+▁art	-716
+▁develop	-717
+▁being	-718
+▁diffe	-719
+▁pres	-720
+rest	-721
+way	-722
+▁person	-723
+ng	-724
+ener	-725
+▁such	-726
+▁Le	-727
+▁inte	-728
+▁mem	-729
+▁disc	-730
+▁him	-731
+ces	-732
+▁support	-733
+▁life	-734
+arn	-735
+ug	-736
+ving	-737
+ced	-738
+ouse	-739
+unity	-740
+ave	-741
+ince	-742
+irect	-743
+▁med	-744
+▁Ar	-745
+▁does	-746
+▁while	-747
+▁those	-748
+ins	-749
+▁provid	-750
+ash	-751
+arm	-752
+view	-753
+▁sim	-754
+ivers	-755
+ros	-756
+▁lead	-757
+▁sk	-758
+akes	-759
+ality	-760
+▁pol	-761
+▁end	-762
+▁mod	-763
+▁used	-764
+▁cur	-765
+ives	-766
+▁around	-767
+ric	-768
+led	-769
+ier	-770
+▁free	-771
+ailable	-772
+ually	-773
+▁each	-774
+▁care	-775
+▁comple	-776
+▁follow	-777
+ional	-778
+ublic	-779
+▁det	-780
+▁On	-781
+ple	-782
+read	-783
+der	-784
+▁ret	-785
+ize	-786
+▁trans	-787
+ather	-788
+▁love	-789
+▁There	-790
+ages	-791
+▁post	-792
+ines	-793
+▁child	-794
+▁system	-795
+ars	-796
+▁bo	-797
+ene	-798
+roup	-799
+▁eas	-800
+▁book	-801
+▁num	-802
+▁ed	-803
+▁How	-804
+▁ser	-805
+,”	-806
+imes	-807
+▁Te	-808
+▁really	-809
+▁count	-810
+ets	-811
+▁gr	-812
+▁str	-813
+▁program	-814
+▁custom	-815
+ton	-816
+▁top	-817
+▁run	-818
+▁del	-819
+au	-820
+▁All	-821
+iet	-822
+▁cour	-823
+▁found	-824
+ffect	-825
+▁So	-826
+▁place	-827
+▁list	-828
+ness	-829
+ved	-830
+iel	-831
+▁form	-832
+▁month	-833
+▁prof	-834
+▁char	-835
+ah	-836
+▁feel	-837
+▁To	-838
+ute	-839
+▁available	-840
+▁going	-841
+▁inter	-842
+ittle	-843
+▁They	-844
+▁sign	-845
+▁sub	-846
+gg	-847
+▁market	-848
+man	-849
+ature	-850
+ames	-851
+▁fun	-852
+▁cle	-853
+▁still	-854
+cept	-855
+▁Pl	-856
+ways	-857
+▁somet	-858
+▁different	-859
+▁aut	-860
+▁both	-861
+▁three	-862
+▁few	-863
+orn	-864
+▁health	-865
+▁though	-866
+▁Ex	-867
+ital	-868
+ired	-869
+▁pur	-870
+ering	-871
+▁rep	-872
+▁adv	-873
+▁exp	-874
+▁techn	-875
+▁happ	-876
+▁open	-877
+▁lot	-878
+▁report	-879
+▁company	-880
+ata	-881
+ween	-882
+▁keep	-883
+meric	-884
+▁Sc	-885
+orth	-886
+▁plan	-887
+▁hand	-888
+ining	-889
+bers	-890
+iqu	-891
+▁She	-892
+tt	-893
+ants	-894
+be	-895
+▁ext	-896
+▁lar	-897
+▁game	-898
+▁sol	-899
+▁point	-900
+▁Q	-901
+ross	-902
+ology	-903
+▁say	-904
+ves	-905
+atur	-906
+▁met	-907
+▁import	-908
+▁process	-909
+▁fil	-910
+▁frie	-911
+▁including	-912
+▁family	-913
+▁ev	-914
+▁using	-915
+▁same	-916
+work	-917
+▁project	-918
+ized	-919
+uc	-920
+oot	-921
+▁school	-922
+▁between	-923
+▁What	-924
+ling	-925
+ik	-926
+▁little	-927
+ution	-928
+att	-929
+ott	-930
+▁experience	-931
+▁during	-932
+."	-933
+less	-934
+▁state	-935
+iving	-936
+▁Col	-937
+▁i	-938
+▁next	-939
+uss	-940
+els	-941
+▁service	-942
+aint	-943
+▁real	-944
+ody	-945
+oh	-946
+▁build	-947
+▁allow	-948
+ms	-949
+reen	-950
+▁opt	-951
+▁water	-952
+ished	-953
+▁things	-954
+▁come	-955
+▁contin	-956
+thing	-957
+▁Americ	-958
+▁var	-959
+▁Ph	-960
+▁dri	-961
+ists	-962
+uck	-963
+ever	-964
+ern	-965
+ield	-966
+▁cent	-967
+arly	-968
+over	-969
+rand	-970
+▁small	-971
+▁rece	-972
+▁organ	-973
+▁appro	-974
+▁rest	-975
+gy	-976
+▁big	-977
+self	-978
+▁Ind	-979
+▁ref	-980
+ex	-981
+▁always	-982
+▁mus	-983
+▁better	-984
+▁sure	-985
+▁With	-986
+▁interest	-987
+▁win	-988
+aut	-989
+loy	-990
+▁full	-991
+▁pat	-992
+▁pass	-993
+▁poss	-994
+ery	-995
+illion	-996
+▁online	-997
+▁pri	-998
+▁iss	-999
+▁ty	-1000
+▁put	-1001
+ined	-1002
+cent	-1003
+ware	-1004
+▁When	-1005
+▁result	-1006
+▁gener	-1007
+▁since	-1008
+▁Bl	-1009
+▁ve	-1010
+ps	-1011
+▁try	-1012
+▁direct	-1013
+▁quest	-1014
+iversity	-1015
+▁mov	-1016
+▁stand	-1017
+▁partic	-1018
+▁days	-1019
+▁perform	-1020
+▁group	-1021
+ok	-1022
+▁val	-1023
+▁pay	-1024
+▁ide	-1025
+▁head	-1026
+▁special	-1027
+▁bel	-1028
+▁Tr	-1029
+▁today	-1030
+▁Chr	-1031
+▁something	-1032
+▁class	-1033
+▁provide	-1034
+ients	-1035
+ours	-1036
+▁tri	-1037
+▁second	-1038
+▁services	-1039
+▁ann	-1040
+▁Our	-1041
+ared	-1042
+▁Con	-1043
+ccess	-1044
+▁resp	-1045
+joy	-1046
+▁phot	-1047
+▁conf	-1048
+▁Is	-1049
+ploy	-1050
+▁Or	-1051
+▁dist	-1052
+▁hard	-1053
+▁without	-1054
+pping	-1055
+con	-1056
+▁Sp	-1057
+▁number	-1058
+▁Z	-1059
+ER	-1060
+▁bro	-1061
+▁def	-1062
+▁sl	-1063
+▁cor	-1064
+▁must	-1065
+oney	-1066
+▁blo	-1067
+▁another	-1068
+ision	-1069
+▁vide	-1070
+stand	-1071
+eng	-1072
+▁current	-1073
+cl	-1074
+outh	-1075
+▁give	-1076
+▁wom	-1077
+▁old	-1078
+aj	-1079
+ically	-1080
+▁access	-1081
+▁able	-1082
+▁webs	-1083
+ards	-1084
+▁important	-1085
+ior	-1086
+iver	-1087
+,"	-1088
+▁cr	-1089
+ately	-1090
+ium	-1091
+▁—	-1092
+▁cost	-1093
+sh	-1094
+▁grow	-1095
+▁ask	-1096
+ope	-1097
+ral	-1098
+▁meet	-1099
+▁fact	-1100
+▁invest	-1101
+▁At	-1102
+▁area	-1103
+ruct	-1104
+▁Cent	-1105
+▁public	-1106
+▁got	-1107
+raph	-1108
+▁Res	-1109
+▁wr	-1110
+▁bre	-1111
+▁soc	-1112
+ote	-1113
+▁visit	-1114
+▁proble	-1115
+ered	-1116
+▁light	-1117
+▁incre	-1118
+▁US	-1119
+ample	-1120
+▁working	-1121
+ems	-1122
+▁ob	-1123
+ense	-1124
+▁data	-1125
+▁unt	-1126
+ann	-1127
+rence	-1128
+pped	-1129
+br	-1130
+▁level	-1131
+▁proper	-1132
+▁looking	-1133
+▁never	-1134
+▁sal	-1135
+▁might	-1136
+inal	-1137
+▁No	-1138
+ats	-1139
+ffic	-1140
+▁order	-1141
+ential	-1142
+ember	-1143
+▁effect	-1144
+ley	-1145
+▁event	-1146
+▁fac	-1147
+▁students	-1148
+▁rese	-1149
+▁food	-1150
+▁local	-1151
+▁Man	-1152
+ency	-1153
+▁four	-1154
+▁Comm	-1155
+▁eng	-1156
+▁profess	-1157
+ird	-1158
+▁let	-1159
+▁That	-1160
+ission	-1161
+▁offer	-1162
+▁inf	-1163
+ww	-1164
+▁enjoy	-1165
+▁site	-1166
+▁Pr	-1167
+▁spec	-1168
+▁season	-1169
+▁check	-1170
+▁addition	-1171
+ertain	-1172
+▁within	-1173
+▁children	-1174
+gin	-1175
+▁oper	-1176
+▁pos	-1177
+▁test	-1178
+ording	-1179
+▁making	-1180
+▁My	-1181
+▁view	-1182
+lection	-1183
+▁room	-1184
+▁sit	-1185
+▁prom	-1186
+▁power	-1187
+ories	-1188
+ney	-1189
+▁expl	-1190
+here	-1191
+▁ca	-1192
+load	-1193
+ently	-1194
+▁products	-1195
+rol	-1196
+▁night	-1197
+▁past	-1198
+▁community	-1199
+▁pop	-1200
+▁Mar	-1201
+▁sing	-1202
+▁against	-1203
+let	-1204
+ream	-1205
+tend	-1206
+▁until	-1207
+ases	-1208
+▁less	-1209
+▁'	-1210
+utes	-1211
+▁el	-1212
+ains	-1213
+agement	-1214
+▁est	-1215
+med	-1216
+ids	-1217
+▁email	-1218
+ieve	-1219
+▁job	-1220
+iron	-1221
+ised	-1222
+ator	-1223
+▁quality	-1224
+ivid	-1225
+▁May	-1226
+ina	-1227
+▁intern	-1228
+▁indust	-1229
+to	-1230
+ills	-1231
+▁gl	-1232
+▁website	-1233
+▁prote	-1234
+▁impro	-1235
+▁law	-1236
+ode	-1237
+ks	-1238
+orm	-1239
+▁equ	-1240
+▁App	-1241
+▁turn	-1242
+ified	-1243
+enn	-1244
+urs	-1245
+co	-1246
+ged	-1247
+IN	-1248
+▁Br	-1249
+▁away	-1250
+icle	-1251
+▁air	-1252
+▁Fe	-1253
+▁contact	-1254
+▁creat	-1255
+▁toget	-1256
+We	-1257
+▁together	-1258
+▁University	-1259
+bo	-1260
+istr	-1261
+ique	-1262
+pend	-1263
+aring	-1264
+▁supp	-1265
+▁learn	-1266
+▁success	-1267
+▁pract	-1268
+▁Co	-1269
+▁dr	-1270
+ury	-1271
+▁complete	-1272
+▁Can	-1273
+▁leg	-1274
+iday	-1275
+▁applic	-1276
+▁expect	-1277
+▁needs	-1278
+▁include	-1279
+por	-1280
+▁Christ	-1281
+iety	-1282
+ocus	-1283
+atter	-1284
+ider	-1285
+▁Cont	-1286
+▁.	-1287
+▁detail	-1288
+▁large	-1289
+▁easy	-1290
+▁la	-1291
+▁Car	-1292
+ability	-1293
+ret	-1294
+▁One	-1295
+oci	-1296
+▁along	-1297
+irl	-1298
+▁course	-1299
+▁says	-1300
+▁change	-1301
+▁news	-1302
+arent	-1303
+aster	-1304
+room	-1305
+▁present	-1306
+ger	-1307
+▁offic	-1308
+vern	-1309
+▁name	-1310
+▁chang	-1311
+hor	-1312
+ism	-1313
+▁conc	-1314
+yle	-1315
+ym	-1316
+atures	-1317
+▁beaut	-1318
+▁Am	-1319
+▁Do	-1320
+▁activ	-1321
+pos	-1322
+▁cap	-1323
+part	-1324
+lish	-1325
+ump	-1326
+ising	-1327
+▁members	-1328
+ries	-1329
+▁Me	-1330
+▁money	-1331
+▁Ste	-1332
+enef	-1333
+min	-1334
+iting	-1335
+▁employ	-1336
+rap	-1337
+▁video	-1338
+▁bas	-1339
+▁times	-1340
+the	-1341
+▁talk	-1342
+▁Eng	-1343
+ify	-1344
+▁buy	-1345
+ec	-1346
+augh	-1347
+▁beh	-1348
+▁music	-1349
+itions	-1350
+▁Ro	-1351
+▁fav	-1352
+▁These	-1353
+▁house	-1354
+une	-1355
+▁pa	-1356
+ift	-1357
+nect	-1358
+▁opport	-1359
+▁dem	-1360
+▁sw	-1361
+side	-1362
+▁/	-1363
+ane	-1364
+▁hist	-1365
+▁why	-1366
+Th	-1367
+▁En	-1368
+▁dra	-1369
+ably	-1370
+▁cond	-1371
+▁ce	-1372
+▁case	-1373
+▁please	-1374
+▁treat	-1375
+by	-1376
+mber	-1377
+ron	-1378
+veral	-1379
+ots	-1380
+▁perfect	-1381
+aff	-1382
+rie	-1383
+aterial	-1384
+pecial	-1385
+▁live	-1386
+ready	-1387
+fort	-1388
+ten	-1389
+▁govern	-1390
+▁account	-1391
+▁dev	-1392
+▁short	-1393
+ention	-1394
+▁thing	-1395
+ization	-1396
+▁create	-1397
+▁following	-1398
+▁Che	-1399
+▁story	-1400
+ON	-1401
+▁clo	-1402
+▁left	-1403
+book	-1404
+▁const	-1405
+ived	-1406
+viron	-1407
+▁review	-1408
+▁below	-1409
+▁trad	-1410
+▁understand	-1411
+▁hum	-1412
+▁million	-1413
+son	-1414
+!!	-1415
+▁side	-1416
+itive	-1417
+▁having	-1418
+alf	-1419
+▁Your	-1420
+ored	-1421
+▁After	-1422
+▁hot	-1423
+ohn	-1424
+ows	-1425
+sc	-1426
+▁page	-1427
+etwork	-1428
+▁Med	-1429
+▁Fl	-1430
+▁based	-1431
+▁focus	-1432
+▁makes	-1433
+of	-1434
+▁word	-1435
+AT	-1436
+RE	-1437
+▁research	-1438
+▁move	-1439
+▁writ	-1440
+▁across	-1441
+▁camp	-1442
+▁personal	-1443
+ienc	-1444
+▁link	-1445
+▁line	-1446
+ances	-1447
+▁kind	-1448
+▁possible	-1449
+▁cou	-1450
+rop	-1451
+▁ever	-1452
+▁mar	-1453
+▁pot	-1454
+uture	-1455
+ividual	-1456
+▁getting	-1457
+▁comes	-1458
+▁already	-1459
+uly	-1460
+▁benef	-1461
+ajor	-1462
+▁elect	-1463
+▁educ	-1464
+vious	-1465
+▁record	-1466
+ured	-1467
+uper	-1468
+osp	-1469
+▁country	-1470
+▁become	-1471
+▁soft	-1472
+▁Rep	-1473
+ination	-1474
+oice	-1475
+orts	-1476
+▁often	-1477
+▁share	-1478
+▁friends	-1479
+▁several	-1480
+ush	-1481
+▁Ass	-1482
+▁done	-1483
+iven	-1484
+ister	-1485
+▁social	-1486
+▁Count	-1487
+▁es	-1488
+duct	-1489
+▁pack	-1490
+▁bit	-1491
+wards	-1492
+▁fund	-1493
+ead	-1494
+iam	-1495
+▁enough	-1496
+▁quick	-1497
+▁mil	-1498
+▁tre	-1499
+ones	-1500
+▁minutes	-1501
+uro	-1502
+▁Please	-1503
+conom	-1504
+fer	-1505
+▁bring	-1506
+▁Inst	-1507
+inc	-1508
+▁women	-1509
+uff	-1510
+▁development	-1511
+▁vers	-1512
+▁Serv	-1513
+▁hours	-1514
+▁Des	-1515
+▁body	-1516
+▁mult	-1517
+unch	-1518
+app	-1519
+oose	-1520
+ips	-1521
+▁tell	-1522
+ides	-1523
+iful	-1524
+▁John	-1525
+vironment	-1526
+▁return	-1527
+▁purch	-1528
+mend	-1529
+▁:	-1530
+aim	-1531
+▁cut	-1532
+▁men	-1533
+ners	-1534
+▁city	-1535
+▁lo	-1536
+arl	-1537
+reet	-1538
+ape	-1539
+▁Intern	-1540
+▁deal	-1541
+▁X	-1542
+oon	-1543
+▁individual	-1544
+AN	-1545
+▁exc	-1546
+▁won	-1547
+ST	-1548
+▁ens	-1549
+▁young	-1550
+ted	-1551
+ateg	-1552
+▁Here	-1553
+▁material	-1554
+▁hold	-1555
+▁compet	-1556
+ograph	-1557
+▁sum	-1558
+▁...	-1559
+▁Comp	-1560
+▁others	-1561
+▁jo	-1562
+yn	-1563
+utions	-1564
+▁Tw	-1565
+▁started	-1566
+▁called	-1567
+▁industry	-1568
+▁months	-1569
+▁mom	-1570
+▁term	-1571
+▁non	-1572
+▁orig	-1573
+idd	-1574
+ights	-1575
+▁didn	-1576
+ript	-1577
+▁land	-1578
+ee	-1579
+ai	-1580
+nder	-1581
+▁Gu	-1582
+▁walk	-1583
+▁clean	-1584
+▁future	-1585
+▁rele	-1586
+▁American	-1587
+▁However	-1588
+▁pie	-1589
+.,	-1590
+▁City	-1591
+▁far	-1592
+▁commun	-1593
+lished	-1594
+ched	-1595
+▁po	-1596
+▁doing	-1597
+▁major	-1598
+ained	-1599
+▁control	-1600
+▁space	-1601
+ource	-1602
+fact	-1603
+ball	-1604
+urity	-1605
+arr	-1606
+osed	-1607
+▁wa	-1608
+▁low	-1609
+ges	-1610
+▁cover	-1611
+▁Ab	-1612
+▁store	-1613
+anies	-1614
+lement	-1615
+ference	-1616
+ford	-1617
+▁occ	-1618
+▁games	-1619
+▁means	-1620
+AR	-1621
+lege	-1622
+▁Not	-1623
+▁mind	-1624
+▁offers	-1625
+oring	-1626
+▁Tra	-1627
+▁yet	-1628
+▁bra	-1629
+▁Dr	-1630
+▁came	-1631
+▁five	-1632
+▁percent	-1633
+▁chall	-1634
+▁comb	-1635
+▁Min	-1636
+▁took	-1637
+▁invol	-1638
+▁doesn	-1639
+sel	-1640
+▁lim	-1641
+orld	-1642
+▁fore	-1643
+ilities	-1644
+▁*	-1645
+▁customers	-1646
+▁features	-1647
+bal	-1648
+▁State	-1649
+▁least	-1650
+▁strong	-1651
+▁step	-1652
+▁price	-1653
+ches	-1654
+▁heart	-1655
+▁God	-1656
+▁Ke	-1657
+urther	-1658
+▁range	-1659
+▁specific	-1660
+▁More	-1661
+▁main	-1662
+most	-1663
+▁require	-1664
+▁close	-1665
+▁School	-1666
+▁once	-1667
+▁key	-1668
+▁pict	-1669
+sw	-1670
+err	-1671
+ler	-1672
+▁upd	-1673
+ilt	-1674
+ither	-1675
+▁mean	-1676
+▁Bo	-1677
+▁early	-1678
+▁ey	-1679
+▁cra	-1680
+▁Jan	-1681
+▁Now	-1682
+▁tool	-1683
+▁stay	-1684
+▁discuss	-1685
+▁government	-1686
+illed	-1687
+aces	-1688
+af	-1689
+▁series	-1690
+▁tem	-1691
+ources	-1692
+▁hig	-1693
+▁priv	-1694
+▁Bro	-1695
+▁ste	-1696
+▁technology	-1697
+pro	-1698
+cle	-1699
+▁install	-1700
+▁charact	-1701
+▁Im	-1702
+atural	-1703
+▁Ed	-1704
+▁typ	-1705
+▁United	-1706
+▁redu	-1707
+▁beautiful	-1708
+atic	-1709
+▁By	-1710
+▁ago	-1711
+▁went	-1712
+▁begin	-1713
+aken	-1714
+//	-1715
+▁announ	-1716
+org	-1717
+▁thought	-1718
+▁Pe	-1719
+▁pick	-1720
+▁told	-1721
+▁hope	-1722
+▁appear	-1723
+ancial	-1724
+isk	-1725
+It	-1726
+resent	-1727
+▁anal	-1728
+▁happen	-1729
+anks	-1730
+rew	-1731
+▁Gr	-1732
+▁Em	-1733
+irm	-1734
+▁break	-1735
+ille	-1736
+▁wind	-1737
+▁questions	-1738
+resh	-1739
+OR	-1740
+▁York	-1741
+▁x	-1742
+▁Qu	-1743
+come	-1744
+▁Pre	-1745
+▁content	-1746
+▁certain	-1747
+▁Add	-1748
+oll	-1749
+▁everything	-1750
+▁prep	-1751
+ourn	-1752
+hers	-1753
+://	-1754
+▁sn	-1755
+ians	-1756
+irt	-1757
+gle	-1758
+▁field	-1759
+▁companies	-1760
+▁travel	-1761
+ony	-1762
+▁Cal	-1763
+▁enc	-1764
+▁recom	-1765
+▁single	-1766
+▁known	-1767
+▁added	-1768
+▁favor	-1769
+▁media	-1770
+▁--	-1771
+cell	-1772
+▁building	-1773
+arning	-1774
+▁manag	-1775
+▁Park	-1776
+aps	-1777
+▁search	-1778
+▁environment	-1779
+▁friend	-1780
+▁actually	-1781
+aur	-1782
+▁address	-1783
+ief	-1784
+▁tot	-1785
+▁ener	-1786
+de	-1787
+▁study	-1788
+▁mess	-1789
+eral	-1790
+▁vol	-1791
+▁tax	-1792
+▁press	-1793
+▁problem	-1794
+play	-1795
+isc	-1796
+▁later	-1797
+▁connect	-1798
+ino	-1799
+▁works	-1800
+ests	-1801
+▁Sm	-1802
+▁girl	-1803
+icy	-1804
+▁improve	-1805
+gest	-1806
+acy	-1807
+ibr	-1808
+▁taking	-1809
+ew	-1810
+▁South	-1811
+▁ident	-1812
+▁maint	-1813
+▁sound	-1814
+▁pub	-1815
+ental	-1816
+year	-1817
+lebr	-1818
+ural	-1819
+▁Su	-1820
+▁track	-1821
+ided	-1822
+▁training	-1823
+▁watch	-1824
+▁results	-1825
+ster	-1826
+▁staff	-1827
+▁card	-1828
+▁wond	-1829
+abor	-1830
+▁North	-1831
+▁face	-1832
+back	-1833
+▁professional	-1834
+nes	-1835
+ensive	-1836
+▁Mc	-1837
+▁Just	-1838
+ocu	-1839
+gs	-1840
+ES	-1841
+▁film	-1842
+▁provides	-1843
+wh	-1844
+atest	-1845
+yl	-1846
+▁seen	-1847
+▁While	-1848
+▁issues	-1849
+▁someone	-1850
+ama	-1851
+▁Per	-1852
+▁unique	-1853
+▁host	-1854
+▁half	-1855
+▁front	-1856
+▁official	-1857
+cer	-1858
+▁Euro	-1859
+fully	-1860
+▁near	-1861
+opy	-1862
+▁econom	-1863
+▁relations	-1864
+▁web	-1865
+▁sell	-1866
+▁particular	-1867
+▁National	-1868
+▁County	-1869
+▁everyone	-1870
+▁miss	-1871
+▁port	-1872
+AL	-1873
+▁dig	-1874
+urch	-1875
+▁due	-1876
+▁Aust	-1877
+▁Some	-1878
+go	-1879
+▁recommend	-1880
+▁network	-1881
+hod	-1882
+▁cook	-1883
+▁Center	-1884
+▁Don	-1885
+lex	-1886
+▁cred	-1887
+▁office	-1888
+▁respons	-1889
+▁z	-1890
+ued	-1891
+▁Inc	-1892
+▁Oct	-1893
+▁simple	-1894
+itted	-1895
+▁Part	-1896
+▁age	-1897
+▁ant	-1898
+ctor	-1899
+ibility	-1900
+▁aud	-1901
+▁management	-1902
+ging	-1903
+▁click	-1904
+not	-1905
+roll	-1906
+▁oil	-1907
+▁Pol	-1908
+▁particip	-1909
+time	-1910
+▁Dep	-1911
+asing	-1912
+▁whole	-1913
+pecially	-1914
+▁mot	-1915
+▁bar	-1916
+obile	-1917
+iod	-1918
+▁Acc	-1919
+▁Pres	-1920
+▁performance	-1921
+▁areas	-1922
+▁Apr	-1923
+▁mor	-1924
+▁ess	-1925
+pper	-1926
+▁fall	-1927
+▁author	-1928
+cing	-1929
+▁given	-1930
+ply	-1931
+imate	-1932
+▁bed	-1933
+▁World	-1934
+icult	-1935
+nding	-1936
+▁above	-1937
+▁reason	-1938
+▁protect	-1939
+ites	-1940
+▁events	-1941
+In	-1942
+ators	-1943
+aining	-1944
+▁among	-1945
+▁eff	-1946
+ables	-1947
+umb	-1948
+▁Will	-1949
+ops	-1950
+▁experienc	-1951
+ask	-1952
+▁Sec	-1953
+▁history	-1954
+EN	-1955
+▁select	-1956
+▁Stud	-1957
+omes	-1958
+▁black	-1959
+ogn	-1960
+ED	-1961
+▁assist	-1962
+▁size	-1963
+▁energy	-1964
+▁foot	-1965
+ison	-1966
+cy	-1967
+ili	-1968
+▁High	-1969
+▁details	-1970
+▁print	-1971
+ledge	-1972
+▁htt	-1973
+▁Reg	-1974
+▁glo	-1975
+▁believe	-1976
+▁flo	-1977
+▁sex	-1978
+crib	-1979
+▁further	-1980
+▁From	-1981
+▁amount	-1982
+▁Post	-1983
+▁six	-1984
+▁log	-1985
+idence	-1986
+ety	-1987
+ulation	-1988
+▁designed	-1989
+▁includes	-1990
+▁prob	-1991
+▁Friday	-1992
+astic	-1993
+▁pain	-1994
+ands	-1995
+vert	-1996
+▁cult	-1997
+ufact	-1998
+▁points	-1999
+▁repl	-2000
+▁parent	-2001
+▁mag	-2002
+▁red	-2003
+▁Day	-2004
+▁property	-2005
+AS	-2006
+▁Ge	-2007
+ruction	-2008
+▁Bar	-2009
+▁continue	-2010
+▁soon	-2011
+nov	-2012
+▁feature	-2013
+▁Aug	-2014
+▁value	-2015
+urance	-2016
+▁et	-2017
+▁Mr	-2018
+▁Europe	-2019
+▁anything	-2020
+▁text	-2021
+▁various	-2022
+itch	-2023
+▁coming	-2024
+▁question	-2025
+▁popular	-2026
+▁latest	-2027
+itional	-2028
+▁according	-2029
+aily	-2030
+▁lov	-2031
+▁living	-2032
+rodu	-2033
+▁phys	-2034
+▁forward	-2035
+▁type	-2036
+my	-2037
+▁fre	-2038
+uation	-2039
+▁March	-2040
+▁phone	-2041
+itc	-2042
+ouch	-2043
+▁consider	-2044
+cript	-2045
+▁pret	-2046
+▁whether	-2047
+aturday	-2048
+IC	-2049
+IT	-2050
+▁brand	-2051
+▁entire	-2052
+▁idea	-2053
+ze	-2054
+though	-2055
+▁claim	-2056
+▁white	-2057
+edd	-2058
+aching	-2059
+▁celebr	-2060
+▁weeks	-2061
+▁gra	-2062
+▁dou	-2063
+▁needed	-2064
+▁Bu	-2065
+▁diff	-2066
+▁consum	-2067
+▁potential	-2068
+▁opportunity	-2069
+▁comput	-2070
+▁deb	-2071
+▁El	-2072
+▁color	-2073
+elt	-2074
+▁taken	-2075
+▁Us	-2076
+▁June	-2077
+▁wide	-2078
+▁required	-2079
+▁receive	-2080
+▁par	-2081
+▁date	-2082
+▁Sept	-2083
+▁extra	-2084
+selves	-2085
+▁Sund	-2086
+ung	-2087
+itter	-2088
+▁docu	-2089
+new	-2090
+▁third	-2091
+▁example	-2092
+AC	-2093
+▁relationship	-2094
+▁safe	-2095
+ival	-2096
+▁bad	-2097
+▁sent	-2098
+▁ensure	-2099
+This	-2100
+itor	-2101
+ises	-2102
+▁ready	-2103
+▁inj	-2104
+▁Off	-2105
+▁West	-2106
+▁,	-2107
+▁comfort	-2108
+▁currently	-2109
+ilar	-2110
+amer	-2111
+▁meas	-2112
+ees	-2113
+ires	-2114
+▁financial	-2115
+▁common	-2116
+▁almost	-2117
+ffe	-2118
+▁sugg	-2119
+▁fire	-2120
+head	-2121
+▁ach	-2122
+▁April	-2123
+val	-2124
+uary	-2125
+▁ways	-2126
+▁human	-2127
+▁kids	-2128
+▁Read	-2129
+▁Art	-2130
+▁pretty	-2131
+▁period	-2132
+▁quite	-2133
+▁Jo	-2134
+▁options	-2135
+▁final	-2136
+▁skin	-2137
+▁natural	-2138
+▁yourself	-2139
+▁especially	-2140
+▁veh	-2141
+irc	-2142
+▁road	-2143
+▁style	-2144
+▁trying	-2145
+▁park	-2146
+▁sho	-2147
+▁box	-2148
+▁Health	-2149
+▁Cor	-2150
+ring	-2151
+▁items	-2152
+▁His	-2153
+▁answ	-2154
+▁paper	-2155
+used	-2156
+▁member	-2157
+▁provided	-2158
+▁either	-2159
+ese	-2160
+ana	-2161
+ively	-2162
+....	-2163
+▁Saturday	-2164
+itting	-2165
+onday	-2166
+▁coll	-2167
+▁engine	-2168
+▁choose	-2169
+▁hon	-2170
+▁self	-2171
+▁crit	-2172
+▁held	-2173
+▁throughout	-2174
+▁happy	-2175
+▁dam	-2176
+▁fit	-2177
+▁download	-2178
+▁via	-2179
+▁swe	-2180
+▁attend	-2181
+▁wanted	-2182
+▁flow	-2183
+▁clients	-2184
+▁stra	-2185
+ication	-2186
+▁summer	-2187
+▁Pa	-2188
+▁recent	-2189
+▁Fin	-2190
+▁impact	-2191
+▁Aut	-2192
+▁users	-2193
+ada	-2194
+▁created	-2195
+▁sales	-2196
+▁tit	-2197
+▁Af	-2198
+icro	-2199
+▁July	-2200
+azing	-2201
+▁blog	-2202
+▁issue	-2203
+▁previous	-2204
+▁behind	-2205
+▁takes	-2206
+arter	-2207
+oogle	-2208
+▁recently	-2209
+hel	-2210
+▁TH	-2211
+▁software	-2212
+▁Dav	-2213
+angu	-2214
+gress	-2215
+IS	-2216
+do	-2217
+▁init	-2218
+cast	-2219
+ams	-2220
+ux	-2221
+▁version	-2222
+▁super	-2223
+▁Get	-2224
+▁Feb	-2225
+ried	-2226
+▁bott	-2227
+▁seem	-2228
+▁Up	-2229
+▁couple	-2230
+▁song	-2231
+▁running	-2232
+▁insp	-2233
+▁hol	-2234
+verage	-2235
+ume	-2236
+ober	-2237
+▁clear	-2238
+▁collect	-2239
+▁problems	-2240
+ades	-2241
+apt	-2242
+▁isn	-2243
+▁education	-2244
+▁received	-2245
+▁method	-2246
+oura	-2247
+▁table	-2248
+▁players	-2249
+▁role	-2250
+▁represent	-2251
+▁reading	-2252
+▁Val	-2253
+uge	-2254
+▁Direct	-2255
+eth	-2256
+▁Int	-2257
+anced	-2258
+itten	-2259
+▁signific	-2260
+atform	-2261
+▁likely	-2262
+eke	-2263
+ole	-2264
+earch	-2265
+ification	-2266
+▁Sw	-2267
+par	-2268
+▁shows	-2269
+▁di	-2270
+where	-2271
+▁security	-2272
+▁increase	-2273
+▁accom	-2274
+▁States	-2275
+▁Mon	-2276
+▁favorite	-2277
+▁customer	-2278
+▁stri	-2279
+▁pan	-2280
+▁party	-2281
+reme	-2282
+▁action	-2283
+▁skills	-2284
+▁regular	-2285
+St	-2286
+▁difficult	-2287
+▁fast	-2288
+▁simply	-2289
+idge	-2290
+OU	-2291
+▁sle	-2292
+▁else	-2293
+▁Face	-2294
+▁writing	-2295
+▁ele	-2296
+▁nice	-2297
+aging	-2298
+▁Sunday	-2299
+▁Monday	-2300
+oud	-2301
+oid	-2302
+▁position	-2303
+overed	-2304
+▁article	-2305
+▁outside	-2306
+▁original	-2307
+▁Her	-2308
+▁probably	-2309
+▁cool	-2310
+icles	-2311
+aving	-2312
+mit	-2313
+▁cup	-2314
+▁necess	-2315
+▁inside	-2316
+▁fresh	-2317
+ID	-2318
+istration	-2319
+▁asked	-2320
+▁wonder	-2321
+▁goal	-2322
+▁systems	-2323
+.)	-2324
+▁manufact	-2325
+arth	-2326
+aby	-2327
+▁model	-2328
+--	-2329
+▁House	-2330
+li	-2331
+▁morning	-2332
+▁ground	-2333
+▁President	-2334
+icated	-2335
+▁application	-2336
+▁leave	-2337
+ham	-2338
+eter	-2339
+▁ful	-2340
+▁learning	-2341
+▁anim	-2342
+uit	-2343
+aker	-2344
+▁Associ	-2345
+▁risk	-2346
+▁Act	-2347
+▁Black	-2348
+▁knowledge	-2349
+▁located	-2350
+based	-2351
+▁contrib	-2352
+▁UK	-2353
+▁release	-2354
+▁projects	-2355
+▁lives	-2356
+▁changes	-2357
+▁tour	-2358
+▁Are	-2359
+▁Bus	-2360
+▁however	-2361
+ox	-2362
+▁Free	-2363
+▁treatment	-2364
+▁stop	-2365
+medi	-2366
+face	-2367
+right	-2368
+▁Austral	-2369
+▁exist	-2370
+▁mix	-2371
+▁recogn	-2372
+▁additional	-2373
+▁polit	-2374
+adem	-2375
+▁Red	-2376
+▁activities	-2377
+▁private	-2378
+▁abs	-2379
+▁sat	-2380
+▁career	-2381
+iple	-2382
+name	-2383
+▁board	-2384
+▁medical	-2385
+▁Work	-2386
+▁total	-2387
+▁Mich	-2388
+▁cal	-2389
+▁anyone	-2390
+▁hit	-2391
+▁etc	-2392
+artment	-2393
+▁fail	-2394
+▁ple	-2395
+▁TV	-2396
+▁accept	-2397
+urg	-2398
+▁town	-2399
+▁Soc	-2400
+ague	-2401
+▁base	-2402
+arget	-2403
+aign	-2404
+amed	-2405
+bor	-2406
+OT	-2407
+hib	-2408
+▁mark	-2409
+▁former	-2410
+▁contract	-2411
+▁matter	-2412
+▁included	-2413
+▁America	-2414
+ming	-2415
+ounc	-2416
+ules	-2417
+▁mach	-2418
+ession	-2419
+▁Sal	-2420
+iol	-2421
+▁stock	-2422
+▁match	-2423
+▁autom	-2424
+▁words	-2425
+▁significant	-2426
+izing	-2427
+▁hair	-2428
+ipment	-2429
+▁saf	-2430
+ecut	-2431
+▁Ser	-2432
+▁meeting	-2433
+wood	-2434
+▁Of	-2435
+▁October	-2436
+▁books	-2437
+▁September	-2438
+ovember	-2439
+▁growth	-2440
+▁Ac	-2441
+▁playing	-2442
+▁January	-2443
+aced	-2444
+▁leaders	-2445
+empt	-2446
+▁ball	-2447
+▁worth	-2448
+mon	-2449
+irth	-2450
+▁round	-2451
+▁longer	-2452
+▁drive	-2453
+▁hy	-2454
+▁character	-2455
+▁variety	-2456
+ny	-2457
+▁concern	-2458
+▁News	-2459
+▁First	-2460
+▁practice	-2461
+ester	-2462
+▁production	-2463
+che	-2464
+▁function	-2465
+▁Sk	-2466
+▁Wed	-2467
+rict	-2468
+▁looks	-2469
+▁squ	-2470
+ground	-2471
+▁exam	-2472
+▁late	-2473
+reg	-2474
+▁San	-2475
+ude	-2476
+▁lay	-2477
+airs	-2478
+▁Every	-2479
+▁wall	-2480
+mercial	-2481
+pm	-2482
+iff	-2483
+▁sun	-2484
+ursday	-2485
+▁defin	-2486
+adu	-2487
+▁determ	-2488
+na	-2489
+▁Ag	-2490
+▁August	-2491
+▁suggest	-2492
+ci	-2493
+▁Har	-2494
+elcome	-2495
+▁worked	-2496
+▁weeke	-2497
+▁fig	-2498
+ville	-2499
+▁associ	-2500
+uesday	-2501
+▁Google	-2502
+▁programs	-2503
+▁death	-2504
+imum	-2505
+▁chance	-2506
+▁platform	-2507
+▁cand	-2508
+▁screen	-2509
+▁international	-2510
+▁Then	-2511
+iddle	-2512
+▁Let	-2513
+ipping	-2514
+cks	-2515
+rect	-2516
+▁deg	-2517
+▁true	-2518
+▁Dis	-2519
+▁nothing	-2520
+Wh	-2521
+▁challeng	-2522
+itchen	-2523
+▁loss	-2524
+▁general	-2525
+▁clos	-2526
+▁rather	-2527
+▁plans	-2528
+arden	-2529
+▁Facebook	-2530
+▁purchase	-2531
+▁estab	-2532
+erc	-2533
+▁amazing	-2534
+▁credit	-2535
+▁leading	-2536
+▁subject	-2537
+▁Department	-2538
+▁regard	-2539
+▁stat	-2540
+cember	-2541
+▁allows	-2542
+ouncil	-2543
+▁seems	-2544
+olution	-2545
+eds	-2546
+▁built	-2547
+▁arri	-2548
+▁police	-2549
+mas	-2550
+▁similar	-2551
+▁Mus	-2552
+▁student	-2553
+▁Sim	-2554
+▁usually	-2555
+▁infl	-2556
+▁Pat	-2557
+▁rate	-2558
+▁quickly	-2559
+▁Air	-2560
+oke	-2561
+▁November	-2562
+▁teac	-2563
+▁Also	-2564
+lin	-2565
+AM	-2566
+▁Street	-2567
+▁draw	-2568
+▁national	-2569
+ashing	-2570
+▁touch	-2571
+ought	-2572
+▁providing	-2573
+▁comment	-2574
+▁International	-2575
+oph	-2576
+light	-2577
+▁excell	-2578
+▁deep	-2579
+nesday	-2580
+▁apply	-2581
+▁higher	-2582
+iter	-2583
+iber	-2584
+▁choice	-2585
+▁photos	-2586
+clus	-2587
+▁Group	-2588
+str	-2589
+gar	-2590
+▁tast	-2591
+ING	-2592
+▁respect	-2593
+off	-2594
+▁collection	-2595
+▁safety	-2596
+▁image	-2597
+▁Out	-2598
+▁Cons	-2599
+now	-2600
+▁hands	-2601
+▁marketing	-2602
+▁prior	-2603
+ondon	-2604
+▁ideas	-2605
+▁integr	-2606
+▁moment	-2607
+▁movie	-2608
+▁sil	-2609
+▁encoura	-2610
+▁easily	-2611
+▁decision	-2612
+example	-2613
+▁ut	-2614
+▁Cour	-2615
+▁location	-2616
+▁cell	-2617
+▁bal	-2618
+▁inde	-2619
+▁dom	-2620
+hern	-2621
+▁rad	-2622
+▁prevent	-2623
+▁court	-2624
+▁af	-2625
+▁bud	-2626
+▁Wind	-2627
+▁op	-2628
+▁released	-2629
+▁decided	-2630
+▁mass	-2631
+▁ill	-2632
+▁commit	-2633
+▁Thursday	-2634
+ached	-2635
+▁digital	-2636
+▁Home	-2637
+put	-2638
+▁Tuesday	-2639
+ournal	-2640
+▁emb	-2641
+ha	-2642
+▁reported	-2643
+▁Well	-2644
+▁benefits	-2645
+▁Calif	-2646
+▁file	-2647
+ivery	-2648
+▁exact	-2649
+▁seek	-2650
+▁December	-2651
+▁introdu	-2652
+▁wood	-2653
+amb	-2654
+▁La	-2655
+▁cannot	-2656
+ma	-2657
+eal	-2658
+▁campaign	-2659
+▁lost	-2660
+reng	-2661
+▁display	-2662
+▁Most	-2663
+▁daily	-2664
+▁partners	-2665
+▁parents	-2666
+▁ord	-2667
+▁attack	-2668
+▁Business	-2669
+ishing	-2670
+idents	-2671
+hood	-2672
+▁involved	-2673
+▁agree	-2674
+▁announced	-2675
+▁cause	-2676
+▁sche	-2677
+▁effic	-2678
+rown	-2679
+▁sens	-2680
+ructure	-2681
+▁Gl	-2682
+unities	-2683
+▁drink	-2684
+▁piece	-2685
+▁center	-2686
+▁Ang	-2687
+ray	-2688
+ospital	-2689
+▁neg	-2690
+atory	-2691
+▁user	-2692
+▁dest	-2693
+OM	-2694
+▁related	-2695
+▁saw	-2696
+▁Any	-2697
+▁affect	-2698
+▁expected	-2699
+▁vict	-2700
+ipe	-2701
+▁Design	-2702
+▁investig	-2703
+▁ability	-2704
+▁club	-2705
+ederal	-2706
+▁patients	-2707
+▁Wednesday	-2708
+▁ep	-2709
+▁London	-2710
+▁Click	-2711
+ruary	-2712
+EO	-2713
+avy	-2714
+▁rout	-2715
+▁send	-2716
+illing	-2717
+▁ri	-2718
+▁save	-2719
+▁tick	-2720
+ilies	-2721
+▁modern	-2722
+▁norm	-2723
+just	-2724
+ET	-2725
+▁weekend	-2726
+▁mobile	-2727
+▁circ	-2728
+sp	-2729
+▁standard	-2730
+▁langu	-2731
+▁Prof	-2732
+▁expert	-2733
+▁option	-2734
+ett	-2735
+▁goes	-2736
+▁boy	-2737
+▁ded	-2738
+▁immedi	-2739
+▁green	-2740
+▁enter	-2741
+▁restaur	-2742
+▁computer	-2743
+▁Over	-2744
+▁fight	-2745
+▁War	-2746
+▁aw	-2747
+▁woman	-2748
+▁bag	-2749
+▁global	-2750
+▁pers	-2751
+istic	-2752
+board	-2753
+lim	-2754
+▁target	-2755
+▁mother	-2756
+ivity	-2757
+▁iP	-2758
+▁emer	-2759
+uel	-2760
+▁sym	-2761
+▁College	-2762
+like	-2763
+iring	-2764
+▁serious	-2765
+▁innov	-2766
+▁parts	-2767
+▁helps	-2768
+▁huge	-2769
+▁PM	-2770
+▁costs	-2771
+▁English	-2772
+key	-2773
+asons	-2774
+oday	-2775
+aves	-2776
+▁gen	-2777
+▁Check	-2778
+zz	-2779
+ellow	-2780
+▁surpr	-2781
+▁weight	-2782
+▁http	-2783
+▁earn	-2784
+enge	-2785
+uk	-2786
+erve	-2787
+▁rights	-2788
+ara	-2789
+▁bank	-2790
+▁ones	-2791
+ornia	-2792
+▁legal	-2793
+▁code	-2794
+▁solutions	-2795
+▁request	-2796
+▁equipment	-2797
+▁Sen	-2798
+▁myself	-2799
+▁gives	-2800
+▁tools	-2801
+▁Afric	-2802
+▁warm	-2803
+▁arch	-2804
+▁Other	-2805
+▁insurance	-2806
+cription	-2807
+raft	-2808
+band	-2809
+▁Del	-2810
+ram	-2811
+edding	-2812
+▁feed	-2813
+▁Hol	-2814
+EC	-2815
+▁approach	-2816
+ault	-2817
+▁conditions	-2818
+▁played	-2819
+▁giving	-2820
+▁admin	-2821
+▁dress	-2822
+▁Ob	-2823
+▁Techn	-2824
+pri	-2825
+▁Book	-2826
+attle	-2827
+▁attention	-2828
+▁roll	-2829
+OS	-2830
+▁levels	-2831
+▁sus	-2832
+▁sett	-2833
+▁resources	-2834
+unt	-2835
+▁award	-2836
+▁Par	-2837
+▁Brit	-2838
+▁prim	-2839
+hold	-2840
+▁deliver	-2841
+▁trust	-2842
+ension	-2843
+iction	-2844
+atives	-2845
+▁Service	-2846
+▁note	-2847
+▁sold	-2848
+aged	-2849
+bert	-2850
+▁qual	-2851
+▁remember	-2852
+▁policy	-2853
+▁February	-2854
+▁interested	-2855
+erous	-2856
+▁Play	-2857
+▁solution	-2858
+▁door	-2859
+▁Trans	-2860
+▁businesses	-2861
+▁capt	-2862
+▁gets	-2863
+▁planning	-2864
+▁subs	-2865
+▁highly	-2866
+▁lab	-2867
+aught	-2868
+▁object	-2869
+iding	-2870
+pose	-2871
+▁starting	-2872
+▁opp	-2873
+▁cases	-2874
+partment	-2875
+▁Law	-2876
+ysis	-2877
+▁Christmas	-2878
+akers	-2879
+▁lower	-2880
+▁upon	-2881
+▁instead	-2882
+▁vac	-2883
+▁write	-2884
+▁hear	-2885
+▁organization	-2886
+▁materials	-2887
+vey	-2888
+▁express	-2889
+▁themselves	-2890
+▁published	-2891
+EL	-2892
+irit	-2893
+▁California	-2894
+ening	-2895
+▁president	-2896
+▁source	-2897
+ica	-2898
+▁reach	-2899
+▁Gener	-2900
+▁plant	-2901
+▁condition	-2902
+ples	-2903
+mission	-2904
+ashion	-2905
+orge	-2906
+urt	-2907
+▁sense	-2908
+▁fine	-2909
+▁streng	-2910
+apan	-2911
+ibrary	-2912
+www	-2913
+▁dry	-2914
+izes	-2915
+▁effective	-2916
+▁firm	-2917
+▁sale	-2918
+bum	-2919
+▁mid	-2920
+▁photo	-2921
+▁written	-2922
+▁types	-2923
+AP	-2924
+▁dise	-2925
+▁average	-2926
+▁interview	-2927
+rup	-2928
+urb	-2929
+rom	-2930
+▁consult	-2931
+▁AM	-2932
+▁Go	-2933
+▁countries	-2934
+▁Met	-2935
+▁positive	-2936
+ule	-2937
+▁remov	-2938
+▁multiple	-2939
+wide	-2940
+▁Rem	-2941
+▁Services	-2942
+iles	-2943
+ida	-2944
+gu	-2945
+ael	-2946
+▁lif	-2947
+arant	-2948
+▁Great	-2949
+▁join	-2950
+mm	-2951
+▁Je	-2952
+enty	-2953
+unk	-2954
+▁slow	-2955
+▁Spe	-2956
+▁India	-2957
+▁trip	-2958
+▁describ	-2959
+ube	-2960
+aches	-2961
+ength	-2962
+▁began	-2963
+ato	-2964
+▁interesting	-2965
+▁imm	-2966
+▁Mod	-2967
+▁images	-2968
+▁answer	-2969
+▁prem	-2970
+▁player	-2971
+▁cat	-2972
+add	-2973
+▁viol	-2974
+▁opportunities	-2975
+urer	-2976
+▁message	-2977
+▁Cle	-2978
+▁employees	-2979
+▁dream	-2980
+ography	-2981
+▁heat	-2982
+▁healthy	-2983
+ager	-2984
+▁Sch	-2985
+▁Why	-2986
+▁Thanks	-2987
+▁sites	-2988
+ration	-2989
+▁directly	-2990
+▁camer	-2991
+▁hour	-2992
+▁item	-2993
+rel	-2994
+rought	-2995
+▁document	-2996
+▁fans	-2997
+▁According	-2998
+bit	-2999
+orage	-3000
+press	-3001
+▁necessary	-3002
+itute	-3003
+▁picture	-3004
+▁achieve	-3005
+▁David	-3006
+IL	-3007
+▁copy	-3008
+▁Hot	-3009
+▁Av	-3010
+▁Program	-3011
+▁essential	-3012
+▁completely	-3013
+▁lic	-3014
+▁Sub	-3015
+▁gift	-3016
+▁Once	-3017
+▁tele	-3018
+▁band	-3019
+▁families	-3020
+▁stories	-3021
+sy	-3022
+▁prices	-3023
+▁groups	-3024
+duc	-3025
+▁Year	-3026
+olf	-3027
+▁Phot	-3028
+▁commercial	-3029
+▁King	-3030
+arlier	-3031
+▁Rec	-3032
+▁Whe	-3033
+▁Found	-3034
+▁Since	-3035
+▁reve	-3036
+elling	-3037
+▁offe	-3038
+▁goals	-3039
+ocol	-3040
+▁excellent	-3041
+▁div	-3042
+▁cert	-3043
+▁East	-3044
+▁Cr	-3045
+▁promot	-3046
+▁dru	-3047
+▁Even	-3048
+▁pull	-3049
+▁successful	-3050
+▁eye	-3051
+▁Market	-3052
+▁fully	-3053
+▁www	-3054
+▁growing	-3055
+ares	-3056
+itely	-3057
+▁Mag	-3058
+▁hor	-3059
+▁led	-3060
+▁itself	-3061
+itation	-3062
+▁Many	-3063
+▁Loc	-3064
+▁creating	-3065
+▁fix	-3066
+▁stru	-3067
+iant	-3068
+▁except	-3069
+▁adult	-3070
+▁traditional	-3071
+▁White	-3072
+▁comments	-3073
+▁gold	-3074
+▁paint	-3075
+▁separ	-3076
+oul	-3077
+erved	-3078
+▁Good	-3079
+▁fab	-3080
+▁aim	-3081
+coming	-3082
+▁neigh	-3083
+▁broad	-3084
+▁Germ	-3085
+▁Russ	-3086
+mb	-3087
+▁Green	-3088
+ancy	-3089
+iable	-3090
+▁birth	-3091
+onse	-3092
+▁propos	-3093
+omen	-3094
+▁fair	-3095
+▁cy	-3096
+ooth	-3097
+▁gar	-3098
+▁device	-3099
+BC	-3100
+▁reports	-3101
+uses	-3102
+anch	-3103
+▁Best	-3104
+▁block	-3105
+▁mount	-3106
+▁teams	-3107
+▁terms	-3108
+▁kitchen	-3109
+▁cross	-3110
+oms	-3111
+udd	-3112
+▁Spr	-3113
+▁stuff	-3114
+tee	-3115
+▁extreme	-3116
+▁dark	-3117
+ffee	-3118
+▁vehicle	-3119
+▁Last	-3120
+▁Jack	-3121
+▁attempt	-3122
+▁Each	-3123
+▁glass	-3124
+urning	-3125
+▁wasn	-3126
+▁applications	-3127
+ores	-3128
+venue	-3129
+▁hop	-3130
+▁saying	-3131
+▁floor	-3132
+hest	-3133
+▁wrong	-3134
+ey	-3135
+▁baby	-3136
+imately	-3137
+▁Tex	-3138
+▁dead	-3139
+ties	-3140
+uth	-3141
+▁Bra	-3142
+▁China	-3143
+▁thinking	-3144
+▁Port	-3145
+▁rev	-3146
+▁depend	-3147
+▁shoot	-3148
+▁Web	-3149
+▁Ty	-3150
+inner	-3151
+ipped	-3152
+▁blood	-3153
+ashington	-3154
+ecutive	-3155
+▁bi	-3156
+ald	-3157
+oming	-3158
+▁Twitter	-3159
+▁Develop	-3160
+OL	-3161
+istry	-3162
+▁mention	-3163
+▁See	-3164
+TM	-3165
+”.	-3166
+▁gave	-3167
+▁Japan	-3168
+aughter	-3169
+▁Hall	-3170
+▁smart	-3171
+▁System	-3172
+▁wait	-3173
+inary	-3174
+▁implement	-3175
+pite	-3176
+▁obs	-3177
+rote	-3178
+▁profession	-3179
+▁speed	-3180
+▁aware	-3181
+▁serve	-3182
+▁spend	-3183
+▁attract	-3184
+▁director	-3185
+▁organiz	-3186
+▁Bel	-3187
+▁offering	-3188
+iced	-3189
+▁section	-3190
+▁sen	-3191
+▁budget	-3192
+▁Association	-3193
+▁became	-3194
+▁farm	-3195
+aries	-3196
+ological	-3197
+▁impress	-3198
+▁distrib	-3199
+Ch	-3200
+rows	-3201
+▁Office	-3202
+▁ge	-3203
+▁Mor	-3204
+▁pictures	-3205
+▁nation	-3206
+▁college	-3207
+▁wish	-3208
+AD	-3209
+▁Pri	-3210
+▁correct	-3211
+▁Sol	-3212
+field	-3213
+overn	-3214
+▁Make	-3215
+▁suit	-3216
+▁IN	-3217
+▁effort	-3218
+▁Mem	-3219
+▁developed	-3220
+▁places	-3221
+▁moving	-3222
+▁conduct	-3223
+▁coun	-3224
+▁tal	-3225
+▁carry	-3226
+▁dog	-3227
+▁limited	-3228
+▁individuals	-3229
+▁advice	-3230
+ils	-3231
+▁dro	-3232
+vest	-3233
+▁son	-3234
+pre	-3235
+▁rent	-3236
+▁avoid	-3237
+▁spent	-3238
+yond	-3239
+ications	-3240
+zy	-3241
+▁complex	-3242
+▁Paul	-3243
+▁defe	-3244
+lock	-3245
+▁bath	-3246
+▁title	-3247
+▁sleep	-3248
+▁situation	-3249
+▁Down	-3250
+▁Road	-3251
+idered	-3252
+▁requirements	-3253
+▁album	-3254
+▁progress	-3255
+▁delivery	-3256
+ceed	-3257
+▁Today	-3258
+▁jud	-3259
+▁Washington	-3260
+▁cas	-3261
+▁Vis	-3262
+▁Educ	-3263
+▁Inter	-3264
+▁vot	-3265
+▁construction	-3266
+rench	-3267
+riend	-3268
+▁enh	-3269
+▁Public	-3270
+ibly	-3271
+▁About	-3272
+house	-3273
+haps	-3274
+▁ble	-3275
+word	-3276
+▁Canada	-3277
+▁advant	-3278
+▁wants	-3279
+▁Top	-3280
+▁statement	-3281
+▁feet	-3282
+▁Use	-3283
+▁schools	-3284
+▁Gold	-3285
+▁war	-3286
+down	-3287
+▁race	-3288
+useum	-3289
+▁heard	-3290
+▁convers	-3291
+▁eat	-3292
+▁Find	-3293
+US	-3294
+▁sometimes	-3295
+▁sweet	-3296
+▁Director	-3297
+▁AN	-3298
+▁nut	-3299
+▁stress	-3300
+▁billion	-3301
+reci	-3302
+▁Lear	-3303
+▁quarter	-3304
+▁physical	-3305
+▁felt	-3306
+ancing	-3307
+▁hous	-3308
+PS	-3309
+▁Indian	-3310
+▁hotel	-3311
+▁Mac	-3312
+itary	-3313
+▁towards	-3314
+▁consist	-3315
+▁stage	-3316
+▁spot	-3317
+▁annual	-3318
+▁shop	-3319
+▁shot	-3320
+▁strateg	-3321
+▁Flor	-3322
+▁wonderful	-3323
+ports	-3324
+porate	-3325
+▁Open	-3326
+▁loved	-3327
+▁region	-3328
+▁ing	-3329
+▁path	-3330
+▁Dem	-3331
+▁feeling	-3332
+▁owners	-3333
+▁finish	-3334
+▁ver	-3335
+▁Pal	-3336
+▁THE	-3337
+▁aff	-3338
+unte	-3339
+▁mat	-3340
+ari	-3341
+▁eyes	-3342
+▁pattern	-3343
+▁Council	-3344
+▁finally	-3345
+isions	-3346
+▁lik	-3347
+ctions	-3348
+▁ten	-3349
+▁brought	-3350
+ION	-3351
+▁Texas	-3352
+▁language	-3353
+▁wife	-3354
+▁Care	-3355
+▁pet	-3356
+▁interact	-3357
+▁partner	-3358
+▁sports	-3359
+▁straight	-3360
+rast	-3361
+▁inform	-3362
+▁Dan	-3363
+▁nature	-3364
+ads	-3365
+▁investment	-3366
+▁Club	-3367
+roid	-3368
+▁respond	-3369
+▁concept	-3370
+▁nearly	-3371
+owl	-3372
+dule	-3373
+▁helping	-3374
+▁Hel	-3375
+▁Class	-3376
+▁exerc	-3377
+▁overall	-3378
+▁star	-3379
+▁Bre	-3380
+▁categ	-3381
+▁weather	-3382
+▁ult	-3383
+▁Apple	-3384
+▁max	-3385
+▁tried	-3386
+▁guide	-3387
+▁blue	-3388
+▁William	-3389
+end	-3390
+▁temper	-3391
+estival	-3392
+▁pow	-3393
+▁collabor	-3394
+▁largest	-3395
+▁Court	-3396
+".	-3397
+ened	-3398
+▁demand	-3399
+▁charge	-3400
+▁independ	-3401
+▁client	-3402
+hips	-3403
+▁Board	-3404
+As	-3405
+▁rock	-3406
+▁Time	-3407
+itect	-3408
+ourney	-3409
+▁wear	-3410
+change	-3411
+▁Oh	-3412
+ament	-3413
+▁pred	-3414
+He	-3415
+▁advert	-3416
+▁definitely	-3417
+mitted	-3418
+▁appoint	-3419
+▁wrote	-3420
+▁candid	-3421
+▁activity	-3422
+▁gas	-3423
+▁seven	-3424
+▁Windows	-3425
+rences	-3426
+▁Ann	-3427
+▁Ir	-3428
+▁cold	-3429
+rig	-3430
+aly	-3431
+▁benefit	-3432
+ago	-3433
+▁Internet	-3434
+▁offered	-3435
+inger	-3436
+roud	-3437
+asc	-3438
+▁Australia	-3439
+yd	-3440
+▁acqu	-3441
+▁influ	-3442
+▁response	-3443
+▁turned	-3444
+▁Ant	-3445
+wise	-3446
+▁double	-3447
+▁miles	-3448
+▁Review	-3449
+▁pieces	-3450
+▁uses	-3451
+▁Tom	-3452
+last	-3453
+ounds	-3454
+▁earlier	-3455
+▁devices	-3456
+▁Fam	-3457
+▁internet	-3458
+uted	-3459
+▁beginning	-3460
+▁thous	-3461
+ned	-3462
+▁considered	-3463
+▁ahead	-3464
+lies	-3465
+▁altern	-3466
+▁appreci	-3467
+ails	-3468
+▁grand	-3469
+▁reduce	-3470
+▁exactly	-3471
+▁Adv	-3472
+▁histor	-3473
+▁View	-3474
+▁prec	-3475
+▁Research	-3476
+▁James	-3477
+bon	-3478
+▁wedding	-3479
+▁active	-3480
+▁homes	-3481
+▁imag	-3482
+▁entertain	-3483
+arc	-3484
+▁Michael	-3485
+▁paid	-3486
+ategy	-3487
+▁doll	-3488
+ustain	-3489
+▁transport	-3490
+▁difference	-3491
+▁belie	-3492
+▁Thank	-3493
+icks	-3494
+olute	-3495
+▁political	-3496
+▁IT	-3497
+▁regul	-3498
+▁challenge	-3499
+▁served	-3500
+▁supply	-3501
+▁cho	-3502
+more	-3503
+▁surround	-3504
+ampions	-3505
+▁Micro	-3506
+▁finished	-3507
+▁Rich	-3508
+▁Have	-3509
+icate	-3510
+OV	-3511
+▁Big	-3512
+umn	-3513
+ading	-3514
+You	-3515
+agn	-3516
+▁Rel	-3517
+▁cash	-3518
+▁Look	-3519
+▁creative	-3520
+cause	-3521
+▁eight	-3522
+estern	-3523
+ston	-3524
+▁understanding	-3525
+▁retail	-3526
+▁replace	-3527
+▁Govern	-3528
+icip	-3529
+▁states	-3530
+LE	-3531
+ying	-3532
+:||	-3533
+▁Cur	-3534
+▁Mark	-3535
+▁rates	-3536
+orrow	-3537
+mod	-3538
+▁culture	-3539
+▁Char	-3540
+antly	-3541
+ky	-3542
+vin	-3543
+oly	-3544
+▁European	-3545
+▁Super	-3546
+▁lots	-3547
+▁guarant	-3548
+▁easier	-3549
+▁experienced	-3550
+▁ST	-3551
+▁afford	-3552
+▁Call	-3553
+box	-3554
+▁pages	-3555
+▁Life	-3556
+▁hus	-3557
+dd	-3558
+▁bottom	-3559
+place	-3560
+▁expand	-3561
+iny	-3562
+▁truly	-3563
+sec	-3564
+▁father	-3565
+▁pressure	-3566
+▁maybe	-3567
+▁flav	-3568
+hens	-3569
+▁economic	-3570
+ales	-3571
+▁thank	-3572
+▁reflect	-3573
+inated	-3574
+▁machine	-3575
+ses	-3576
+▁Company	-3577
+error	-3578
+rial	-3579
+▁analysis	-3580
+amic	-3581
+icious	-3582
+▁fat	-3583
+▁IS	-3584
+▁immediately	-3585
+▁emot	-3586
+▁named	-3587
+alt	-3588
+aled	-3589
+▁gradu	-3590
+▁numbers	-3591
+sych	-3592
+het	-3593
+▁tom	-3594
+▁Child	-3595
+▁Det	-3596
+▁Angel	-3597
+▁demon	-3598
+▁girls	-3599
+▁exhib	-3600
+rey	-3601
+▁prot	-3602
+▁comfortable	-3603
+IP	-3604
+erry	-3605
+pa	-3606
+▁assess	-3607
+▁posted	-3608
+▁satis	-3609
+nown	-3610
+▁degree	-3611
+▁tips	-3612
+chan	-3613
+▁helped	-3614
+▁damage	-3615
+ivil	-3616
+▁Ev	-3617
+▁opening	-3618
+▁Management	-3619
+▁garden	-3620
+▁dating	-3621
+▁Bank	-3622
+▁videos	-3623
+▁contain	-3624
+▁obt	-3625
+▁wild	-3626
+▁PC	-3627
+ronic	-3628
+care	-3629
+▁storage	-3630
+▁Bay	-3631
+▁Ret	-3632
+▁speak	-3633
+▁behav	-3634
+phone	-3635
+▁subst	-3636
+▁remain	-3637
+force	-3638
+anging	-3639
+▁Plan	-3640
+▁trade	-3641
+▁launch	-3642
+undred	-3643
+rem	-3644
+▁reviews	-3645
+▁completed	-3646
+▁Ins	-3647
+▁II	-3648
+ico	-3649
+▁pool	-3650
+▁Sun	-3651
+▁Island	-3652
+▁beyond	-3653
+amm	-3654
+▁lack	-3655
+▁disease	-3656
+asy	-3657
+▁lock	-3658
+▁Sing	-3659
+▁Rock	-3660
+set	-3661
+▁threat	-3662
+▁purpose	-3663
+If	-3664
+tion	-3665
+▁Water	-3666
+order	-3667
+orial	-3668
+▁cards	-3669
+▁Contact	-3670
+ado	-3671
+▁adjust	-3672
+▁Mart	-3673
+dom	-3674
+que	-3675
+▁ter	-3676
+▁spread	-3677
+▁accur	-3678
+▁existing	-3679
+▁fashion	-3680
+arily	-3681
+▁knew	-3682
+▁decor	-3683
+▁Love	-3684
+▁fant	-3685
+▁Jes	-3686
+▁highest	-3687
+▁cancer	-3688
+Re	-3689
+lied	-3690
+▁Florida	-3691
+▁plus	-3692
+OW	-3693
+▁craft	-3694
+▁jobs	-3695
+soft	-3696
+▁Although	-3697
+met	-3698
+▁conference	-3699
+▁Rob	-3700
+body	-3701
+▁Win	-3702
+▁responsible	-3703
+▁increasing	-3704
+▁Sur	-3705
+▁During	-3706
+▁allowed	-3707
+aling	-3708
+▁train	-3709
+▁setting	-3710
+▁excited	-3711
+atever	-3712
+▁prefer	-3713
+rapy	-3714
+▁driving	-3715
+▁camera	-3716
+▁proud	-3717
+door	-3718
+▁increased	-3719
+▁Sa	-3720
+▁sty	-3721
+imal	-3722
+▁welcome	-3723
+▁lines	-3724
+▁himself	-3725
+▁middle	-3726
+▁initial	-3727
+▁appropri	-3728
+▁Dec	-3729
+▁proced	-3730
+ona	-3731
+aith	-3732
+ences	-3733
+▁fem	-3734
+illa	-3735
+▁Sum	-3736
+▁Church	-3737
+▁certainly	-3738
+▁General	-3739
+▁passion	-3740
+▁frame	-3741
+▁furn	-3742
+▁coffee	-3743
+cel	-3744
+▁strugg	-3745
+▁journey	-3746
+▁Product	-3747
+▁holiday	-3748
+iling	-3749
+▁files	-3750
+▁Community	-3751
+▁Camp	-3752
+▁estate	-3753
+▁effects	-3754
+▁er	-3755
+za	-3756
+fl	-3757
+▁husband	-3758
+▁thanks	-3759
+▁Back	-3760
+▁frequ	-3761
+▁cast	-3762
+▁ingred	-3763
+aming	-3764
+▁steps	-3765
+▁button	-3766
+▁Republic	-3767
+▁length	-3768
+▁update	-3769
+▁People	-3770
+▁pen	-3771
+▁Custom	-3772
+▁born	-3773
+ologies	-3774
+▁normal	-3775
+istics	-3776
+▁efforts	-3777
+▁selection	-3778
+▁Two	-3779
+▁Education	-3780
+▁changed	-3781
+ously	-3782
+▁Mary	-3783
+▁batter	-3784
+▁Cong	-3785
+net	-3786
+▁secure	-3787
+▁mission	-3788
+vant	-3789
+▁cru	-3790
+anta	-3791
+▁spirit	-3792
+▁dedicated	-3793
+▁bill	-3794
+▁owner	-3795
+▁clin	-3796
+▁relax	-3797
+▁surv	-3798
+▁shopping	-3799
+▁looked	-3800
+lying	-3801
+icken	-3802
+ken	-3803
+▁incred	-3804
+▁occas	-3805
+▁stream	-3806
+ovel	-3807
+▁moved	-3808
+▁Show	-3809
+ady	-3810
+▁links	-3811
+▁mis	-3812
+omb	-3813
+nection	-3814
+▁Cap	-3815
+▁science	-3816
+ij	-3817
+EM	-3818
+▁aspect	-3819
+▁protection	-3820
+):	-3821
+oma	-3822
+▁haven	-3823
+fit	-3824
+▁wine	-3825
+▁powerful	-3826
+▁French	-3827
+othing	-3828
+▁extend	-3829
+▁evening	-3830
+▁demonstr	-3831
+▁instruct	-3832
+▁Take	-3833
+▁meaning	-3834
+▁background	-3835
+▁Like	-3836
+oos	-3837
+ipp	-3838
+▁occur	-3839
+▁talking	-3840
+▁patient	-3841
+▁produce	-3842
+IV	-3843
+▁particularly	-3844
+nded	-3845
+▁USA	-3846
+enance	-3847
+▁aren	-3848
+▁guys	-3849
+porary	-3850
+reed	-3851
+friend	-3852
+▁measure	-3853
+▁Power	-3854
+▁Sil	-3855
+▁opin	-3856
+▁basic	-3857
+▁challenges	-3858
+▁alone	-3859
+ota	-3860
+▁Under	-3861
+▁Online	-3862
+▁fan	-3863
+DA	-3864
+▁cream	-3865
+ocr	-3866
+▁payment	-3867
+▁biggest	-3868
+▁transfer	-3869
+▁rules	-3870
+▁Gra	-3871
+▁doub	-3872
+▁session	-3873
+CC	-3874
+itiz	-3875
+▁shared	-3876
+▁fill	-3877
+leg	-3878
+▁spring	-3879
+▁fra	-3880
+▁winter	-3881
+▁sort	-3882
+▁Project	-3883
+range	-3884
+▁runs	-3885
+▁whose	-3886
+▁letter	-3887
+▁basis	-3888
+▁couldn	-3889
+IM	-3890
+▁coach	-3891
+▁federal	-3892
+▁Information	-3893
+▁Special	-3894
+azine	-3895
+annel	-3896
+▁bur	-3897
+▁schedule	-3898
+▁liter	-3899
+free	-3900
+▁organizations	-3901
+▁Pet	-3902
+▁Because	-3903
+▁manager	-3904
+ios	-3905
+istrict	-3906
+▁leader	-3907
+see	-3908
+▁Phil	-3909
+icing	-3910
+▁drop	-3911
+▁Who	-3912
+▁models	-3913
+▁electric	-3914
+▁strength	-3915
+▁Music	-3916
+▁artist	-3917
+acity	-3918
+uing	-3919
+▁church	-3920
+isl	-3921
+▁peace	-3922
+▁reasons	-3923
+uled	-3924
+esome	-3925
+▁Food	-3926
+▁egg	-3927
+▁Lake	-3928
+▁slight	-3929
+iques	-3930
+▁absolute	-3931
+▁capital	-3932
+▁communities	-3933
+▁sugar	-3934
+▁volunte	-3935
+▁extremely	-3936
+▁Star	-3937
+▁adding	-3938
+▁competition	-3939
+iture	-3940
+▁exclus	-3941
+▁guests	-3942
+▁instit	-3943
+▁onto	-3944
+▁views	-3945
+▁unit	-3946
+▁mer	-3947
+▁stick	-3948
+▁British	-3949
+▁shown	-3950
+▁regarding	-3951
+istered	-3952
+▁Follow	-3953
+vision	-3954
+iation	-3955
+▁residents	-3956
+▁Sam	-3957
+▁Ve	-3958
+▁Thom	-3959
+rief	-3960
+gency	-3961
+▁Profess	-3962
+▁hundred	-3963
+▁voice	-3964
+▁conven	-3965
+▁Miss	-3966
+umber	-3967
+hone	-3968
+▁Enter	-3969
+azon	-3970
+la	-3971
+▁seeing	-3972
+▁River	-3973
+▁chem	-3974
+▁taste	-3975
+▁ideal	-3976
+▁strategy	-3977
+apter	-3978
+▁Mil	-3979
+▁Yes	-3980
+▁scient	-3981
+▁followed	-3982
+▁AP	-3983
+▁Dri	-3984
+▁Blue	-3985
+ustr	-3986
+▁daughter	-3987
+▁Real	-3988
+eria	-3989
+▁colors	-3990
+oyal	-3991
+▁heavy	-3992
+▁Institute	-3993
+▁trou	-3994
+▁compon	-3995
+▁sched	-3996
+▁Att	-3997
+▁cry	-3998
+osing	-3999
+▁brother	-4000
+▁gone	-4001
+▁advantage	-4002
+imb	-4003
+▁notice	-4004
+rian	-4005
+▁Lou	-4006
+▁guid	-4007
+esterday	-4008
+▁manage	-4009
+oman	-4010
+▁score	-4011
+▁Matt	-4012
+▁characters	-4013
+▁virt	-4014
+ags	-4015
+standing	-4016
+▁Fire	-4017
+▁Police	-4018
+▁Fore	-4019
+iverse	-4020
+▁traffic	-4021
+asp	-4022
+▁window	-4023
+▁surface	-4024
+▁ton	-4025
+ocolate	-4026
+term	-4027
+▁Mount	-4028
+▁experiences	-4029
+▁Pay	-4030
+▁smooth	-4031
+ette	-4032
+▁happened	-4033
+▁Mal	-4034
+▁reb	-4035
+▁Ben	-4036
+fast	-4037
+▁graph	-4038
+▁hom	-4039
+▁Vol	-4040
+▁names	-4041
+▁identify	-4042
+encies	-4043
+▁shipping	-4044
+▁pair	-4045
+▁standards	-4046
+▁senior	-4047
+Sh	-4048
+▁Wood	-4049
+ech	-4050
+icine	-4051
+acing	-4052
+gen	-4053
+mark	-4054
+▁talent	-4055
+▁u	-4056
+itude	-4057
+▁District	-4058
+BS	-4059
+▁hospital	-4060
+▁professionals	-4061
+▁List	-4062
+raw	-4063
+▁initi	-4064
+uce	-4065
+▁breat	-4066
+▁although	-4067
+▁classic	-4068
+▁workers	-4069
+▁experts	-4070
+ula	-4071
+ixt	-4072
+TS	-4073
+▁luck	-4074
+gn	-4075
+▁Step	-4076
+▁Hist	-4077
+▁audience	-4078
+▁covered	-4079
+▁Est	-4080
+▁laws	-4081
+ero	-4082
+▁Mot	-4083
+▁Sign	-4084
+▁passed	-4085
+▁waiting	-4086
+▁academ	-4087
+▁guy	-4088
+▁dang	-4089
+▁beauty	-4090
+rooms	-4091
+▁fear	-4092
+▁approx	-4093
+▁continues	-4094
+▁Development	-4095
+▁finding	-4096
+▁Team	-4097
+▁snow	-4098
+▁flex	-4099
+▁efficient	-4100
+orney	-4101
+▁master	-4102
+▁mail	-4103
+▁associated	-4104
+▁exciting	-4105
+▁eval	-4106
+▁Elect	-4107
+inese	-4108
+▁Exper	-4109
+▁compared	-4110
+inate	-4111
+ga	-4112
+▁larger	-4113
+▁Chic	-4114
+ss	-4115
+▁critical	-4116
+▁laun	-4117
+sequ	-4118
+▁cars	-4119
+▁rob	-4120
+▁Color	-4121
+▁cab	-4122
+▁technical	-4123
+▁Family	-4124
+▁trail	-4125
+icon	-4126
+▁ice	-4127
+UR	-4128
+▁shape	-4129
+▁beg	-4130
+▁district	-4131
+▁keeping	-4132
+▁TO	-4133
+▁remind	-4134
+▁solid	-4135
+▁den	-4136
+osh	-4137
+▁Foundation	-4138
+▁England	-4139
+▁Science	-4140
+▁facilities	-4141
+▁boo	-4142
+rees	-4143
+▁wat	-4144
+▁calls	-4145
+▁restaurant	-4146
+▁scene	-4147
+▁maintain	-4148
+▁greater	-4149
+▁PR	-4150
+▁Engine	-4151
+▁sustain	-4152
+▁officials	-4153
+▁sy	-4154
+mail	-4155
+▁Alex	-4156
+▁Bet	-4157
+▁Sl	-4158
+▁Jesus	-4159
+▁posts	-4160
+▁station	-4161
+▁friendly	-4162
+▁epis	-4163
+▁Str	-4164
+▁driver	-4165
+▁sand	-4166
+▁bul	-4167
+▁listed	-4168
+▁recipe	-4169
+▁plenty	-4170
+▁Glo	-4171
+▁forget	-4172
+odes	-4173
+▁Vir	-4174
+▁fish	-4175
+▁older	-4176
+illage	-4177
+cul	-4178
+▁rich	-4179
+▁Start	-4180
+▁continued	-4181
+▁football	-4182
+incip	-4183
+▁package	-4184
+▁developing	-4185
+itors	-4186
+log	-4187
+▁Hum	-4188
+▁established	-4189
+yer	-4190
+iller	-4191
+▁Brown	-4192
+rowd	-4193
+▁income	-4194
+▁useful	-4195
+▁minute	-4196
+▁truck	-4197
+well	-4198
+▁studies	-4199
+▁advent	-4200
+▁announce	-4201
+oop	-4202
+▁learned	-4203
+ervation	-4204
+▁Press	-4205
+atically	-4206
+▁disapp	-4207
+▁tim	-4208
+▁produced	-4209
+win	-4210
+▁motor	-4211
+tra	-4212
+▁League	-4213
+using	-4214
+▁rooms	-4215
+unately	-4216
+▁closed	-4217
+▁beat	-4218
+▁handle	-4219
+▁appropriate	-4220
+▁Whether	-4221
+▁classes	-4222
+unning	-4223
+▁origin	-4224
+▁military	-4225
+ander	-4226
+▁Central	-4227
+▁artists	-4228
+▁died	-4229
+gal	-4230
+▁Commission	-4231
+▁explore	-4232
+▁sup	-4233
+▁placed	-4234
+▁Offic	-4235
+CA	-4236
+▁economy	-4237
+▁kept	-4238
+▁thousands	-4239
+night	-4240
+▁knows	-4241
+▁Franc	-4242
+▁connection	-4243
+▁winning	-4244
+▁Smith	-4245
+▁remove	-4246
+▁pros	-4247
+▁Social	-4248
+▁evidence	-4249
+▁force	-4250
+▁primary	-4251
+▁CEO	-4252
+▁Media	-4253
+▁adop	-4254
+▁tree	-4255
+▁repair	-4256
+▁salt	-4257
+▁Build	-4258
+▁bright	-4259
+aded	-4260
+▁novel	-4261
+▁testing	-4262
+▁Download	-4263
+iment	-4264
+IG	-4265
+▁Christian	-4266
+▁operations	-4267
+▁util	-4268
+rael	-4269
+▁status	-4270
+▁opened	-4271
+▁figure	-4272
+▁requires	-4273
+BA	-4274
+▁street	-4275
+▁discount	-4276
+▁fol	-4277
+There	-4278
+▁Another	-4279
+▁gun	-4280
+▁communication	-4281
+atab	-4282
+ipes	-4283
+▁presented	-4284
+▁Grand	-4285
+rd	-4286
+▁decl	-4287
+▁Beach	-4288
+▁discover	-4289
+ka	-4290
+What	-4291
+▁Obama	-4292
+overy	-4293
+▁ingredients	-4294
+▁teaching	-4295
+▁surg	-4296
+▁medium	-4297
+▁Network	-4298
+▁injury	-4299
+inn	-4300
+▁Arch	-4301
+semb	-4302
+▁harm	-4303
+▁starts	-4304
+vention	-4305
+oe	-4306
+▁brain	-4307
+bed	-4308
+▁Carol	-4309
+▁catch	-4310
+▁contains	-4311
+iled	-4312
+▁selected	-4313
+irection	-4314
+▁shall	-4315
+▁Mex	-4316
+outhern	-4317
+▁sharing	-4318
+▁brings	-4319
+look	-4320
+action	-4321
+▁butter	-4322
+arge	-4323
+▁doctor	-4324
+idential	-4325
+▁Disc	-4326
+▁structure	-4327
+▁advance	-4328
+itar	-4329
+ideo	-4330
+▁poor	-4331
+rehens	-4332
+▁scen	-4333
+men	-4334
+▁famous	-4335
+asure	-4336
+▁pray	-4337
+▁dinner	-4338
+mp	-4339
+▁arrest	-4340
+apers	-4341
+pective	-4342
+▁Dig	-4343
+▁prepared	-4344
+olic	-4345
+▁esc	-4346
+▁Scott	-4347
+▁Hill	-4348
+▁manufacturer	-4349
+▁suff	-4350
+enses	-4351
+▁Mad	-4352
+▁Word	-4353
+▁pm	-4354
+▁serving	-4355
+▁Microsoft	-4356
+▁jump	-4357
+▁Card	-4358
+▁ship	-4359
+▁loan	-4360
+▁architect	-4361
+▁Light	-4362
+uries	-4363
+▁Full	-4364
+▁department	-4365
+▁mo	-4366
+▁remains	-4367
+▁funds	-4368
+▁Valley	-4369
+▁vision	-4370
+▁watching	-4371
+▁secret	-4372
+▁rank	-4373
+atively	-4374
+▁victim	-4375
+PA	-4376
+▁sto	-4377
+▁Amazon	-4378
+▁resist	-4379
+▁Cup	-4380
+ini	-4381
+ctors	-4382
+▁veget	-4383
+▁gain	-4384
+▁Chicago	-4385
+aven	-4386
+▁Their	-4387
+noon	-4388
+▁methods	-4389
+▁balance	-4390
+usion	-4391
+lor	-4392
+iers	-4393
+▁agency	-4394
+allery	-4395
+▁updated	-4396
+▁buying	-4397
+▁movement	-4398
+”,	-4399
+riage	-4400
+▁leaves	-4401
+CH	-4402
+▁Keep	-4403
+▁Bill	-4404
+▁drug	-4405
+▁compl	-4406
+▁Chinese	-4407
+▁guess	-4408
+▁Support	-4409
+ooper	-4410
+▁Net	-4411
+RA	-4412
+aked	-4413
+▁encourage	-4414
+▁Stand	-4415
+▁spending	-4416
+▁cloud	-4417
+▁journal	-4418
+▁map	-4419
+▁OF	-4420
+▁Week	-4421
+▁reality	-4422
+lands	-4423
+▁Award	-4424
+going	-4425
+ption	-4426
+ishes	-4427
+▁Africa	-4428
+LC	-4429
+▁properties	-4430
+okes	-4431
+lastname	-4432
+eless	-4433
+▁beach	-4434
+▁becoming	-4435
+▁happens	-4436
+▁Date	-4437
+▁Ber	-4438
+ellig	-4439
+▁bought	-4440
+top	-4441
+▁sector	-4442
+▁cleaning	-4443
+▁Women	-4444
+▁spons	-4445
+▁RE	-4446
+▁ID	-4447
+▁Mel	-4448
+▁leaving	-4449
+▁sport	-4450
+iency	-4451
+▁relig	-4452
+▁Commit	-4453
+▁showing	-4454
+antic	-4455
+▁plants	-4456
+itness	-4457
+life	-4458
+▁maintenance	-4459
+▁https	-4460
+▁facility	-4461
+▁metal	-4462
+▁Fort	-4463
+▁Tor	-4464
+ception	-4465
+▁perhaps	-4466
+▁dep	-4467
+▁Times	-4468
+essions	-4469
+hem	-4470
+ki	-4471
+▁determine	-4472
+ifts	-4473
+▁leadership	-4474
+▁Long	-4475
+▁advanced	-4476
+▁worksh	-4477
+▁Israel	-4478
+▁independent	-4479
+▁stores	-4480
+▁entry	-4481
+▁Rad	-4482
+▁Academ	-4483
+▁Android	-4484
+▁cris	-4485
+▁mechan	-4486
+▁fee	-4487
+▁analy	-4488
+▁Where	-4489
+▁rain	-4490
+berg	-4491
+edy	-4492
+▁upgr	-4493
+▁rare	-4494
+osure	-4495
+▁unc	-4496
+outs	-4497
+▁cart	-4498
+▁Que	-4499
+▁exercise	-4500
+▁wouldn	-4501
+▁committed	-4502
+abilities	-4503
+ror	-4504
+▁faith	-4505
+itz	-4506
+▁NY	-4507
+▁meant	-4508
+alls	-4509
+▁vote	-4510
+▁sem	-4511
+▁iPhone	-4512
+▁Mass	-4513
+ograp	-4514
+▁mist	-4515
+▁bird	-4516
+craft	-4517
+▁Both	-4518
+▁fabric	-4519
+▁designs	-4520
+▁Tim	-4521
+▁numerous	-4522
+▁ride	-4523
+▁focused	-4524
+▁anti	-4525
+▁markets	-4526
+▁Div	-4527
+▁brows	-4528
+▁Nov	-4529
+▁ju	-4530
+▁incor	-4531
+▁Fil	-4532
+fr	-4533
+▁signed	-4534
+agram	-4535
+▁sources	-4536
+▁Pub	-4537
+▁records	-4538
+**	-4539
+▁funding	-4540
+▁theme	-4541
+▁actual	-4542
+aturing	-4543
+iest	-4544
+▁establish	-4545
+▁changing	-4546
+▁chair	-4547
+ae	-4548
+▁visitors	-4549
+▁steel	-4550
+▁visual	-4551
+▁multi	-4552
+▁ir	-4553
+For	-4554
+estic	-4555
+▁Next	-4556
+MS	-4557
+▁Los	-4558
+▁forms	-4559
+iences	-4560
+▁crowd	-4561
+iance	-4562
+▁joined	-4563
+▁Organ	-4564
+isation	-4565
+▁mill	-4566
+▁coverage	-4567
+▁elements	-4568
+▁showed	-4569
+rim	-4570
+▁kick	-4571
+▁selling	-4572
+▁Watch	-4573
+▁practices	-4574
+▁animals	-4575
+▁operating	-4576
+▁obvious	-4577
+fin	-4578
+▁menu	-4579
+▁busy	-4580
+▁Nor	-4581
+▁capacity	-4582
+▁locations	-4583
+▁grant	-4584
+▁Medical	-4585
+▁songs	-4586
+▁fell	-4587
+▁Set	-4588
+▁neighbor	-4589
+▁roof	-4590
+▁refer	-4591
+▁Head	-4592
+isher	-4593
+eared	-4594
+▁George	-4595
+oor	-4596
+miss	-4597
+▁memory	-4598
+▁raised	-4599
+▁Only	-4600
+rics	-4601
+▁worry	-4602
+▁whatever	-4603
+▁corner	-4604
+▁ban	-4605
+▁lose	-4606
+▁allowing	-4607
+igan	-4608
+▁listen	-4609
+IA	-4610
+▁central	-4611
+reek	-4612
+▁plastic	-4613
+▁society	-4614
+▁accommod	-4615
+gage	-4616
+vere	-4617
+▁relationships	-4618
+SS	-4619
+▁Tri	-4620
+▁diet	-4621
+igation	-4622
+▁lux	-4623
+▁diagn	-4624
+▁thr	-4625
+▁managed	-4626
+▁Copy	-4627
+OP	-4628
+▁updates	-4629
+▁limit	-4630
+▁caused	-4631
+▁estim	-4632
+▁rap	-4633
+▁parking	-4634
+▁population	-4635
+▁tables	-4636
+▁Before	-4637
+ya	-4638
+▁Note	-4639
+▁uns	-4640
+",	-4641
+fol	-4642
+▁parties	-4643
+▁decide	-4644
+isco	-4645
+uty	-4646
+▁claims	-4647
+▁articles	-4648
+▁core	-4649
+ano	-4650
+▁survey	-4651
+▁repe	-4652
+▁Mer	-4653
+ferences	-4654
+▁assistance	-4655
+amin	-4656
+▁walking	-4657
+▁tickets	-4658
+▁Its	-4659
+▁techniques	-4660
+▁thoughts	-4661
+ection	-4662
+▁CD	-4663
+rab	-4664
+ivered	-4665
+▁Sy	-4666
+▁afternoon	-4667
+▁colour	-4668
+▁documents	-4669
+▁wire	-4670
+arrant	-4671
+▁bowl	-4672
+▁ended	-4673
+▁transl	-4674
+▁youth	-4675
+▁brown	-4676
+▁combination	-4677
+▁vehicles	-4678
+lines	-4679
+▁flat	-4680
+▁forum	-4681
+▁yesterday	-4682
+▁previously	-4683
+▁Game	-4684
+▁enjoyed	-4685
+▁landsc	-4686
+▁Society	-4687
+▁profile	-4688
+▁courses	-4689
+iliar	-4690
+▁launched	-4691
+▁toward	-4692
+▁appears	-4693
+DF	-4694
+▁eating	-4695
+point	-4696
+▁sea	-4697
+▁Bur	-4698
+▁Town	-4699
+▁accident	-4700
+▁Cre	-4701
+▁awesome	-4702
+▁filled	-4703
+▁optim	-4704
+▁teacher	-4705
+coh	-4706
+▁factors	-4707
+bour	-4708
+eed	-4709
+▁Chris	-4710
+▁Technology	-4711
+▁temperature	-4712
+rs	-4713
+▁micro	-4714
+▁mort	-4715
+pan	-4716
+▁psych	-4717
+while	-4718
+▁generally	-4719
+▁putting	-4720
+▁shel	-4721
+▁charges	-4722
+▁Learn	-4723
+▁Mont	-4724
+▁Trump	-4725
+▁citiz	-4726
+▁Atl	-4727
+▁notes	-4728
+▁smaller	-4729
+▁Author	-4730
+▁firstname	-4731
+▁Pack	-4732
+▁direction	-4733
+▁values	-4734
+▁task	-4735
+no	-4736
+rehensive	-4737
+▁counter	-4738
+▁Lord	-4739
+▁Log	-4740
+▁Wil	-4741
+▁AL	-4742
+▁outdoor	-4743
+▁CA	-4744
+▁Sand	-4745
+▁earth	-4746
+▁kid	-4747
+▁teachers	-4748
+▁panel	-4749
+▁becomes	-4750
+▁vs	-4751
+▁tend	-4752
+▁corporate	-4753
+orthern	-4754
+▁favour	-4755
+ola	-4756
+▁bon	-4757
+▁Arts	-4758
+▁Virgin	-4759
+▁century	-4760
+▁honest	-4761
+▁separate	-4762
+▁legisl	-4763
+??	-4764
+▁cheese	-4765
+▁Security	-4766
+▁assign	-4767
+yan	-4768
+▁Congress	-4769
+▁matt	-4770
+On	-4771
+▁sch	-4772
+▁truth	-4773
+▁purs	-4774
+▁concerns	-4775
+OD	-4776
+▁situ	-4777
+▁Committee	-4778
+▁Main	-4779
+istan	-4780
+▁Data	-4781
+▁helpful	-4782
+▁dur	-4783
+▁shut	-4784
+▁Jew	-4785
+New	-4786
+▁swim	-4787
+▁Centre	-4788
+iration	-4789
+▁missing	-4790
+▁orders	-4791
+▁fold	-4792
+▁Jul	-4793
+▁Frank	-4794
+▁milk	-4795
+rain	-4796
+▁McC	-4797
+een	-4798
+▁Government	-4799
+▁flu	-4800
+▁throw	-4801
+!!!	-4802
+po	-4803
+▁Ext	-4804
+▁adapt	-4805
+▁polic	-4806
+▁innovative	-4807
+▁installation	-4808
+ownt	-4809
+▁Aud	-4810
+▁ur	-4811
+▁south	-4812
+▁relevant	-4813
+▁Lo	-4814
+▁tow	-4815
+▁van	-4816
+pet	-4817
+ifying	-4818
+olars	-4819
+rical	-4820
+▁Robert	-4821
+SP	-4822
+▁Museum	-4823
+▁decisions	-4824
+▁environmental	-4825
+ye	-4826
+▁discussion	-4827
+▁despite	-4828
+▁waste	-4829
+▁AND	-4830
+▁fourth	-4831
+▁slightly	-4832
+orter	-4833
+▁Tur	-4834
+oles	-4835
+▁inspired	-4836
+▁Mike	-4837
+▁ang	-4838
+▁dance	-4839
+▁net	-4840
+▁Tre	-4841
+▁enhance	-4842
+▁Den	-4843
+▁apart	-4844
+▁Prov	-4845
+▁Wall	-4846
+▁Jim	-4847
+▁scr	-4848
+▁spect	-4849
+▁mental	-4850
+▁Hotel	-4851
+▁Old	-4852
+▁fantastic	-4853
+▁Land	-4854
+▁pal	-4855
+▁format	-4856
+▁Somet	-4857
+▁sav	-4858
+▁joint	-4859
+▁desk	-4860
+ita	-4861
+▁upcoming	-4862
+▁ath	-4863
+▁AC	-4864
+▁spl	-4865
+▁Lead	-4866
+▁Dou	-4867
+inct	-4868
+▁emp	-4869
+▁YOU	-4870
+▁willing	-4871
+rist	-4872
+▁hearing	-4873
+▁sounds	-4874
+▁fuel	-4875
+▁commitment	-4876
+ups	-4877
+▁consumers	-4878
+▁appeal	-4879
+▁raise	-4880
+?”	-4881
+▁Manager	-4882
+▁civil	-4883
+▁UN	-4884
+kin	-4885
+osen	-4886
+▁Place	-4887
+▁library	-4888
+umin	-4889
+SA	-4890
+ensions	-4891
+▁vir	-4892
+▁north	-4893
+▁Through	-4894
+▁expertise	-4895
+▁Report	-4896
+▁promote	-4897
+▁asking	-4898
+▁absolutely	-4899
+▁units	-4900
+▁Contin	-4901
+water	-4902
+▁chocolate	-4903
+cher	-4904
+▁extensive	-4905
+▁Louis	-4906
+▁movies	-4907
+▁delivered	-4908
+▁Series	-4909
+▁bask	-4910
+▁delicious	-4911
+▁Ill	-4912
+Pro	-4913
+▁eth	-4914
+▁reached	-4915
+▁sets	-4916
+zen	-4917
+Com	-4918
+▁Vict	-4919
+known	-4920
+▁executive	-4921
+uable	-4922
+▁plays	-4923
+▁agreement	-4924
+ternal	-4925
+▁Link	-4926
+▁radio	-4927
+nergy	-4928
+▁Posted	-4929
+▁Ma	-4930
+▁foreign	-4931
+▁alle	-4932
+▁lunch	-4933
+REE	-4934
+▁transform	-4935
+▁datab	-4936
+aser	-4937
+▁register	-4938
+icians	-4939
+▁emergency	-4940
+▁thick	-4941
+▁struct	-4942
+▁trees	-4943
+▁Angeles	-4944
+▁Invest	-4945
+list	-4946
+eline	-4947
+▁Ham	-4948
+▁Lim	-4949
+▁Const	-4950
+▁Oper	-4951
+▁provider	-4952
+▁brief	-4953
+▁NE	-4954
+▁presence	-4955
+text	-4956
+▁Upd	-4957
+▁combined	-4958
+▁Fund	-4959
+▁rid	-4960
+!)	-4961
+▁Admin	-4962
+▁Fun	-4963
+▁achie	-4964
+prise	-4965
+▁Gal	-4966
+▁furniture	-4967
+▁seeking	-4968
+▁fruit	-4969
+▁NOT	-4970
+▁Hand	-4971
+▁controll	-4972
+▁Union	-4973
+osition	-4974
+▁connected	-4975
+▁Join	-4976
+bre	-4977
+▁Jun	-4978
+▁readers	-4979
+▁expensive	-4980
+▁adults	-4981
+▁Person	-4982
+▁Cook	-4983
+▁Democr	-4984
+reens	-4985
+▁seconds	-4986
+▁feels	-4987
+▁poll	-4988
+▁ON	-4989
+uality	-4990
+▁rat	-4991
+▁generation	-4992
+▁distance	-4993
+▁edge	-4994
+▁fees	-4995
+▁mentioned	-4996
+▁recommended	-4997
+▁trial	-4998
+▁chat	-4999
+▁calling	-5000
+▁har	-5001
+▁nine	-5002
+▁cities	-5003
+▁chicken	-5004
+▁approximately	-5005
+▁Plus	-5006
+atin	-5007
+▁bringing	-5008
+TH	-5009
+▁consid	-5010
+▁Access	-5011
+▁Journal	-5012
+▁Inte	-5013
+▁wel	-5014
+▁married	-5015
+fortunately	-5016
+▁Peter	-5017
+▁prepare	-5018
+▁websites	-5019
+▁operation	-5020
+▁alternative	-5021
+▁confidence	-5022
+▁server	-5023
+▁dogs	-5024
+IR	-5025
+▁registered	-5026
+▁stars	-5027
+cean	-5028
+LA	-5029
+▁educational	-5030
+▁Master	-5031
+burg	-5032
+▁Di	-5033
+appy	-5034
+▁Indust	-5035
+▁photograph	-5036
+▁restrict	-5037
+ef	-5038
+ruit	-5039
+▁Chief	-5040
+▁Ol	-5041
+▁tight	-5042
+My	-5043
+▁Children	-5044
+▁centre	-5045
+hab	-5046
+emporary	-5047
+▁square	-5048
+▁France	-5049
+othes	-5050
+▁Spring	-5051
+▁tun	-5052
+▁returned	-5053
+▁lovely	-5054
+▁minimum	-5055
+▁category	-5056
+OC	-5057
+▁Live	-5058
+azz	-5059
+▁exchange	-5060
+▁seat	-5061
+irmed	-5062
+▁stret	-5063
+▁Prote	-5064
+ears	-5065
+▁topic	-5066
+▁installed	-5067
+▁tea	-5068
+▁info	-5069
+▁Rest	-5070
+rag	-5071
+▁tough	-5072
+▁brands	-5073
+asks	-5074
+▁guest	-5075
+▁princip	-5076
+▁Way	-5077
+bu	-5078
+▁majority	-5079
+▁researc	-5080
+atre	-5081
+inations	-5082
+▁wearing	-5083
+▁appearance	-5084
+▁female	-5085
+how	-5086
+▁neck	-5087
+▁Minister	-5088
+▁colle	-5089
+estyle	-5090
+ship	-5091
+orry	-5092
+▁Cy	-5093
+IF	-5094
+When	-5095
+ulated	-5096
+aks	-5097
+▁ven	-5098
+▁accompl	-5099
+▁therefore	-5100
+▁mostly	-5101
+▁instru	-5102
+▁Canad	-5103
+▁Ok	-5104
+▁Price	-5105
+elines	-5106
+▁maximum	-5107
+▁HD	-5108
+▁winner	-5109
+▁sauce	-5110
+▁processes	-5111
+▁academic	-5112
+▁surgery	-5113
+van	-5114
+kins	-5115
+▁measures	-5116
+▁responsibility	-5117
+▁Ver	-5118
+ifications	-5119
+▁leads	-5120
+▁impl	-5121
+▁teen	-5122
+▁Mo	-5123
+▁killed	-5124
+▁Sup	-5125
+▁approved	-5126
+▁apps	-5127
+▁anywhere	-5128
+▁arrange	-5129
+▁Max	-5130
+nel	-5131
+▁Men	-5132
+osis	-5133
+▁Sports	-5134
+▁stre	-5135
+▁Video	-5136
+▁Hy	-5137
+▁importance	-5138
+▁Test	-5139
+▁gather	-5140
+▁ring	-5141
+▁climate	-5142
+▁Squ	-5143
+alian	-5144
+▁satisf	-5145
+▁detailed	-5146
+▁boost	-5147
+▁signs	-5148
+▁battery	-5149
+An	-5150
+▁nom	-5151
+hi	-5152
+▁battle	-5153
+▁feedback	-5154
+▁chief	-5155
+▁veter	-5156
+▁Festival	-5157
+▁switch	-5158
+▁Creat	-5159
+mond	-5160
+▁dyn	-5161
+▁worldwide	-5162
+▁featured	-5163
+▁scheduled	-5164
+▁cooking	-5165
+▁disp	-5166
+▁highlight	-5167
+ius	-5168
+lets	-5169
+▁Wild	-5170
+▁supporting	-5171
+▁rise	-5172
+ait	-5173
+▁crim	-5174
+▁Library	-5175
+▁sympt	-5176
+ulty	-5177
+▁cheap	-5178
+cohol	-5179
+▁comprehensive	-5180
+▁predict	-5181
+▁participants	-5182
+vis	-5183
+▁Walk	-5184
+▁Jud	-5185
+arsh	-5186
+▁Cat	-5187
+ker	-5188
+▁IP	-5189
+▁Thomas	-5190
+▁affordable	-5191
+▁otherwise	-5192
+paper	-5193
+▁Bob	-5194
+▁Tour	-5195
+▁defense	-5196
+▁Conference	-5197
+alend	-5198
+ters	-5199
+Cl	-5200
+cious	-5201
+▁bike	-5202
+▁Lab	-5203
+roy	-5204
+otten	-5205
+▁properly	-5206
+ician	-5207
+▁animal	-5208
+▁actions	-5209
+▁Using	-5210
+ulate	-5211
+▁clearly	-5212
+ena	-5213
+▁performed	-5214
+▁Earth	-5215
+FL	-5216
+▁Search	-5217
+gl	-5218
+▁mur	-5219
+▁Pan	-5220
+▁purchased	-5221
+itable	-5222
+bl	-5223
+▁Those	-5224
+idden	-5225
+▁ourselves	-5226
+iner	-5227
+pected	-5228
+oston	-5229
+▁Bi	-5230
+▁conv	-5231
+▁joy	-5232
+uts	-5233
+▁Copyright	-5234
+▁audio	-5235
+iser	-5236
+▁chemical	-5237
+▁meal	-5238
+▁vent	-5239
+▁competitive	-5240
+verse	-5241
+anda	-5242
+▁Johnson	-5243
+▁appeared	-5244
+▁windows	-5245
+▁advertising	-5246
+▁Global	-5247
+▁applied	-5248
+▁push	-5249
+▁motiv	-5250
+UT	-5251
+bol	-5252
+▁Prem	-5253
+▁ment	-5254
+▁Cam	-5255
+▁doors	-5256
+▁Soft	-5257
+ENT	-5258
+▁Party	-5259
+▁sister	-5260
+▁policies	-5261
+gment	-5262
+▁pump	-5263
+▁mouth	-5264
+oga	-5265
+▁topics	-5266
+▁Form	-5267
+▁Jeff	-5268
+erg	-5269
+▁supported	-5270
+▁valid	-5271
+▁Bas	-5272
+▁technologies	-5273
+▁pregn	-5274
+▁scale	-5275
+▁flowers	-5276
+▁rom	-5277
+▁behavior	-5278
+▁arm	-5279
+▁African	-5280
+▁sitting	-5281
+rastructure	-5282
+GB	-5283
+MA	-5284
+▁minor	-5285
+▁writer	-5286
+▁familiar	-5287
+▁Jose	-5288
+▁holding	-5289
+▁entertainment	-5290
+▁featuring	-5291
+▁rub	-5292
+▁Germany	-5293
+▁episode	-5294
+▁coord	-5295
+but	-5296
+▁bond	-5297
+ushed	-5298
+▁studio	-5299
+▁Western	-5300
+▁editor	-5301
+▁Charl	-5302
+▁opinion	-5303
+▁Kore	-5304
+▁elim	-5305
+alog	-5306
+▁Cost	-5307
+▁participate	-5308
+▁revenue	-5309
+▁plug	-5310
+▁Haw	-5311
+tr	-5312
+▁removed	-5313
+▁faster	-5314
+▁Connect	-5315
+▁Fair	-5316
+▁Help	-5317
+▁Saf	-5318
+▁sides	-5319
+west	-5320
+inch	-5321
+▁strategies	-5322
+▁Champions	-5323
+▁coast	-5324
+erts	-5325
+▁jew	-5326
+▁charged	-5327
+▁depending	-5328
+col	-5329
+▁totally	-5330
+prene	-5331
+oration	-5332
+▁birthday	-5333
+▁reliable	-5334
+▁visiting	-5335
+▁quiet	-5336
+▁begins	-5337
+▁Martin	-5338
+▁species	-5339
+▁conversation	-5340
+▁described	-5341
+UN	-5342
+inating	-5343
+▁Energy	-5344
+▁flight	-5345
+orough	-5346
+▁caught	-5347
+▁Girl	-5348
+▁Cert	-5349
+▁ap	-5350
+▁eventually	-5351
+▁monthly	-5352
+▁fif	-5353
+▁consumer	-5354
+hus	-5355
+den	-5356
+▁Hospital	-5357
+tered	-5358
+▁Sar	-5359
+▁restaurants	-5360
+▁tail	-5361
+▁meat	-5362
+▁housing	-5363
+▁cells	-5364
+▁dish	-5365
+▁teach	-5366
+▁MP	-5367
+▁deals	-5368
+▁inches	-5369
+▁Digital	-5370
+▁pu	-5371
+▁television	-5372
+otic	-5373
+▁Mic	-5374
+▁accounts	-5375
+with	-5376
+▁improved	-5377
+reprene	-5378
+ersey	-5379
+▁German	-5380
+▁Dev	-5381
+▁nav	-5382
+▁Orig	-5383
+apes	-5384
+▁Gen	-5385
+▁labor	-5386
+▁Australian	-5387
+▁delight	-5388
+inter	-5389
+▁university	-5390
+▁dim	-5391
+▁Id	-5392
+▁fly	-5393
+▁Joe	-5394
+▁officer	-5395
+▁marriage	-5396
+▁hundreds	-5397
+▁neighborhood	-5398
+▁campus	-5399
+▁revealed	-5400
+ario	-5401
+▁shoes	-5402
+▁employee	-5403
+ste	-5404
+▁cro	-5405
+▁label	-5406
+▁breakfast	-5407
+ulous	-5408
+▁ign	-5409
+weight	-5410
+▁CH	-5411
+▁Ul	-5412
+▁confirm	-5413
+▁Penn	-5414
+▁administration	-5415
+▁typically	-5416
+SE	-5417
+▁occasion	-5418
+▁Academy	-5419
+▁introduced	-5420
+▁celebrate	-5421
+▁exclusive	-5422
+How	-5423
+▁election	-5424
+▁covers	-5425
+ht	-5426
+▁Secret	-5427
+▁essay	-5428
+▁Mid	-5429
+▁appointment	-5430
+ighter	-5431
+▁volume	-5432
+▁Ce	-5433
+▁unless	-5434
+sm	-5435
+▁Opt	-5436
+hew	-5437
+achel	-5438
+▁discovered	-5439
+▁specifically	-5440
+▁amb	-5441
+▁vary	-5442
+hent	-5443
+▁compar	-5444
+iat	-5445
+▁internal	-5446
+▁indic	-5447
+▁planned	-5448
+Our	-5449
+▁Hope	-5450
+▁twe	-5451
+▁debt	-5452
+▁intended	-5453
+NA	-5454
+▁cultural	-5455
+▁cutting	-5456
+▁sessions	-5457
+▁AT	-5458
+▁Americans	-5459
+▁Lt	-5460
+▁aspects	-5461
+▁manufacturing	-5462
+▁remaining	-5463
+▁Maybe	-5464
+▁Young	-5465
+eries	-5466
+ushing	-5467
+▁mel	-5468
+▁sexual	-5469
+▁SP	-5470
+bur	-5471
+ixture	-5472
+igr	-5473
+▁shares	-5474
+edia	-5475
+▁nor	-5476
+▁Box	-5477
+merce	-5478
+▁Boy	-5479
+▁Second	-5480
+▁recovery	-5481
+);	-5482
+▁basket	-5483
+▁fle	-5484
+▁Boston	-5485
+▁icon	-5486
+▁chart	-5487
+▁engineering	-5488
+▁remote	-5489
+▁trading	-5490
+ords	-5491
+▁concent	-5492
+▁Ari	-5493
+▁scored	-5494
+▁Er	-5495
+▁bread	-5496
+▁incredible	-5497
+▁partnership	-5498
+▁Key	-5499
+▁investigation	-5500
+▁lights	-5501
+▁edition	-5502
+ournament	-5503
+▁dining	-5504
+▁Commun	-5505
+uke	-5506
+asts	-5507
+▁industrial	-5508
+▁Jon	-5509
+▁guarantee	-5510
+▁forg	-5511
+▁detect	-5512
+▁Mur	-5513
+CE	-5514
+▁invent	-5515
+aren	-5516
+▁Meet	-5517
+cont	-5518
+▁Carolina	-5519
+▁drivers	-5520
+gas	-5521
+▁components	-5522
+▁Japanese	-5523
+▁negative	-5524
+▁liqu	-5525
+▁hyd	-5526
+▁automatically	-5527
+mosp	-5528
+▁End	-5529
+elly	-5530
+▁resource	-5531
+eper	-5532
+▁depos	-5533
+▁cake	-5534
+ala	-5535
+▁Pac	-5536
+▁mir	-5537
+▁freed	-5538
+▁fields	-5539
+lymp	-5540
+▁burn	-5541
+▁Virginia	-5542
+odies	-5543
+▁practical	-5544
+berry	-5545
+▁chain	-5546
+▁Type	-5547
+cm	-5548
+▁choices	-5549
+▁noted	-5550
+rupt	-5551
+▁Human	-5552
+▁evalu	-5553
+▁quot	-5554
+▁pock	-5555
+▁confirmed	-5556
+inet	-5557
+▁interior	-5558
+▁dollars	-5559
+▁seemed	-5560
+▁Applic	-5561
+otton	-5562
+▁Lee	-5563
+lywood	-5564
+▁cop	-5565
+▁victory	-5566
+▁bedroom	-5567
+▁Jones	-5568
+itionally	-5569
+▁thus	-5570
+▁rule	-5571
+idays	-5572
+▁suitable	-5573
+▁Wal	-5574
+iability	-5575
+▁argu	-5576
+▁depart	-5577
+▁arrived	-5578
+cles	-5579
+▁Brand	-5580
+▁Quest	-5581
+ua	-5582
+unting	-5583
+▁perfectly	-5584
+Al	-5585
+▁FREE	-5586
+▁twice	-5587
+tters	-5588
+hand	-5589
+uits	-5590
+▁buildings	-5591
+▁boys	-5592
+Ex	-5593
+away	-5594
+▁teeth	-5595
+▁Tem	-5596
+aped	-5597
+▁possibly	-5598
+▁broken	-5599
+▁warrant	-5600
+▁Mult	-5601
+▁Equ	-5602
+king	-5603
+abet	-5604
+gers	-5605
+▁symptoms	-5606
+▁films	-5607
+▁crew	-5608
+▁honor	-5609
+uous	-5610
+▁shooting	-5611
+▁elig	-5612
+▁Italian	-5613
+▁doubt	-5614
+▁bathroom	-5615
+▁Victor	-5616
+arp	-5617
+▁ticket	-5618
+▁Know	-5619
+▁anc	-5620
+arks	-5621
+No	-5622
+!”	-5623
+▁Gar	-5624
+▁island	-5625
+▁stated	-5626
+▁issued	-5627
+ailability	-5628
+flow	-5629
+▁DV	-5630
+▁chosen	-5631
+ilit	-5632
+▁Cast	-5633
+rier	-5634
+▁considering	-5635
+▁enable	-5636
+▁commission	-5637
+▁Mexico	-5638
+▁Steve	-5639
+▁Little	-5640
+▁injuries	-5641
+▁Trust	-5642
+urban	-5643
+▁candidates	-5644
+poses	-5645
+▁tests	-5646
+related	-5647
+otal	-5648
+▁Williams	-5649
+▁reference	-5650
+▁desire	-5651
+▁foods	-5652
+▁rapid	-5653
+▁keeps	-5654
+▁corn	-5655
+TC	-5656
+▁bigger	-5657
+ibilities	-5658
+road	-5659
+▁ris	-5660
+▁missed	-5661
+ipl	-5662
+▁Instead	-5663
+▁mode	-5664
+▁paying	-5665
+ulations	-5666
+▁boat	-5667
+▁picked	-5668
+▁golf	-5669
+▁contest	-5670
+▁Does	-5671
+iors	-5672
+▁intellig	-5673
+▁circum	-5674
+▁Farm	-5675
+acks	-5676
+▁Students	-5677
+▁Hard	-5678
+▁appreciate	-5679
+▁decades	-5680
+▁premium	-5681
+▁turns	-5682
+▁tomorrow	-5683
+▁sizes	-5684
+iamond	-5685
+▁trend	-5686
+▁Games	-5687
+▁valuable	-5688
+gend	-5689
+owntown	-5690
+▁fro	-5691
+▁settings	-5692
+▁Coast	-5693
+▁protected	-5694
+ien	-5695
+▁voc	-5696
+▁Tit	-5697
+▁Kn	-5698
+▁presentation	-5699
+▁soul	-5700
+▁Mat	-5701
+▁Mov	-5702
+▁lived	-5703
+▁Page	-5704
+▁regularly	-5705
+▁realize	-5706
+mes	-5707
+▁earned	-5708
+atoes	-5709
+▁Current	-5710
+▁registration	-5711
+▁nurs	-5712
+▁Night	-5713
+▁config	-5714
+▁Ohio	-5715
+▁attorney	-5716
+▁magazine	-5717
+▁citizens	-5718
+▁quant	-5719
+hetic	-5720
+▁aid	-5721
+▁failed	-5722
+▁oven	-5723
+▁AS	-5724
+▁database	-5725
+fection	-5726
+ora	-5727
+ris	-5728
+▁spr	-5729
+▁Assist	-5730
+▁therapy	-5731
+▁organic	-5732
+ias	-5733
+▁license	-5734
+▁sequ	-5735
+wing	-5736
+▁Canadian	-5737
+weet	-5738
+▁Econom	-5739
+▁agent	-5740
+▁Michigan	-5741
+▁surrounding	-5742
+AY	-5743
+▁mine	-5744
+▁affected	-5745
+▁greatest	-5746
+▁resol	-5747
+▁ends	-5748
+▁providers	-5749
+▁moments	-5750
+oosing	-5751
+▁ran	-5752
+▁county	-5753
+▁Olymp	-5754
+▁tells	-5755
+what	-5756
+▁ec	-5757
+▁dates	-5758
+▁Span	-5759
+PR	-5760
+▁grown	-5761
+▁Cross	-5762
+▁reput	-5763
+▁MS	-5764
+▁athlet	-5765
+▁Code	-5766
+ev	-5767
+▁surf	-5768
+▁virtual	-5769
+▁investors	-5770
+▁Instagram	-5771
+▁grade	-5772
+spe	-5773
+▁Pass	-5774
+▁calcul	-5775
+▁answers	-5776
+.|	-5777
+▁loves	-5778
+▁shock	-5779
+▁supports	-5780
+▁painting	-5781
+▁inn	-5782
+▁draft	-5783
+phas	-5784
+▁influence	-5785
+▁proposed	-5786
+lights	-5787
+▁agencies	-5788
+oup	-5789
+▁surprise	-5790
+▁History	-5791
+pass	-5792
+▁Control	-5793
+▁Kh	-5794
+abled	-5795
+▁hero	-5796
+▁dial	-5797
+▁poly	-5798
+▁Sn	-5799
+▁explain	-5800
+▁weap	-5801
+▁accurate	-5802
+▁submit	-5803
+▁degrees	-5804
+▁renew	-5805
+▁Bal	-5806
+race	-5807
+▁recorded	-5808
+▁Executive	-5809
+▁ages	-5810
+▁Van	-5811
+▁Point	-5812
+oking	-5813
+▁owned	-5814
+▁convenient	-5815
+▁Georg	-5816
+▁AR	-5817
+▁purposes	-5818
+▁Share	-5819
+vell	-5820
+▁load	-5821
+ria	-5822
+which	-5823
+▁Did	-5824
+▁beer	-5825
+▁yes	-5826
+irms	-5827
+▁whom	-5828
+fficient	-5829
+▁Inf	-5830
+▁league	-5831
+▁Federal	-5832
+▁holds	-5833
+▁processing	-5834
+ella	-5835
+▁Buy	-5836
+▁Middle	-5837
+TA	-5838
+▁gro	-5839
+TV	-5840
+▁instructions	-5841
+▁die	-5842
+▁Cas	-5843
+▁Asia	-5844
+kes	-5845
+▁interests	-5846
+▁Jackson	-5847
+▁Def	-5848
+▁apparent	-5849
+▁efficiency	-5850
+▁pure	-5851
+ansas	-5852
+hors	-5853
+▁jack	-5854
+▁atmosp	-5855
+▁effectively	-5856
+▁Expl	-5857
+mar	-5858
+▁violence	-5859
+luding	-5860
+▁returns	-5861
+alendar	-5862
+▁Comple	-5863
+▁Enjoy	-5864
+▁element	-5865
+▁pleased	-5866
+▁awareness	-5867
+▁goods	-5868
+▁Paris	-5869
+vy	-5870
+real	-5871
+▁messages	-5872
+OVID	-5873
+cking	-5874
+▁pepper	-5875
+▁channel	-5876
+▁receiving	-5877
+▁infrastructure	-5878
+print	-5879
+▁Ken	-5880
+▁pod	-5881
+rick	-5882
+▁Three	-5883
+▁electronic	-5884
+▁Ire	-5885
+▁occup	-5886
+▁Made	-5887
+▁forced	-5888
+intage	-5889
+▁officers	-5890
+▁Size	-5891
+▁facing	-5892
+▁creation	-5893
+ospit	-5894
+▁musical	-5895
+▁standing	-5896
+▁Requ	-5897
+▁researchers	-5898
+▁Dom	-5899
+▁sam	-5900
+▁incident	-5901
+▁Royal	-5902
+▁perman	-5903
+▁Columb	-5904
+▁belong	-5905
+▁closer	-5906
+irty	-5907
+▁lighting	-5908
+▁everyday	-5909
+▁Try	-5910
+▁diverse	-5911
+▁grad	-5912
+▁Richard	-5913
+▁route	-5914
+▁Daily	-5915
+profit	-5916
+ban	-5917
+▁Travel	-5918
+▁ongoing	-5919
+▁distribution	-5920
+▁Photo	-5921
+▁lit	-5922
+▁Cred	-5923
+▁causes	-5924
+poration	-5925
+made	-5926
+▁trouble	-5927
+▁Ell	-5928
+▁thread	-5929
+▁apartment	-5930
+▁Sher	-5931
+▁administr	-5932
+▁advoc	-5933
+▁usual	-5934
+▁wheel	-5935
+▁serves	-5936
+▁Chair	-5937
+▁Ut	-5938
+rum	-5939
+▁sad	-5940
+▁Need	-5941
+▁pun	-5942
+anche	-5943
+▁Store	-5944
+▁du	-5945
+▁mini	-5946
+isters	-5947
+▁obtain	-5948
+▁kinds	-5949
+▁ped	-5950
+▁healthcare	-5951
+▁favourite	-5952
+hy	-5953
+▁judge	-5954
+▁silver	-5955
+▁arts	-5956
+▁wid	-5957
+PM	-5958
+GE	-5959
+▁Cath	-5960
+▁supposed	-5961
+▁meetings	-5962
+▁error	-5963
+▁crime	-5964
+equ	-5965
+▁rough	-5966
+▁spaces	-5967
+▁yellow	-5968
+▁knowing	-5969
+rete	-5970
+▁plate	-5971
+▁affili	-5972
+udden	-5973
+ribe	-5974
+▁disappoint	-5975
+▁stopped	-5976
+▁flour	-5977
+▁enthus	-5978
+▁fellow	-5979
+▁WH	-5980
+umes	-5981
+▁Wi	-5982
+▁bound	-5983
+never	-5984
+oses	-5985
+▁collaboration	-5986
+aration	-5987
+▁manner	-5988
+Tube	-5989
+▁Rev	-5990
+xy	-5991
+▁designer	-5992
+itage	-5993
+▁licens	-5994
+▁construct	-5995
+▁concerned	-5996
+actions	-5997
+▁Andrew	-5998
+▁monit	-5999
+▁subscrib	-6000
+▁massive	-6001
+▁Ltd	-6002
+person	-6003
+anges	-6004
+▁weekly	-6005
+▁clothes	-6006
+▁follows	-6007
+ennis	-6008
+uction	-6009
+▁Low	-6010
+▁tut	-6011
+▁rot	-6012
+▁Four	-6013
+ancer	-6014
+cue	-6015
+sembly	-6016
+▁Local	-6017
+▁Daniel	-6018
+arian	-6019
+ello	-6020
+▁prison	-6021
+▁tur	-6022
+▁household	-6023
+▁Wr	-6024
+yard	-6025
+▁simpl	-6026
+▁forces	-6027
+▁Clean	-6028
+▁reduced	-6029
+▁regional	-6030
+▁challenging	-6031
+iveness	-6032
+EE	-6033
+astern	-6034
+▁male	-6035
+▁Mean	-6036
+▁tack	-6037
+▁Guide	-6038
+▁functions	-6039
+▁stone	-6040
+▁Ra	-6041
+▁agreed	-6042
+pond	-6043
+▁hang	-6044
+▁Right	-6045
+▁script	-6046
+▁Room	-6047
+▁Santa	-6048
+▁Francisco	-6049
+oti	-6050
+▁Hen	-6051
+▁lifestyle	-6052
+▁Russian	-6053
+▁moist	-6054
+▁treated	-6055
+orable	-6056
+▁horse	-6057
+▁debut	-6058
+▁complic	-6059
+▁Marketing	-6060
+▁alcohol	-6061
+ansion	-6062
+▁assets	-6063
+▁native	-6064
+▁innovation	-6065
+▁payments	-6066
+▁sample	-6067
+▁fixed	-6068
+ml	-6069
+▁reserved	-6070
+▁successfully	-6071
+▁impressive	-6072
+Con	-6073
+▁powder	-6074
+▁crisis	-6075
+▁emotional	-6076
+▁explained	-6077
+FC	-6078
+DS	-6079
+▁Ep	-6080
+Ar	-6081
+▁inspiration	-6082
+▁cute	-6083
+▁Job	-6084
+All	-6085
+▁Visit	-6086
+Un	-6087
+ache	-6088
+▁witness	-6089
+under	-6090
+▁leather	-6091
+▁spokes	-6092
+▁row	-6093
+▁Rights	-6094
+writ	-6095
+ench	-6096
+▁fort	-6097
+▁forest	-6098
+▁password	-6099
+ppers	-6100
+▁matters	-6101
+▁Brook	-6102
+▁FOR	-6103
+Pl	-6104
+ani	-6105
+▁identified	-6106
+alled	-6107
+▁luxury	-6108
+▁employment	-6109
+BI	-6110
+▁photograp	-6111
+Be	-6112
+▁blogg	-6113
+▁drugs	-6114
+▁Pot	-6115
+▁Summer	-6116
+▁Hor	-6117
+▁cock	-6118
+▁extended	-6119
+And	-6120
+▁phil	-6121
+▁iron	-6122
+▁Die	-6123
+shire	-6124
+igration	-6125
+erves	-6126
+▁Area	-6127
+lyn	-6128
+▁determined	-6129
+▁rand	-6130
+▁accepted	-6131
+▁grab	-6132
+▁recognized	-6133
+▁outstanding	-6134
+▁prop	-6135
+▁Blo	-6136
+▁prompt	-6137
+▁der	-6138
+▁styles	-6139
+▁resolution	-6140
+▁Southern	-6141
+▁tou	-6142
+▁height	-6143
+folio	-6144
+▁walls	-6145
+▁odd	-6146
+▁gifts	-6147
+▁Rose	-6148
+▁clinical	-6149
+▁casino	-6150
+▁vacation	-6151
+▁Name	-6152
+▁decre	-6153
+▁advis	-6154
+▁Cra	-6155
+▁accessible	-6156
+▁context	-6157
+▁nearby	-6158
+▁graduate	-6159
+liance	-6160
+▁conducted	-6161
+can	-6162
+They	-6163
+vate	-6164
+▁happening	-6165
+rip	-6166
+▁Number	-6167
+▁positions	-6168
+▁worse	-6169
+▁Small	-6170
+▁dangerous	-6171
+▁perspective	-6172
+▁Awards	-6173
+▁Financial	-6174
+▁SH	-6175
+▁freedom	-6176
+▁gear	-6177
+mary	-6178
+▁carried	-6179
+▁speaking	-6180
+▁factor	-6181
+letter	-6182
+▁Ash	-6183
+▁Turn	-6184
+▁stunning	-6185
+▁sustainable	-6186
+▁speech	-6187
+▁Colorado	-6188
+cling	-6189
+▁tag	-6190
+▁Scot	-6191
+▁folks	-6192
+▁significantly	-6193
+▁candidate	-6194
+▁Oil	-6195
+unction	-6196
+▁telling	-6197
+▁domestic	-6198
+ulture	-6199
+▁examples	-6200
+anged	-6201
+▁Avenue	-6202
+▁constantly	-6203
+rid	-6204
+▁committee	-6205
+▁emphas	-6206
+▁Training	-6207
+▁cable	-6208
+▁Coll	-6209
+▁likes	-6210
+▁Lin	-6211
+▁symbol	-6212
+▁Kim	-6213
+▁univers	-6214
+▁hardware	-6215
+▁mixed	-6216
+▁Perform	-6217
+ificate	-6218
+▁originally	-6219
+▁solar	-6220
+▁Having	-6221
+▁Account	-6222
+▁hook	-6223
+▁vit	-6224
+ucle	-6225
+▁Sometimes	-6226
+▁Which	-6227
+▁stands	-6228
+emic	-6229
+▁retire	-6230
+▁Hon	-6231
+▁conflic	-6232
+▁awards	-6233
+Don	-6234
+ployment	-6235
+▁adventure	-6236
+▁contemporary	-6237
+▁showc	-6238
+LY	-6239
+▁houses	-6240
+▁involve	-6241
+▁logo	-6242
+▁village	-6243
+▁fulf	-6244
+▁Though	-6245
+▁Cond	-6246
+▁bless	-6247
+▁Spanish	-6248
+▁carefully	-6249
+▁patterns	-6250
+▁supplies	-6251
+▁MA	-6252
+▁Dub	-6253
+▁Select	-6254
+▁procedures	-6255
+▁Print	-6256
+▁DC	-6257
+ingly	-6258
+▁auto	-6259
+▁programme	-6260
+▁browser	-6261
+▁imagine	-6262
+▁Mobile	-6263
+▁Despite	-6264
+▁stretch	-6265
+▁losing	-6266
+▁confident	-6267
+▁criminal	-6268
+▁fitness	-6269
+▁replacement	-6270
+lete	-6271
+▁routine	-6272
+▁Available	-6273
+▁illustr	-6274
+▁adds	-6275
+▁Ireland	-6276
+▁procedure	-6277
+▁engage	-6278
+▁Rom	-6279
+ca	-6280
+▁circumst	-6281
+▁Ryan	-6282
+▁bottle	-6283
+etime	-6284
+▁Garden	-6285
+▁crazy	-6286
+utch	-6287
+▁turning	-6288
+▁YouTube	-6289
+▁random	-6290
+▁hosting	-6291
+▁taught	-6292
+▁rose	-6293
+▁expectations	-6294
+▁lift	-6295
+state	-6296
+▁Russia	-6297
+▁command	-6298
+▁recipes	-6299
+▁Tay	-6300
+front	-6301
+▁Drive	-6302
+secut	-6303
+▁fo	-6304
+▁improvement	-6305
+▁alleged	-6306
+▁excess	-6307
+▁hur	-6308
+▁tro	-6309
+▁trained	-6310
+▁sheet	-6311
+▁noticed	-6312
+▁mixture	-6313
+▁festival	-6314
+▁Bon	-6315
+▁funny	-6316
+illy	-6317
+▁tech	-6318
+▁OS	-6319
+ATE	-6320
+▁tab	-6321
+▁shots	-6322
+▁syn	-6323
+▁flavor	-6324
+▁reporting	-6325
+▁passeng	-6326
+▁guitar	-6327
+▁ol	-6328
+▁hoping	-6329
+▁severe	-6330
+▁entreprene	-6331
+▁COVID	-6332
+inder	-6333
+▁suspect	-6334
+▁eleg	-6335
+ether	-6336
+▁foundation	-6337
+orgeous	-6338
+▁Heart	-6339
+ington	-6340
+▁SU	-6341
+▁upper	-6342
+ossible	-6343
+inem	-6344
+anger	-6345
+▁Building	-6346
+▁Environment	-6347
+▁blow	-6348
+eration	-6349
+▁clothing	-6350
+▁scholars	-6351
+▁publish	-6352
+▁Non	-6353
+▁ok	-6354
+enced	-6355
+anna	-6356
+▁Italy	-6357
+adium	-6358
+▁authent	-6359
+▁FA	-6360
+▁climb	-6361
+▁pink	-6362
+comes	-6363
+▁Pop	-6364
+▁Senior	-6365
+rad	-6366
+iano	-6367
+▁talks	-6368
+▁kill	-6369
+pat	-6370
+▁grew	-6371
+▁Son	-6372
+▁pil	-6373
+hered	-6374
+▁Beaut	-6375
+▁root	-6376
+▁san	-6377
+oster	-6378
+▁landscape	-6379
+tle	-6380
+ayer	-6381
+▁figures	-6382
+▁millions	-6383
+ERS	-6384
+ums	-6385
+▁machines	-6386
+▁Country	-6387
+ERE	-6388
+So	-6389
+iece	-6390
+▁Jersey	-6391
+iversary	-6392
+▁Run	-6393
+▁Sky	-6394
+orders	-6395
+▁tasks	-6396
+▁vital	-6397
+▁reward	-6398
+▁attended	-6399
+ikes	-6400
+▁eggs	-6401
+▁tall	-6402
+▁identity	-6403
+▁tested	-6404
+▁hits	-6405
+▁PS	-6406
+▁Senate	-6407
+▁coc	-6408
+’.	-6409
+▁integrated	-6410
+▁champions	-6411
+▁laugh	-6412
+▁herself	-6413
+▁trends	-6414
+▁input	-6415
+▁Division	-6416
+▁Disney	-6417
+forcement	-6418
+▁vibr	-6419
+▁anx	-6420
+▁council	-6421
+oral	-6422
+▁?	-6423
+▁Shop	-6424
+▁Nick	-6425
+▁chapter	-6426
+▁Stock	-6427
+▁Ref	-6428
+HS	-6429
+▁shift	-6430
+▁mal	-6431
+▁Jenn	-6432
+▁guard	-6433
+▁weak	-6434
+▁dram	-6435
+▁wealth	-6436
+▁Dog	-6437
+▁historical	-6438
+▁Writ	-6439
+▁fishing	-6440
+▁incl	-6441
+▁baking	-6442
+.’	-6443
+▁airport	-6444
+▁Proper	-6445
+▁depth	-6446
+▁AD	-6447
+▁museum	-6448
+▁improving	-6449
+▁smile	-6450
+▁invited	-6451
+▁arrested	-6452
+izz	-6453
+host	-6454
+RI	-6455
+▁wash	-6456
+luded	-6457
+rition	-6458
+▁accessories	-6459
+dy	-6460
+▁Professor	-6461
+ampion	-6462
+▁Safety	-6463
+▁thin	-6464
+▁profit	-6465
+▁ease	-6466
+▁unf	-6467
+▁output	-6468
+▁qualified	-6469
+▁Ent	-6470
+▁Ford	-6471
+▁residential	-6472
+rate	-6473
+▁Want	-6474
+riends	-6475
+▁rear	-6476
+▁upload	-6477
+▁abuse	-6478
+▁Ha	-6479
+▁hire	-6480
+▁authorities	-6481
+▁tonight	-6482
+▁carbon	-6483
+▁Georgia	-6484
+▁certified	-6485
+▁skill	-6486
+▁mountain	-6487
+▁Fre	-6488
+▁wet	-6489
+ATION	-6490
+▁Sales	-6491
+remony	-6492
+zil	-6493
+▁ordered	-6494
+pret	-6495
+▁Far	-6496
+▁bags	-6497
+▁managing	-6498
+▁instance	-6499
+▁km	-6500
+▁destination	-6501
+▁Still	-6502
+▁entered	-6503
+▁thorough	-6504
+▁Email	-6505
+iana	-6506
+▁sole	-6507
+▁dropped	-6508
+icial	-6509
+▁entirely	-6510
+▁recy	-6511
+▁Bul	-6512
+▁institutions	-6513
+iami	-6514
+▁terror	-6515
+▁atmosphere	-6516
+▁Silver	-6517
+yers	-6518
+▁Further	-6519
+LS	-6520
+▁Supp	-6521
+▁Fed	-6522
+▁Systems	-6523
+▁Luc	-6524
+▁Space	-6525
+▁closely	-6526
+▁sick	-6527
+▁guidance	-6528
+▁photography	-6529
+PC	-6530
+▁Stat	-6531
+▁breast	-6532
+▁Zeal	-6533
+▁rating	-6534
+ras	-6535
+▁tiny	-6536
+▁description	-6537
+▁Tax	-6538
+▁vend	-6539
+▁Members	-6540
+▁fuck	-6541
+▁offices	-6542
+▁scientific	-6543
+▁transportation	-6544
+▁layer	-6545
+stone	-6546
+▁printed	-6547
+long	-6548
+De	-6549
+▁frequently	-6550
+▁Fac	-6551
+▁Dist	-6552
+▁spin	-6553
+eller	-6554
+igned	-6555
+va	-6556
+agues	-6557
+▁cooper	-6558
+▁entr	-6559
+▁EU	-6560
+▁yards	-6561
+▁shower	-6562
+▁searching	-6563
+▁cycle	-6564
+▁dental	-6565
+▁loans	-6566
+▁delay	-6567
+▁CO	-6568
+▁Phone	-6569
+▁failure	-6570
+▁Pract	-6571
+▁kne	-6572
+▁medicine	-6573
+MP	-6574
+▁equal	-6575
+▁lessons	-6576
+izza	-6577
+▁unable	-6578
+▁protein	-6579
+adow	-6580
+ogue	-6581
+▁broadcast	-6582
+▁founded	-6583
+sen	-6584
+▁Aff	-6585
+▁Finally	-6586
+▁cm	-6587
+▁column	-6588
+▁flexible	-6589
+quir	-6590
+▁Tech	-6591
+▁operate	-6592
+▁bonus	-6593
+▁typical	-6594
+▁compens	-6595
+▁Looking	-6596
+▁rail	-6597
+▁taxes	-6598
+aduate	-6599
+▁Hou	-6600
+▁glad	-6601
+▁Should	-6602
+▁religious	-6603
+▁Never	-6604
+▁sac	-6605
+▁Engineering	-6606
+▁situations	-6607
+▁vacc	-6608
+▁awarded	-6609
+▁bear	-6610
+▁PDF	-6611
+▁Ca	-6612
+▁lad	-6613
+▁Ball	-6614
+▁Zealand	-6615
+oes	-6616
+▁Put	-6617
+▁eligible	-6618
+quality	-6619
+▁Very	-6620
+▁external	-6621
+▁Mach	-6622
+▁historic	-6623
+▁Sat	-6624
+▁alongside	-6625
+icket	-6626
+awn	-6627
+UL	-6628
+▁flood	-6629
+▁strategic	-6630
+▁OR	-6631
+▁sudden	-6632
+▁unlike	-6633
+▁wra	-6634
+▁DVD	-6635
+worth	-6636
+▁assessment	-6637
+▁filed	-6638
+▁Smart	-6639
+osoph	-6640
+ilst	-6641
+▁networks	-6642
+▁seriously	-6643
+▁Sus	-6644
+▁creates	-6645
+▁workshop	-6646
+Is	-6647
+?"	-6648
+umps	-6649
+▁worst	-6650
+▁rental	-6651
+▁Unfortunately	-6652
+xx	-6653
+▁BE	-6654
+▁Charles	-6655
+▁transition	-6656
+uting	-6657
+▁fighting	-6658
+▁critic	-6659
+▁river	-6660
+nam	-6661
+▁membership	-6662
+ircle	-6663
+▁Mountain	-6664
+oker	-6665
+▁believes	-6666
+asters	-6667
+bi	-6668
+▁platforms	-6669
+omy	-6670
+▁none	-6671
+friendly	-6672
+▁availability	-6673
+▁attacks	-6674
+▁versions	-6675
+▁vul	-6676
+▁Foot	-6677
+▁tracks	-6678
+class	-6679
+uling	-6680
+▁distinct	-6681
+erman	-6682
+▁younger	-6683
+▁Es	-6684
+tain	-6685
+▁listening	-6686
+osite	-6687
+▁Fox	-6688
+plate	-6689
+▁faculty	-6690
+▁motion	-6691
+aturally	-6692
+▁Ask	-6693
+▁contribute	-6694
+▁hasn	-6695
+arrow	-6696
+inos	-6697
+!"	-6698
+▁Professional	-6699
+▁juice	-6700
+II	-6701
+▁proven	-6702
+eding	-6703
+▁Pacific	-6704
+One	-6705
+▁hopes	-6706
+▁bab	-6707
+onto	-6708
+star	-6709
+aze	-6710
+With	-6711
+▁joining	-6712
+▁letters	-6713
+irts	-6714
+ucky	-6715
+▁risks	-6716
+▁performing	-6717
+active	-6718
+▁Ray	-6719
+▁streets	-6720
+car	-6721
+▁soph	-6722
+▁Ariz	-6723
+ounter	-6724
+you	-6725
+▁developers	-6726
+▁SC	-6727
+▁conver	-6728
+▁obl	-6729
+▁cups	-6730
+▁pounds	-6731
+neys	-6732
+Fi	-6733
+▁cos	-6734
+▁recording	-6735
+▁Term	-6736
+▁tip	-6737
+ati	-6738
+▁Tele	-6739
+zer	-6740
+▁Harr	-6741
+▁Easy	-6742
+▁lucky	-6743
+▁Kent	-6744
+▁informed	-6745
+oured	-6746
+▁choosing	-6747
+▁surprised	-6748
+ented	-6749
+▁grass	-6750
+▁facilit	-6751
+▁meals	-6752
+)|	-6753
+▁mortgage	-6754
+nic	-6755
+▁Phys	-6756
+obby	-6757
+▁infect	-6758
+▁capture	-6759
+▁liquid	-6760
+ican	-6761
+▁banks	-6762
+▁diss	-6763
+▁tournament	-6764
+▁PA	-6765
+agon	-6766
+▁Leg	-6767
+▁kit	-6768
+▁Fall	-6769
+amps	-6770
+▁LLC	-6771
+▁anticip	-6772
+elry	-6773
+▁papers	-6774
+▁Field	-6775
+▁savings	-6776
+earing	-6777
+At	-6778
+▁privacy	-6779
+cers	-6780
+▁discip	-6781
+To	-6782
+pons	-6783
+uine	-6784
+▁Event	-6785
+aping	-6786
+▁hurt	-6787
+born	-6788
+▁rein	-6789
+▁regulations	-6790
+▁Ram	-6791
+▁Mom	-6792
+▁Broad	-6793
+▁inch	-6794
+▁decade	-6795
+ashed	-6796
+law	-6797
+ially	-6798
+▁charm	-6799
+▁Taylor	-6800
+▁submitted	-6801
+rency	-6802
+celer	-6803
+▁Kat	-6804
+etic	-6805
+▁arg	-6806
+▁west	-6807
+▁Northern	-6808
+▁Ter	-6809
+▁blend	-6810
+▁ille	-6811
+Le	-6812
+▁reputation	-6813
+▁LED	-6814
+▁bat	-6815
+Se	-6816
+▁Po	-6817
+▁suggested	-6818
+▁monitor	-6819
+▁hall	-6820
+▁proceed	-6821
+▁liked	-6822
+▁relief	-6823
+▁organized	-6824
+▁filter	-6825
+▁shops	-6826
+▁domain	-6827
+▁consequ	-6828
+▁mic	-6829
+▁Lind	-6830
+▁belief	-6831
+▁sight	-6832
+▁engagement	-6833
+entle	-6834
+▁Cut	-6835
+▁Source	-6836
+▁Miami	-6837
+bury	-6838
+▁extract	-6839
+▁pulled	-6840
+Read	-6841
+▁Radio	-6842
+▁Come	-6843
+▁Credit	-6844
+▁gorgeous	-6845
+days	-6846
+▁justice	-6847
+uter	-6848
+pes	-6849
+▁Cab	-6850
+▁drawing	-6851
+▁Sea	-6852
+▁negoti	-6853
+▁circumstances	-6854
+▁capable	-6855
+▁quote	-6856
+▁Arab	-6857
+▁)	-6858
+▁tank	-6859
+▁monitoring	-6860
+ava	-6861
+▁empt	-6862
+▁crucial	-6863
+rell	-6864
+▁Think	-6865
+▁legs	-6866
+▁Order	-6867
+▁portfolio	-6868
+▁Bible	-6869
+▁sky	-6870
+bing	-6871
+ulf	-6872
+ographic	-6873
+▁hate	-6874
+▁immediate	-6875
+▁increases	-6876
+▁ads	-6877
+▁arrive	-6878
+▁exhibition	-6879
+▁stir	-6880
+▁Ms	-6881
+bar	-6882
+▁believed	-6883
+foot	-6884
+▁penal	-6885
+▁moves	-6886
+▁Insurance	-6887
+▁linked	-6888
+ta	-6889
+athan	-6890
+▁Continue	-6891
+▁counsel	-6892
+▁relatively	-6893
+▁treatments	-6894
+▁faces	-6895
+▁attached	-6896
+▁Pak	-6897
+▁manual	-6898
+faction	-6899
+▁soil	-6900
+▁crack	-6901
+▁adm	-6902
+▁defend	-6903
+illiant	-6904
+uis	-6905
+▁mm	-6906
+▁jun	-6907
+ura	-6908
+▁Mir	-6909
+▁planet	-6910
+resents	-6911
+bles	-6912
+Ad	-6913
+▁technique	-6914
+cknow	-6915
+▁concert	-6916
+▁enjoying	-6917
+rowse	-6918
+▁guidelines	-6919
+▁listing	-6920
+esides	-6921
+▁directed	-6922
+▁interface	-6923
+▁injured	-6924
+arters	-6925
+▁vast	-6926
+▁hosted	-6927
+▁execut	-6928
+▁dent	-6929
+▁LA	-6930
+▁ast	-6931
+▁Conf	-6932
+▁Rod	-6933
+▁spark	-6934
+▁garage	-6935
+▁authors	-6936
+▁hospit	-6937
+▁memories	-6938
+uration	-6939
+rich	-6940
+▁contrast	-6941
+▁aside	-6942
+▁volunteers	-6943
+▁equipped	-6944
+sey	-6945
+▁Ron	-6946
+ardens	-6947
+▁Ur	-6948
+▁normally	-6949
+ppy	-6950
+▁estimated	-6951
+▁:)	-6952
+▁promise	-6953
+▁firms	-6954
+▁Republican	-6955
+▁dreams	-6956
+▁Happy	-6957
+▁Pow	-6958
+onym	-6959
+▁Jac	-6960
+▁warn	-6961
+▁trig	-6962
+▁pin	-6963
+hot	-6964
+▁trick	-6965
+▁phase	-6966
+▁depress	-6967
+▁rice	-6968
+▁Remember	-6969
+▁urban	-6970
+▁illness	-6971
+By	-6972
+▁Being	-6973
+▁Quality	-6974
+iger	-6975
+▁agents	-6976
+▁Justice	-6977
+▁acid	-6978
+▁prove	-6979
+ba	-6980
+▁consistent	-6981
+oty	-6982
+▁dust	-6983
+▁spoke	-6984
+▁Airport	-6985
+▁Houston	-6986
+▁pitch	-6987
+▁Bed	-6988
+▁organis	-6989
+▁pleasure	-6990
+▁arms	-6991
+holders	-6992
+aints	-6993
+▁matches	-6994
+▁Medicine	-6995
+AA	-6996
+ults	-6997
+Bl	-6998
+%.	-6999
+▁Ide	-7000
+▁Talk	-7001
+▁portion	-7002
+▁Conc	-7003
+▁index	-7004
+▁Line	-7005
+▁chances	-7006
+ogether	-7007
+▁Brazil	-7008
+asant	-7009
+▁fasc	-7010
+▁Fact	-7011
+.'	-7012
+icit	-7013
+▁lapt	-7014
+▁newly	-7015
+▁chose	-7016
+▁Personal	-7017
+▁objects	-7018
+▁Carl	-7019
+▁dynamic	-7020
+ensity	-7021
+▁breath	-7022
+▁finance	-7023
+rm	-7024
+▁Arizona	-7025
+▁refund	-7026
+▁Asian	-7027
+▁Living	-7028
+▁Standard	-7029
+▁Prom	-7030
+▁proof	-7031
+▁seed	-7032
+SC	-7033
+eling	-7034
+▁passing	-7035
+▁continuing	-7036
+But	-7037
+▁visited	-7038
+▁represents	-7039
+▁Officer	-7040
+▁drinking	-7041
+▁Give	-7042
+site	-7043
+ership	-7044
+▁iPad	-7045
+cket	-7046
+▁formed	-7047
+▁storm	-7048
+▁ultimate	-7049
+▁mile	-7050
+pack	-7051
+inois	-7052
+alle	-7053
+▁Brad	-7054
+▁Mill	-7055
+▁roles	-7056
+▁border	-7057
+▁Estate	-7058
+▁forever	-7059
+▁MO	-7060
+▁discussed	-7061
+▁superv	-7062
+▁ceremony	-7063
+▁Cru	-7064
+annels	-7065
+▁approval	-7066
+iking	-7067
+▁Las	-7068
+▁zone	-7069
+amber	-7070
+▁Welcome	-7071
+▁Army	-7072
+▁Season	-7073
+▁Student	-7074
+▁id	-7075
+▁suc	-7076
+she	-7077
+▁stim	-7078
+▁exposure	-7079
+▁recommendations	-7080
+adel	-7081
+▁gaming	-7082
+▁dealing	-7083
+stal	-7084
+▁sending	-7085
+ultural	-7086
+▁Oak	-7087
+▁Iran	-7088
+▁stake	-7089
+▁evol	-7090
+▁Therefore	-7091
+▁phones	-7092
+MC	-7093
+anes	-7094
+▁Sav	-7095
+▁Kevin	-7096
+▁capabilities	-7097
+▁teasp	-7098
+▁division	-7099
+▁gallery	-7100
+▁Webs	-7101
+uclear	-7102
+Americ	-7103
+whel	-7104
+amsung	-7105
+▁boxes	-7106
+▁downtown	-7107
+▁saving	-7108
+▁presents	-7109
+▁collected	-7110
+▁holidays	-7111
+respond	-7112
+▁lawyer	-7113
+▁possibility	-7114
+▁fairly	-7115
+▁Again	-7116
+▁implementation	-7117
+iki	-7118
+▁vulner	-7119
+▁pra	-7120
+ainless	-7121
+▁mand	-7122
+▁susp	-7123
+▁hat	-7124
+GA	-7125
+ja	-7126
+▁ensuring	-7127
+▁Choose	-7128
+▁permanent	-7129
+aper	-7130
+▁attractive	-7131
+▁pharm	-7132
+▁smell	-7133
+▁cookies	-7134
+▁Administration	-7135
+▁constit	-7136
+▁flash	-7137
+▁Site	-7138
+▁industries	-7139
+ih	-7140
+▁tub	-7141
+▁hidden	-7142
+▁suggestions	-7143
+▁scheme	-7144
+aste	-7145
+bro	-7146
+▁trib	-7147
+▁finds	-7148
+lers	-7149
+▁Experience	-7150
+izer	-7151
+▁porn	-7152
+▁Natural	-7153
+▁Brian	-7154
+ione	-7155
+wear	-7156
+urse	-7157
+▁recognize	-7158
+▁Express	-7159
+RS	-7160
+▁Kenn	-7161
+▁instrument	-7162
+missions	-7163
+▁facts	-7164
+phy	-7165
+▁Ju	-7166
+▁theory	-7167
+▁heads	-7168
+▁vari	-7169
+pot	-7170
+▁priority	-7171
+▁mainly	-7172
+▁acknow	-7173
+zes	-7174
+▁($	-7175
+lessly	-7176
+▁Meanwhile	-7177
+Sc	-7178
+▁legislation	-7179
+ffered	-7180
+rible	-7181
+▁reader	-7182
+▁Clin	-7183
+▁Ros	-7184
+▁Isl	-7185
+▁bodies	-7186
+▁Case	-7187
+FA	-7188
+▁butt	-7189
+▁liber	-7190
+▁categories	-7191
+▁Chall	-7192
+▁posting	-7193
+▁realized	-7194
+▁mut	-7195
+▁Hollywood	-7196
+anned	-7197
+page	-7198
+inson	-7199
+▁Software	-7200
+▁communications	-7201
+▁Vers	-7202
+▁Ba	-7203
+▁solve	-7204
+▁Own	-7205
+▁bench	-7206
+▁personally	-7207
+▁Dun	-7208
+▁garlic	-7209
+▁Secretary	-7210
+▁upgrade	-7211
+da	-7212
+▁bars	-7213
+allas	-7214
+▁Queen	-7215
+boy	-7216
+▁bridge	-7217
+phones	-7218
+▁Emer	-7219
+Book	-7220
+EA	-7221
+▁Stay	-7222
+▁incredibly	-7223
+▁USB	-7224
+then	-7225
+▁ancient	-7226
+▁Learning	-7227
+▁Policy	-7228
+CT	-7229
+▁Create	-7230
+▁reform	-7231
+▁tradition	-7232
+esy	-7233
+▁||	-7234
+▁permission	-7235
+▁hole	-7236
+▁Bang	-7237
+stra	-7238
+ingu	-7239
+▁tiss	-7240
+osc	-7241
+▁Prime	-7242
+▁Anal	-7243
+▁generate	-7244
+▁Yet	-7245
+odd	-7246
+anny	-7247
+ounce	-7248
+▁Cand	-7249
+▁exec	-7250
+▁CN	-7251
+▁copyright	-7252
+▁packages	-7253
+▁calendar	-7254
+▁rum	-7255
+odge	-7256
+▁handling	-7257
+tw	-7258
+ials	-7259
+▁substant	-7260
+▁travell	-7261
+▁pace	-7262
+▁basketball	-7263
+▁east	-7264
+▁magic	-7265
+▁Hold	-7266
+▁debate	-7267
+parent	-7268
+OO	-7269
+▁victims	-7270
+▁raw	-7271
+▁claimed	-7272
+▁Level	-7273
+That	-7274
+▁Additionally	-7275
+iti	-7276
+▁celebration	-7277
+▁clar	-7278
+▁walked	-7279
+▁orange	-7280
+▁programming	-7281
+▁Jr	-7282
+▁doctors	-7283
+▁MD	-7284
+HA	-7285
+ulpt	-7286
+▁achieved	-7287
+▁fest	-7288
+▁giant	-7289
+▁cotton	-7290
+▁Toronto	-7291
+▁absor	-7292
+▁forth	-7293
+▁purchasing	-7294
+▁habit	-7295
+onna	-7296
+▁prospect	-7297
+▁replaced	-7298
+▁Cro	-7299
+▁Stan	-7300
+▁bare	-7301
+▁Film	-7302
+burgh	-7303
+▁fifth	-7304
+▁explains	-7305
+uls	-7306
+▁tooth	-7307
+▁Illinois	-7308
+▁desired	-7309
+▁Studies	-7310
+level	-7311
+CD	-7312
+zing	-7313
+isa	-7314
+▁king	-7315
+▁Tool	-7316
+▁manufacturers	-7317
+▁spots	-7318
+▁titles	-7319
+▁gym	-7320
+▁saved	-7321
+▁Dar	-7322
+▁seasons	-7323
+▁cuts	-7324
+season	-7325
+▁somewhere	-7326
+▁marked	-7327
+▁Auto	-7328
+▁proposal	-7329
+▁Consult	-7330
+▁insight	-7331
+▁marks	-7332
+▁hotels	-7333
+▁initiative	-7334
+uster	-7335
+▁feelings	-7336
+▁venue	-7337
+▁slowly	-7338
+RL	-7339
+▁singer	-7340
+▁specialist	-7341
+▁suffering	-7342
+▁Produ	-7343
+▁Catholic	-7344
+ila	-7345
+▁NFL	-7346
+▁expressed	-7347
+▁Story	-7348
+▁Capital	-7349
+▁compat	-7350
+▁requests	-7351
+▁Irish	-7352
+▁drinks	-7353
+▁Material	-7354
+imize	-7355
+▁architecture	-7356
+App	-7357
+iot	-7358
+▁vegetables	-7359
+▁Save	-7360
+▁Sep	-7361
+aron	-7362
+▁Agency	-7363
+igate	-7364
+esh	-7365
+▁buyers	-7366
+acon	-7367
+aters	-7368
+▁Joseph	-7369
+▁merch	-7370
+▁volunteer	-7371
+▁gay	-7372
+▁exceptional	-7373
+▁impossible	-7374
+▁stuck	-7375
+▁Liber	-7376
+▁Table	-7377
+▁meets	-7378
+▁enables	-7379
+▁swimming	-7380
+stream	-7381
+▁combine	-7382
+inton	-7383
+▁murder	-7384
+▁broke	-7385
+bridge	-7386
+▁publication	-7387
+▁announcement	-7388
+▁destroy	-7389
+▁tie	-7390
+▁extension	-7391
+ylvan	-7392
+▁causing	-7393
+▁ultimately	-7394
+▁enem	-7395
+VER	-7396
+▁consultation	-7397
+▁encouraged	-7398
+▁reducing	-7399
+▁muscle	-7400
+▁err	-7401
+▁accomplish	-7402
+▁Pakistan	-7403
+▁Mess	-7404
+regon	-7405
+nesota	-7406
+▁split	-7407
+ologist	-7408
+▁packaging	-7409
+▁yard	-7410
+▁surprising	-7411
+▁Mix	-7412
+▁lets	-7413
+▁Pu	-7414
+▁publ	-7415
+▁Bell	-7416
+ickets	-7417
+▁magn	-7418
+aid	-7419
+▁Short	-7420
+▁Vegas	-7421
+▁Map	-7422
+▁actor	-7423
+▁rig	-7424
+▁printing	-7425
+▁Would	-7426
+▁enterprise	-7427
+▁engaged	-7428
+▁Autom	-7429
+▁pit	-7430
+lements	-7431
+▁describe	-7432
+▁Camer	-7433
+▁heav	-7434
+▁massage	-7435
+▁pricing	-7436
+run	-7437
+▁DI	-7438
+bel	-7439
+apore	-7440
+des	-7441
+aska	-7442
+▁Motor	-7443
+▁electrical	-7444
+▁noise	-7445
+▁mood	-7446
+▁Location	-7447
+▁widely	-7448
+▁preparation	-7449
+▁Kids	-7450
+ifer	-7451
+▁seeds	-7452
+▁reasonable	-7453
+▁talked	-7454
+▁Pen	-7455
+▁enroll	-7456
+▁blocks	-7457
+▁covering	-7458
+▁performances	-7459
+▁Labor	-7460
+ns	-7461
+▁Spain	-7462
+▁breaking	-7463
+▁expansion	-7464
+bell	-7465
+▁recognition	-7466
+▁pill	-7467
+olis	-7468
+▁default	-7469
+▁framework	-7470
+eah	-7471
+▁wins	-7472
+▁Recent	-7473
+▁genuine	-7474
+▁overwhel	-7475
+▁traveling	-7476
+▁remark	-7477
+▁blank	-7478
+▁Forest	-7479
+▁seats	-7480
+rage	-7481
+▁classroom	-7482
+RC	-7483
+▁agric	-7484
+wan	-7485
+▁knock	-7486
+inator	-7487
+cons	-7488
+▁Ira	-7489
+▁interactive	-7490
+uct	-7491
+▁concrete	-7492
+▁neighb	-7493
+▁Theatre	-7494
+▁Ess	-7495
+▁CB	-7496
+iler	-7497
+▁Adam	-7498
+▁unw	-7499
+▁pand	-7500
+▁Gallery	-7501
+)||	-7502
+▁Studio	-7503
+▁birds	-7504
+▁formal	-7505
+▁Force	-7506
+▁Pin	-7507
+▁compr	-7508
+▁dishes	-7509
+▁Band	-7510
+wich	-7511
+▁Memorial	-7512
+▁writers	-7513
+▁Ice	-7514
+▁franch	-7515
+▁resistance	-7516
+▁Following	-7517
+▁gall	-7518
+▁empty	-7519
+▁Rs	-7520
+▁Toy	-7521
+gypt	-7522
+▁brilliant	-7523
+▁spray	-7524
+▁consists	-7525
+▁constant	-7526
+ulum	-7527
+▁scenes	-7528
+▁increasingly	-7529
+▁staying	-7530
+▁compliance	-7531
+proof	-7532
+▁Square	-7533
+▁incorpor	-7534
+▁Mrs	-7535
+▁resulting	-7536
+▁acting	-7537
+▁Davis	-7538
+▁Annual	-7539
+EP	-7540
+▁duty	-7541
+▁suggests	-7542
+▁pic	-7543
+▁dad	-7544
+▁recover	-7545
+ludes	-7546
+▁managers	-7547
+▁Fred	-7548
+▁Member	-7549
+▁experiment	-7550
+nda	-7551
+▁Treat	-7552
+▁basically	-7553
+▁spiritual	-7554
+ateful	-7555
+axy	-7556
+ding	-7557
+▁Things	-7558
+▁professor	-7559
+ifies	-7560
+▁anyway	-7561
+▁bow	-7562
+▁Diego	-7563
+▁nights	-7564
+▁Paper	-7565
+▁Mah	-7566
+being	-7567
+▁Spirit	-7568
+▁mere	-7569
+child	-7570
+▁Eric	-7571
+books	-7572
+▁FL	-7573
+leep	-7574
+▁graphics	-7575
+otted	-7576
+▁Dam	-7577
+▁lists	-7578
+▁Partners	-7579
+▁Jord	-7580
+▁forecast	-7581
+▁slic	-7582
+▁slot	-7583
+▁Solutions	-7584
+▁scan	-7585
+▁pride	-7586
+▁deck	-7587
+▁Samsung	-7588
+▁Roman	-7589
+abetes	-7590
+’,	-7591
+▁prize	-7592
+▁authority	-7593
+▁Shipping	-7594
+▁producing	-7595
+▁Ly	-7596
+rated	-7597
+▁Interest	-7598
+ilton	-7599
+alo	-7600
+▁centers	-7601
+▁clicking	-7602
+▁Seattle	-7603
+irus	-7604
+▁Model	-7605
+▁packed	-7606
+una	-7607
+▁wireless	-7608
+▁Gro	-7609
+erate	-7610
+alse	-7611
+▁Books	-7612
+▁everywhere	-7613
+▁aims	-7614
+ghan	-7615
+▁legend	-7616
+acle	-7617
+▁Golden	-7618
+▁Minnesota	-7619
+▁enthusi	-7620
+ashes	-7621
+▁whenever	-7622
+▁expenses	-7623
+vas	-7624
+▁Pur	-7625
+▁Age	-7626
+▁indeed	-7627
+▁healing	-7628
+▁Limited	-7629
+utional	-7630
+▁interpret	-7631
+▁closing	-7632
+▁Cover	-7633
+▁talented	-7634
+▁singles	-7635
+▁anniversary	-7636
+▁succeed	-7637
+▁inner	-7638
+inding	-7639
+▁Lew	-7640
+making	-7641
+▁involves	-7642
+rome	-7643
+▁Swed	-7644
+▁pocket	-7645
+ls	-7646
+▁riding	-7647
+▁unex	-7648
+▁connections	-7649
+▁Sound	-7650
+▁GM	-7651
+heast	-7652
+▁channels	-7653
+▁obtained	-7654
+pends	-7655
+▁narr	-7656
+▁founder	-7657
+▁vice	-7658
+▁OK	-7659
+ylvania	-7660
+▁Magazine	-7661
+▁Perhaps	-7662
+▁displayed	-7663
+▁Customer	-7664
+▁Dream	-7665
+▁bunch	-7666
+▁assum	-7667
+▁Total	-7668
+▁opens	-7669
+greg	-7670
+▁Collection	-7671
+▁delivering	-7672
+▁Month	-7673
+▁Bad	-7674
+▁Dallas	-7675
+▁designers	-7676
+▁struggle	-7677
+ureau	-7678
+▁lemon	-7679
+Press	-7680
+▁trips	-7681
+▁Based	-7682
+▁Steel	-7683
+▁attrib	-7684
+▁differences	-7685
+stein	-7686
+▁acts	-7687
+▁ending	-7688
+▁Working	-7689
+▁driven	-7690
+▁Pict	-7691
+lder	-7692
+abeth	-7693
+▁CP	-7694
+nders	-7695
+▁Station	-7696
+ronics	-7697
+▁defined	-7698
+▁Mother	-7699
+▁watched	-7700
+▁complim	-7701
+▁improvements	-7702
+▁mob	-7703
+▁Cloud	-7704
+▁primarily	-7705
+coin	-7706
+▁CL	-7707
+▁loving	-7708
+▁vintage	-7709
+bits	-7710
+▁Action	-7711
+▁gender	-7712
+▁boss	-7713
+sters	-7714
+▁guaranteed	-7715
+▁introduction	-7716
+▁Rub	-7717
+▁Oregon	-7718
+▁booking	-7719
+▁Dark	-7720
+ambling	-7721
+▁returning	-7722
+▁Rand	-7723
+oom	-7724
+▁Sym	-7725
+▁sensitive	-7726
+▁fits	-7727
+▁shouldn	-7728
+▁Eastern	-7729
+▁SS	-7730
+▁podcast	-7731
+Fr	-7732
+▁apparently	-7733
+▁Everyone	-7734
+▁Anth	-7735
+▁Base	-7736
+▁politics	-7737
+owa	-7738
+▁officially	-7739
+pool	-7740
+issions	-7741
+▁precise	-7742
+oned	-7743
+▁Common	-7744
+▁rug	-7745
+▁Products	-7746
+rive	-7747
+▁alive	-7748
+▁headed	-7749
+▁Bru	-7750
+▁Return	-7751
+AB	-7752
+▁chopped	-7753
+su	-7754
+▁Miller	-7755
+iders	-7756
+▁fing	-7757
+▁unus	-7758
+▁Jay	-7759
+▁Spec	-7760
+▁Blog	-7761
+▁coat	-7762
+▁Change	-7763
+▁narrow	-7764
+▁highlights	-7765
+▁protest	-7766
+▁trim	-7767
+▁recre	-7768
+AND	-7769
+▁potentially	-7770
+▁honey	-7771
+▁shell	-7772
+▁Transport	-7773
+ailing	-7774
+▁percentage	-7775
+▁authentic	-7776
+▁Austin	-7777
+▁filling	-7778
+▁tape	-7779
+▁maintaining	-7780
+▁lin	-7781
+▁Capt	-7782
+▁analyst	-7783
+▁retirement	-7784
+▁Cry	-7785
+▁casual	-7786
+▁speaker	-7787
+▁crash	-7788
+pson	-7789
+atics	-7790
+riers	-7791
+▁Among	-7792
+▁assistant	-7793
+▁charity	-7794
+▁personality	-7795
+▁Corporation	-7796
+wart	-7797
+▁acquis	-7798
+▁scientists	-7799
+jo	-7800
+▁Kingdom	-7801
+▁resident	-7802
+▁Guard	-7803
+▁falling	-7804
+inent	-7805
+lose	-7806
+scribe	-7807
+raid	-7808
+▁plot	-7809
+▁DO	-7810
+▁elev	-7811
+▁Iraq	-7812
+pection	-7813
+iac	-7814
+▁bills	-7815
+▁opinions	-7816
+onut	-7817
+▁Josh	-7818
+▁Barb	-7819
+▁strike	-7820
+▁licensed	-7821
+▁aircraft	-7822
+▁heading	-7823
+ali	-7824
+▁CR	-7825
+▁Nic	-7826
+▁naturally	-7827
+▁Dead	-7828
+acher	-7829
+raction	-7830
+▁consumption	-7831
+ydney	-7832
+▁renov	-7833
+▁Sarah	-7834
+▁carrying	-7835
+▁tired	-7836
+▁gentle	-7837
+arliam	-7838
+▁colours	-7839
+Cont	-7840
+▁Jewish	-7841
+▁Egypt	-7842
+▁correspond	-7843
+▁obviously	-7844
+▁functional	-7845
+▁preparing	-7846
+asted	-7847
+▁involving	-7848
+▁suffered	-7849
+▁Stephen	-7850
+▁iOS	-7851
+ilipp	-7852
+▁oz	-7853
+▁Ap	-7854
+▁smartphone	-7855
+▁NC	-7856
+▁Ven	-7857
+▁tap	-7858
+teen	-7859
+▁intent	-7860
+▁winners	-7861
+osophy	-7862
+▁stronger	-7863
+▁controls	-7864
+athy	-7865
+▁instant	-7866
+▁Singapore	-7867
+▁CON	-7868
+ockey	-7869
+▁satisfaction	-7870
+oices	-7871
+▁communicate	-7872
+▁Jason	-7873
+▁aggress	-7874
+▁integration	-7875
+▁Golf	-7876
+▁attending	-7877
+ilty	-7878
+▁colleagues	-7879
+hour	-7880
+phia	-7881
+▁lect	-7882
+▁statements	-7883
+▁Dave	-7884
+▁racing	-7885
+ador	-7886
+▁Jam	-7887
+tics	-7888
+anchester	-7889
+▁rising	-7890
+▁finger	-7891
+▁component	-7892
+epend	-7893
+▁minimal	-7894
+▁gained	-7895
+▁tracking	-7896
+▁ski	-7897
+uma	-7898
+▁Weight	-7899
+▁Atlanta	-7900
+▁undert	-7901
+iley	-7902
+▁Website	-7903
+terior	-7904
+▁Creek	-7905
+▁reduction	-7906
+▁Past	-7907
+▁behalf	-7908
+vard	-7909
+▁trailer	-7910
+▁bid	-7911
+unes	-7912
+arliament	-7913
+icted	-7914
+Man	-7915
+rovers	-7916
+tery	-7917
+lee	-7918
+etch	-7919
+▁checking	-7920
+▁Vice	-7921
+▁principles	-7922
+▁superior	-7923
+▁dollar	-7924
+▁signing	-7925
+▁preferred	-7926
+▁focuses	-7927
+▁illegal	-7928
+▁findings	-7929
+▁displ	-7930
+▁tone	-7931
+▁deposit	-7932
+sylvania	-7933
+▁Friends	-7934
+▁ED	-7935
+inally	-7936
+avor	-7937
+▁Rog	-7938
+adelphia	-7939
+▁Lic	-7940
+lace	-7941
+▁Bit	-7942
+▁producer	-7943
+ocation	-7944
+▁Britain	-7945
+▁batt	-7946
+▁Korea	-7947
+ression	-7948
+ucks	-7949
+▁enforcement	-7950
+intend	-7951
+▁heating	-7952
+bow	-7953
+▁Image	-7954
+▁intelligence	-7955
+fall	-7956
+▁clubs	-7957
+aka	-7958
+inals	-7959
+amel	-7960
+▁Pick	-7961
+▁regardless	-7962
+▁assault	-7963
+▁humans	-7964
+▁Tony	-7965
+▁emails	-7966
+▁divor	-7967
+limited	-7968
+▁Kansas	-7969
+▁participating	-7970
+▁Columbia	-7971
+brid	-7972
+▁removal	-7973
+▁newspaper	-7974
+haust	-7975
+▁;	-7976
+iously	-7977
+vi	-7978
+gent	-7979
+▁till	-7980
+▁Marc	-7981
+▁Uk	-7982
+▁array	-7983
+----	-7984
+▁Bridge	-7985
+htt	-7986
+aine	-7987
+makers	-7988
+zzle	-7989
+▁anymore	-7990
+▁yield	-7991
+▁Winter	-7992
+cker	-7993
+▁flower	-7994
+▁lifetime	-7995
+▁acceler	-7996
+▁Has	-7997
+▁Below	-7998
+▁lesson	-7999
+UM	-8000
+ints	-8001
+▁Hi	-8002
+roke	-8003
+▁compare	-8004
+▁roads	-8005
+olit	-8006
+▁usage	-8007
+uten	-8008
+▁loose	-8009
+▁Hun	-8010
+▁Sem	-8011
+▁entering	-8012
+▁ham	-8013
+▁jewelry	-8014
+▁Eliz	-8015
+▁Quick	-8016
+pir	-8017
+▁faced	-8018
+▁stations	-8019
+ventional	-8020
+▁Flow	-8021
+▁stable	-8022
+▁passionate	-8023
+▁divid	-8024
+ho	-8025
+AV	-8026
+▁conclus	-8027
+giving	-8028
+▁Village	-8029
+▁intense	-8030
+▁CC	-8031
+ran	-8032
+▁ocean	-8033
+tail	-8034
+mers	-8035
+▁Front	-8036
+etary	-8037
+▁Resources	-8038
+▁expression	-8039
+▁Lady	-8040
+▁introduce	-8041
+▁drama	-8042
+▁poet	-8043
+▁recip	-8044
+▁rural	-8045
+▁diseases	-8046
+▁Break	-8047
+▁pandemic	-8048
+onic	-8049
+▁occurred	-8050
+swe	-8051
+▁somewhat	-8052
+▁string	-8053
+orous	-8054
+▁Kitchen	-8055
+pending	-8056
+▁exception	-8057
+▁warranty	-8058
+▁NEW	-8059
+aza	-8060
+▁lie	-8061
+▁Tenn	-8062
+▁Henry	-8063
+▁sharp	-8064
+▁olive	-8065
+▁careful	-8066
+▁strange	-8067
+▁workshops	-8068
+▁Excell	-8069
+▁oppon	-8070
+▁viewing	-8071
+▁damaged	-8072
+▁baseball	-8073
+▁BB	-8074
+▁compete	-8075
+▁EN	-8076
+▁strict	-8077
+▁Was	-8078
+▁factory	-8079
+▁solo	-8080
+▁literally	-8081
+oked	-8082
+▁elegant	-8083
+▁computers	-8084
+▁creativity	-8085
+▁whilst	-8086
+▁visible	-8087
+itled	-8088
+winning	-8089
+▁shoulder	-8090
+▁screens	-8091
+▁era	-8092
+▁association	-8093
+▁Bol	-8094
+EST	-8095
+▁EX	-8096
+▁Sale	-8097
+▁referred	-8098
+▁photographs	-8099
+oween	-8100
+▁Lar	-8101
+Up	-8102
+▁Due	-8103
+▁amounts	-8104
+▁exceed	-8105
+acular	-8106
+▁DJ	-8107
+▁visits	-8108
+▁Baby	-8109
+▁Without	-8110
+▁attach	-8111
+▁Impro	-8112
+▁Wilson	-8113
+liers	-8114
+▁conflict	-8115
+▁flying	-8116
+inity	-8117
+▁SM	-8118
+▁Ministry	-8119
+▁stead	-8120
+▁limits	-8121
+EG	-8122
+▁depends	-8123
+▁je	-8124
+▁lady	-8125
+walk	-8126
+▁withd	-8127
+acc	-8128
+▁twist	-8129
+▁Process	-8130
+▁Body	-8131
+▁fraud	-8132
+cycle	-8133
+▁Deb	-8134
+from	-8135
+▁AB	-8136
+▁Edition	-8137
+▁SEO	-8138
+▁smoke	-8139
+that	-8140
+▁Pennsylvania	-8141
+▁Steven	-8142
+▁Sug	-8143
+▁Creative	-8144
+▁artwork	-8145
+▁chips	-8146
+▁engaging	-8147
+▁phr	-8148
+▁Grow	-8149
+▁drawn	-8150
+▁signature	-8151
+▁coaching	-8152
+Do	-8153
+▁flexibility	-8154
+riculum	-8155
+▁developer	-8156
+▁bact	-8157
+▁samples	-8158
+▁resort	-8159
+▁newsletter	-8160
+▁minister	-8161
+▁Sydney	-8162
+achelor	-8163
+▁Jordan	-8164
+icide	-8165
+usive	-8166
+▁Bry	-8167
+▁Stone	-8168
+▁Single	-8169
+umer	-8170
+▁golden	-8171
+▁speakers	-8172
+▁Money	-8173
+▁regions	-8174
+▁Forum	-8175
+▁Must	-8176
+▁accuracy	-8177
+▁invite	-8178
+▁puts	-8179
+▁setup	-8180
+▁NAS	-8181
+▁Perfect	-8182
+pton	-8183
+▁!	-8184
+▁accused	-8185
+▁fault	-8186
+▁Fast	-8187
+▁persons	-8188
+yes	-8189
+lier	-8190
+awa	-8191
+▁Mu	-8192
+unicip	-8193
+▁decent	-8194
+▁Tru	-8195
+▁consideration	-8196
+▁Eth	-8197
+▁Bern	-8198
+▁Holy	-8199
+▁weren	-8200
+▁anxiety	-8201
+▁sees	-8202
+▁electricity	-8203
+▁Sche	-8204
+▁shirt	-8205
+obe	-8206
+▁Kelly	-8207
+▁bold	-8208
+ubs	-8209
+Te	-8210
+▁participation	-8211
+▁discl	-8212
+▁Staff	-8213
+▁farmers	-8214
+▁Bush	-8215
+atile	-8216
+▁wondering	-8217
+▁focusing	-8218
+▁Supreme	-8219
+▁Hills	-8220
+▁Ave	-8221
+▁temperatures	-8222
+▁necessarily	-8223
+PD	-8224
+anted	-8225
+▁Greg	-8226
+month	-8227
+▁grateful	-8228
+▁interviews	-8229
+▁subsequ	-8230
+found	-8231
+▁contributions	-8232
+▁cabin	-8233
+▁virus	-8234
+▁insights	-8235
+▁Manchester	-8236
+▁Philadelphia	-8237
+anguage	-8238
+uminum	-8239
+mates	-8240
+onsin	-8241
+▁yoga	-8242
+.....	-8243
+She	-8244
+▁Histor	-8245
+▁boards	-8246
+▁applying	-8247
+▁fundra	-8248
+▁NO	-8249
+▁rely	-8250
+▁afraid	-8251
+▁lip	-8252
+▁loyal	-8253
+▁sculpt	-8254
+isconsin	-8255
+▁HERE	-8256
+▁Harry	-8257
+▁Father	-8258
+▁texture	-8259
+otive	-8260
+mble	-8261
+▁tor	-8262
+▁Kind	-8263
+▁reaction	-8264
+▁Private	-8265
+▁Details	-8266
+▁seam	-8267
+ifting	-8268
+▁childhood	-8269
+▁stages	-8270
+▁controlled	-8271
+style	-8272
+▁structures	-8273
+▁raising	-8274
+▁recon	-8275
+▁hung	-8276
+▁cameras	-8277
+▁Hay	-8278
+▁brush	-8279
+▁vill	-8280
+▁lies	-8281
+▁compensation	-8282
+Me	-8283
+▁phen	-8284
+▁wanting	-8285
+▁wake	-8286
+▁Reading	-8287
+▁blind	-8288
+bourne	-8289
+▁Nut	-8290
+athered	-8291
+▁promoting	-8292
+▁conserv	-8293
+▁ear	-8294
+▁Scotland	-8295
+▁weapons	-8296
+acious	-8297
+▁personnel	-8298
+TER	-8299
+oted	-8300
+▁struggling	-8301
+▁checked	-8302
+oken	-8303
+▁unknown	-8304
+▁generated	-8305
+▁Eag	-8306
+iary	-8307
+▁writes	-8308
+▁cul	-8309
+claim	-8310
+onder	-8311
+▁zero	-8312
+▁synt	-8313
+▁param	-8314
+▁Comb	-8315
+▁Flo	-8316
+▁clock	-8317
+ista	-8318
+quis	-8319
+▁Tel	-8320
+▁pig	-8321
+▁Him	-8322
+▁sour	-8323
+step	-8324
+▁tweet	-8325
+▁Corn	-8326
+▁Everything	-8327
+including	-8328
+▁panels	-8329
+▁tied	-8330
+▁arrival	-8331
+▁Comments	-8332
+▁Explore	-8333
+▁commonly	-8334
+▁reveal	-8335
+▁Install	-8336
+▁mold	-8337
+▁yours	-8338
+▁Fab	-8339
+▁signal	-8340
+▁Trade	-8341
+▁graphic	-8342
+▁semi	-8343
+CO	-8344
+▁Modern	-8345
+▁convenience	-8346
+▁champion	-8347
+▁Civil	-8348
+▁myst	-8349
+▁recruit	-8350
+▁territ	-8351
+▁teaspoon	-8352
+▁mac	-8353
+▁promotion	-8354
+high	-8355
+▁vess	-8356
+▁Matthew	-8357
+▁Ever	-8358
+date	-8359
+▁ly	-8360
+▁Attorney	-8361
+▁chest	-8362
+▁Core	-8363
+▁opposite	-8364
+aire	-8365
+gon	-8366
+▁laptop	-8367
+▁wave	-8368
+▁controvers	-8369
+▁Exp	-8370
+▁Regional	-8371
+Sp	-8372
+▁Appro	-8373
+▁warning	-8374
+chers	-8375
+▁comparison	-8376
+wr	-8377
+▁exposed	-8378
+▁entrance	-8379
+▁Iowa	-8380
+▁lake	-8381
+▁harder	-8382
+▁PH	-8383
+elled	-8384
+▁CS	-8385
+ulating	-8386
+▁borrow	-8387
+▁SE	-8388
+▁Protection	-8389
+obody	-8390
+▁exploring	-8391
+▁Arm	-8392
+▁Muslim	-8393
+▁elected	-8394
+▁Dim	-8395
+mun	-8396
+▁criteria	-8397
+▁Sport	-8398
+oof	-8399
+sole	-8400
+▁wooden	-8401
+▁gotten	-8402
+▁codes	-8403
+▁Victoria	-8404
+▁donation	-8405
+▁remained	-8406
+▁flag	-8407
+▁Poly	-8408
+'.	-8409
+▁scoring	-8410
+oda	-8411
+▁escape	-8412
+vironments	-8413
+stract	-8414
+▁laid	-8415
+▁languages	-8416
+▁Prim	-8417
+edge	-8418
+▁vine	-8419
+och	-8420
+sex	-8421
+▁wal	-8422
+eb	-8423
+▁nuclear	-8424
+▁hanging	-8425
+▁subscription	-8426
+ervations	-8427
+▁asset	-8428
+ellect	-8429
+▁outcome	-8430
+▁Patrick	-8431
+▁Bh	-8432
+▁greatly	-8433
+▁Kong	-8434
+▁Pitt	-8435
+▁Ont	-8436
+▁definition	-8437
+▁thinks	-8438
+▁diversity	-8439
+▁Ox	-8440
+▁Orange	-8441
+sized	-8442
+sea	-8443
+gor	-8444
+▁sexy	-8445
+▁addresses	-8446
+▁painted	-8447
+▁toile	-8448
+▁reaching	-8449
+apping	-8450
+week	-8451
+▁Editor	-8452
+▁Hawai	-8453
+▁Elizabeth	-8454
+▁Philipp	-8455
+▁isol	-8456
+▁scores	-8457
+▁pets	-8458
+▁Published	-8459
+hol	-8460
+▁unexpected	-8461
+war	-8462
+itution	-8463
+roit	-8464
+▁Classic	-8465
+angers	-8466
+▁repairs	-8467
+▁Description	-8468
+▁Prior	-8469
+▁shipped	-8470
+▁union	-8471
+▁Features	-8472
+▁Sex	-8473
+▁Innov	-8474
+▁sections	-8475
+power	-8476
+▁Psych	-8477
+▁uncom	-8478
+▁clip	-8479
+▁repeated	-8480
+▁Ah	-8481
+▁admitted	-8482
+words	-8483
+▁durable	-8484
+▁demands	-8485
+▁export	-8486
+olec	-8487
+IGH	-8488
+▁outcomes	-8489
+ira	-8490
+oen	-8491
+nce	-8492
+▁functionality	-8493
+▁Wisconsin	-8494
+▁Bowl	-8495
+▁everybody	-8496
+ivalent	-8497
+▁tsp	-8498
+▁complicated	-8499
+EW	-8500
+▁sin	-8501
+char	-8502
+▁plane	-8503
+▁Lyn	-8504
+▁employers	-8505
+▁Youth	-8506
+screen	-8507
+▁Assistant	-8508
+▁tutorial	-8509
+▁Cool	-8510
+▁fiber	-8511
+▁UC	-8512
+▁ALL	-8513
+▁safely	-8514
+▁struck	-8515
+rets	-8516
+▁twenty	-8517
+▁Manufact	-8518
+▁concepts	-8519
+▁dough	-8520
+▁employer	-8521
+▁representative	-8522
+▁networking	-8523
+▁alarm	-8524
+▁toys	-8525
+▁plain	-8526
+▁Bat	-8527
+▁Gi	-8528
+▁Bow	-8529
+▁Hur	-8530
+▁requirement	-8531
+▁obst	-8532
+▁tum	-8533
+▁contribution	-8534
+▁Collect	-8535
+rug	-8536
+▁certificate	-8537
+▁Ten	-8538
+▁Advis	-8539
+▁studying	-8540
+▁Var	-8541
+▁Additional	-8542
+▁evaluation	-8543
+▁revolution	-8544
+▁instruction	-8545
+▁Chapter	-8546
+▁Simply	-8547
+eping	-8548
+▁deploy	-8549
+▁jail	-8550
+erning	-8551
+▁cooked	-8552
+heim	-8553
+▁Rick	-8554
+▁Industry	-8555
+▁bands	-8556
+▁completion	-8557
+▁newest	-8558
+▁matching	-8559
+▁Latin	-8560
+▁beef	-8561
+▁tradem	-8562
+minute	-8563
+ruption	-8564
+▁calm	-8565
+▁Exchange	-8566
+▁temporary	-8567
+dale	-8568
+arring	-8569
+▁Blu	-8570
+rog	-8571
+▁cher	-8572
+ieties	-8573
+▁strongly	-8574
+▁Indiana	-8575
+▁possess	-8576
+▁Nav	-8577
+▁guilty	-8578
+▁Complete	-8579
+▁Halloween	-8580
+▁Championship	-8581
+▁initially	-8582
+▁bass	-8583
+aver	-8584
+▁deeply	-8585
+▁hip	-8586
+▁waters	-8587
+▁holes	-8588
+▁arr	-8589
+▁maintained	-8590
+▁Kar	-8591
+utter	-8592
+ko	-8593
+▁organisation	-8594
+▁Send	-8595
+lude	-8596
+pense	-8597
+▁Reviews	-8598
+▁Employ	-8599
+▁releases	-8600
+▁romantic	-8601
+▁screw	-8602
+▁residence	-8603
+▁Entertain	-8604
+▁Glass	-8605
+▁hearts	-8606
+▁reception	-8607
+▁pizza	-8608
+ocal	-8609
+▁Host	-8610
+▁Cancer	-8611
+▁Method	-8612
+%,	-8613
+▁hes	-8614
+▁Currently	-8615
+▁requested	-8616
+▁actress	-8617
+oz	-8618
+enities	-8619
+Get	-8620
+erving	-8621
+xic	-8622
+oir	-8623
+▁salad	-8624
+▁blogs	-8625
+▁exhaust	-8626
+▁voters	-8627
+Tr	-8628
+▁Viet	-8629
+▁supplement	-8630
+▁approaches	-8631
+vals	-8632
+▁singing	-8633
+▁satisfied	-8634
+▁Donald	-8635
+▁subjects	-8636
+▁Circ	-8637
+▁tablet	-8638
+▁Independ	-8639
+▁haz	-8640
+▁Days	-8641
+ounge	-8642
+▁Brother	-8643
+▁coal	-8644
+▁Finance	-8645
+▁loud	-8646
+▁sought	-8647
+▁sorry	-8648
+iche	-8649
+IST	-8650
+▁represented	-8651
+▁Nation	-8652
+▁boot	-8653
+▁swing	-8654
+▁Tam	-8655
+▁Ak	-8656
+▁Update	-8657
+▁Questions	-8658
+▁southern	-8659
+▁franchise	-8660
+▁carpet	-8661
+▁Content	-8662
+▁Six	-8663
+▁kil	-8664
+oore	-8665
+▁prime	-8666
+▁bone	-8667
+▁Ban	-8668
+▁Anton	-8669
+▁Drag	-8670
+▁slee	-8671
+▁III	-8672
+▁Five	-8673
+▁insert	-8674
+▁athletes	-8675
+▁reject	-8676
+▁convert	-8677
+▁Sir	-8678
+▁frustr	-8679
+▁Bud	-8680
+▁stored	-8681
+▁infection	-8682
+▁automatic	-8683
+▁Hong	-8684
+▁cancell	-8685
+▁flights	-8686
+▁overse	-8687
+▁environments	-8688
+▁Planning	-8689
+▁bull	-8690
+▁Jess	-8691
+▁tact	-8692
+▁errors	-8693
+▁inventory	-8694
+▁Bath	-8695
+▁desktop	-8696
+oln	-8697
+▁Du	-8698
+charge	-8699
+▁chef	-8700
+▁prayer	-8701
+▁hopefully	-8702
+▁orient	-8703
+▁sink	-8704
+▁dual	-8705
+▁Round	-8706
+▁Sony	-8707
+▁falls	-8708
+▁unusual	-8709
+▁investments	-8710
+▁donations	-8711
+▁comedy	-8712
+agen	-8713
+▁Self	-8714
+▁BM	-8715
+▁delivers	-8716
+▁Wars	-8717
+izon	-8718
+▁Study	-8719
+▁stamp	-8720
+▁gap	-8721
+▁nose	-8722
+▁pregnant	-8723
+▁discussions	-8724
+itis	-8725
+link	-8726
+▁Ocean	-8727
+ormal	-8728
+▁TR	-8729
+urious	-8730
+ificial	-8731
+▁accommodation	-8732
+▁false	-8733
+▁granted	-8734
+▁initiatives	-8735
+▁ft	-8736
+▁transactions	-8737
+▁McG	-8738
+▁stylish	-8739
+aret	-8740
+game	-8741
+▁react	-8742
+▁rapidly	-8743
+▁Restaur	-8744
+▁Iss	-8745
+▁Lewis	-8746
+▁transaction	-8747
+▁strengthen	-8748
+▁Univers	-8749
+▁worn	-8750
+▁ladies	-8751
+▁lens	-8752
+▁indoor	-8753
+ART	-8754
+▁Tal	-8755
+▁pregnancy	-8756
+▁experim	-8757
+▁hiring	-8758
+▁dispos	-8759
+▁ET	-8760
+▁directions	-8761
+oons	-8762
+▁certification	-8763
+oa	-8764
+▁ensures	-8765
+imp	-8766
+▁finishing	-8767
+▁crypt	-8768
+ji	-8769
+Qu	-8770
+▁Sciences	-8771
+▁Property	-8772
+ingham	-8773
+hire	-8774
+▁largely	-8775
+▁edit	-8776
+via	-8777
+▁resume	-8778
+▁Large	-8779
+Every	-8780
+▁extraord	-8781
+amental	-8782
+▁Major	-8783
+olly	-8784
+▁Exam	-8785
+▁Electric	-8786
+▁deeper	-8787
+▁therap	-8788
+lah	-8789
+▁hyp	-8790
+▁Saint	-8791
+▁Ren	-8792
+otes	-8793
+▁acquired	-8794
+▁trusted	-8795
+▁surve	-8796
+▁Benef	-8797
+▁Greek	-8798
+df	-8799
+▁Style	-8800
+▁catalog	-8801
+▁cats	-8802
+etts	-8803
+▁restrictions	-8804
+▁Fresh	-8805
+▁Prince	-8806
+▁branch	-8807
+▁Performance	-8808
+▁ranging	-8809
+HL	-8810
+▁globe	-8811
+▁Application	-8812
+rape	-8813
+▁Less	-8814
+▁aimed	-8815
+uties	-8816
+▁collections	-8817
+ds	-8818
+▁hitting	-8819
+▁WW	-8820
+igious	-8821
+piece	-8822
+▁Michel	-8823
+▁mild	-8824
+▁Democratic	-8825
+▁emerging	-8826
+▁underst	-8827
+▁Computer	-8828
+▁correctly	-8829
+▁Delivery	-8830
+Ne	-8831
+ensus	-8832
+▁makeup	-8833
+world	-8834
+elle	-8835
+▁HP	-8836
+▁expanded	-8837
+aug	-8838
+▁Architect	-8839
+▁literature	-8840
+▁attitude	-8841
+orship	-8842
+▁targeted	-8843
+▁Comment	-8844
+TE	-8845
+▁Early	-8846
+onymous	-8847
+▁finest	-8848
+▁workplace	-8849
+▁mistake	-8850
+orses	-8851
+▁Course	-8852
+▁Op	-8853
+Hz	-8854
+▁gate	-8855
+▁contained	-8856
+ffective	-8857
+▁diamond	-8858
+▁Barn	-8859
+wall	-8860
+agers	-8861
+▁fabulous	-8862
+ALL	-8863
+▁Snow	-8864
+isdom	-8865
+▁tissue	-8866
+▁Ross	-8867
+▁surge	-8868
+▁Parent	-8869
+▁rib	-8870
+▁Such	-8871
+American	-8872
+doors	-8873
+▁BBC	-8874
+▁COM	-8875
+IVE	-8876
+▁alt	-8877
+▁hell	-8878
+▁sed	-8879
+ceeds	-8880
+▁tube	-8881
+▁tons	-8882
+▁homeown	-8883
+▁Tro	-8884
+▁contracts	-8885
+▁Environmental	-8886
+▁experiencing	-8887
+▁offensive	-8888
+nie	-8889
+▁mask	-8890
+achus	-8891
+uana	-8892
+▁skilled	-8893
+▁weigh	-8894
+▁estimate	-8895
+▁sne	-8896
+bound	-8897
+iffe	-8898
+▁containing	-8899
+▁appointed	-8900
+▁Section	-8901
+▁sophist	-8902
+▁Challenge	-8903
+▁rolling	-8904
+UST	-8905
+swered	-8906
+▁refrig	-8907
+▁bub	-8908
+▁Laure	-8909
+▁Hom	-8910
+▁SA	-8911
+▁themes	-8912
+▁demonstrate	-8913
+▁Premier	-8914
+▁engines	-8915
+▁suffer	-8916
+essee	-8917
+▁Entertainment	-8918
+▁sufficient	-8919
+tes	-8920
+▁telephone	-8921
+itate	-8922
+▁weird	-8923
+othe	-8924
+uters	-8925
+▁container	-8926
+Free	-8927
+▁editing	-8928
+rus	-8929
+▁Moon	-8930
+viously	-8931
+▁Advent	-8932
+▁stayed	-8933
+▁Os	-8934
+▁Text	-8935
+▁personalized	-8936
+han	-8937
+covery	-8938
+PL	-8939
+UD	-8940
+▁subsid	-8941
+pay	-8942
+▁equally	-8943
+▁Song	-8944
+▁Celebr	-8945
+▁ministry	-8946
+▁workout	-8947
+▁copies	-8948
+▁assume	-8949
+▁segment	-8950
+▁formula	-8951
+▁defensive	-8952
+▁Integr	-8953
+graduate	-8954
+▁math	-8955
+▁Construction	-8956
+incoln	-8957
+▁floors	-8958
+▁aged	-8959
+orneys	-8960
+▁Side	-8961
+▁photographer	-8962
+▁responded	-8963
+core	-8964
+▁Cooper	-8965
+▁voted	-8966
+▁Server	-8967
+▁Buff	-8968
+▁drives	-8969
+raine	-8970
+▁sentence	-8971
+▁tours	-8972
+▁roots	-8973
+aya	-8974
+ela	-8975
+▁Anderson	-8976
+▁couples	-8977
+▁recru	-8978
+▁Dur	-8979
+aga	-8980
+▁Events	-8981
+▁Advanced	-8982
+▁drain	-8983
+▁utility	-8984
+orms	-8985
+▁breaks	-8986
+▁shapes	-8987
+▁BC	-8988
+▁Compet	-8989
+▁outs	-8990
+▁’	-8991
+▁indicate	-8992
+▁Channel	-8993
+▁purchases	-8994
+▁stood	-8995
+Last	-8996
+▁beans	-8997
+▁pilot	-8998
+▁viewed	-8999
+▁inspect	-9000
+▁admit	-9001
+▁nursing	-9002
+▁layout	-9003
+▁Double	-9004
+cut	-9005
+icking	-9006
+achusetts	-9007
+▁aest	-9008
+▁Linux	-9009
+▁stability	-9010
+▁wise	-9011
+▁advantages	-9012
+▁Galaxy	-9013
+otte	-9014
+▁muscles	-9015
+▁wrap	-9016
+▁WordPress	-9017
+gi	-9018
+▁Bring	-9019
+▁sear	-9020
+▁Veter	-9021
+▁streaming	-9022
+From	-9023
+▁ranked	-9024
+inar	-9025
+▁Allen	-9026
+gree	-9027
+▁Lock	-9028
+||$	-9029
+▁Les	-9030
+▁Laur	-9031
+▁powered	-9032
+▁buff	-9033
+▁Inn	-9034
+▁hosts	-9035
+▁Tickets	-9036
+ouses	-9037
+▁displays	-9038
+▁outfit	-9039
+oli	-9040
+▁Iron	-9041
+▁Sant	-9042
+▁keys	-9043
+▁religion	-9044
+▁Ath	-9045
+▁Along	-9046
+▁campaigns	-9047
+home	-9048
+▁Mind	-9049
+▁Hair	-9050
+iour	-9051
+▁align	-9052
+▁conscious	-9053
+▁Shel	-9054
+▁amend	-9055
+bered	-9056
+▁excite	-9057
+▁Grant	-9058
+▁leaf	-9059
+▁Detroit	-9060
+▁Anthony	-9061
+▁grace	-9062
+▁explan	-9063
+▁belt	-9064
+▁login	-9065
+▁nutrition	-9066
+hell	-9067
+▁Near	-9068
+▁lowest	-9069
+▁Mail	-9070
+▁IM	-9071
+▁Gre	-9072
+iscal	-9073
+▁emotions	-9074
+▁studied	-9075
+▁File	-9076
+▁killing	-9077
+▁equivalent	-9078
+▁Sweet	-9079
+▁onion	-9080
+▁Overall	-9081
+▁Football	-9082
+▁alert	-9083
+▁respectively	-9084
+SW	-9085
+▁cam	-9086
+▁Thus	-9087
+▁Heat	-9088
+▁accounting	-9089
+▁neither	-9090
+zed	-9091
+▁Final	-9092
+▁peak	-9093
+inyl	-9094
+▁productivity	-9095
+looking	-9096
+▁Albert	-9097
+▁equity	-9098
+▁fewer	-9099
+utor	-9100
+▁FR	-9101
+▁statistics	-9102
+▁evil	-9103
+▁Kir	-9104
+▁Marsh	-9105
+cor	-9106
+ESS	-9107
+▁SD	-9108
+OG	-9109
+▁junior	-9110
+ouri	-9111
+▁organisations	-9112
+▁neut	-9113
+▁Maryland	-9114
+▁god	-9115
+▁Categ	-9116
+▁Tree	-9117
+▁Mini	-9118
+rat	-9119
+▁importantly	-9120
+Post	-9121
+▁Mayor	-9122
+▁institution	-9123
+ellite	-9124
+▁encounter	-9125
+athon	-9126
+▁Title	-9127
+▁Ontario	-9128
+iatric	-9129
+▁Nig	-9130
+▁Girls	-9131
+▁objective	-9132
+lined	-9133
+▁respective	-9134
+opher	-9135
+fire	-9136
+ervice	-9137
+▁letting	-9138
+cery	-9139
+▁Denver	-9140
+▁Records	-9141
+▁locally	-9142
+undry	-9143
+ighth	-9144
+umni	-9145
+▁deserve	-9146
+loved	-9147
+▁Address	-9148
+ECT	-9149
+▁Clinton	-9150
+Ab	-9151
+▁parks	-9152
+▁Register	-9153
+▁comprom	-9154
+!!!!	-9155
+▁Roll	-9156
+▁Valent	-9157
+▁URL	-9158
+▁veteran	-9159
+▁dess	-9160
+wa	-9161
+▁layers	-9162
+▁babies	-9163
+▁compliment	-9164
+▁Article	-9165
+▁characteristics	-9166
+▁Index	-9167
+▁earnings	-9168
+▁operated	-9169
+▁Clark	-9170
+email	-9171
+▁maps	-9172
+▁adopted	-9173
+roduction	-9174
+▁combat	-9175
+▁banking	-9176
+KE	-9177
+▁beneficial	-9178
+▁suppliers	-9179
+▁por	-9180
+▁Always	-9181
+▁entrepreneurs	-9182
+▁Hu	-9183
+onda	-9184
+criptions	-9185
+▁votes	-9186
+▁Release	-9187
+▁ru	-9188
+▁Polit	-9189
+▁stocks	-9190
+▁mistakes	-9191
+▁Sure	-9192
+▁losses	-9193
+▁Coach	-9194
+▁balls	-9195
+fa	-9196
+▁Trail	-9197
+▁worried	-9198
+ILL	-9199
+py	-9200
+▁Inv	-9201
+dri	-9202
+▁Birth	-9203
+▁possibilities	-9204
+▁resid	-9205
+▁diabetes	-9206
+pany	-9207
+▁rubber	-9208
+▁patch	-9209
+lahoma	-9210
+▁Hart	-9211
+▁Trad	-9212
+ousing	-9213
+▁Chem	-9214
+▁Craft	-9215
+assion	-9216
+▁hide	-9217
+oding	-9218
+▁spir	-9219
+mate	-9220
+FF	-9221
+▁complement	-9222
+terest	-9223
+▁pursue	-9224
+▁inspire	-9225
+▁expanding	-9226
+▁Az	-9227
+▁Rain	-9228
+▁AI	-9229
+▁attempts	-9230
+▁Brew	-9231
+▁walks	-9232
+era	-9233
+▁refres	-9234
+▁dies	-9235
+▁pul	-9236
+ogen	-9237
+▁Speed	-9238
+▁delighted	-9239
+overs	-9240
+▁graduated	-9241
+▁ownership	-9242
+olen	-9243
+oto	-9244
+▁continu	-9245
+▁acquisition	-9246
+BM	-9247
+▁intention	-9248
+▁venture	-9249
+▁shortly	-9250
+▁prohib	-9251
+Pr	-9252
+▁Massachusetts	-9253
+grade	-9254
+▁laser	-9255
+QL	-9256
+▁aer	-9257
+▁brothers	-9258
+▁Olympic	-9259
+▁auction	-9260
+abama	-9261
+▁consequences	-9262
+▁abilities	-9263
+▁sevent	-9264
+▁vendors	-9265
+▁tender	-9266
+▁picking	-9267
+▁checks	-9268
+ijuana	-9269
+▁Da	-9270
+▁bomb	-9271
+▁aband	-9272
+▁depression	-9273
+▁spectacular	-9274
+bie	-9275
+▁hunt	-9276
+▁Prep	-9277
+cked	-9278
+itches	-9279
+dep	-9280
+▁therm	-9281
+▁stainless	-9282
+angle	-9283
+▁distributed	-9284
+',	-9285
+▁ske	-9286
+▁crown	-9287
+▁unif	-9288
+▁celebrated	-9289
+▁oral	-9290
+▁squad	-9291
+▁occurs	-9292
+View	-9293
+▁Ult	-9294
+▁implemented	-9295
+ceived	-9296
+▁Schools	-9297
+▁reviewed	-9298
+▁Future	-9299
+▁bol	-9300
+▁Mort	-9301
+▁exercises	-9302
+▁suite	-9303
+▁secondary	-9304
+▁Gift	-9305
+▁gathering	-9306
+ii	-9307
+▁Jane	-9308
+▁consistently	-9309
+▁pointed	-9310
+▁representatives	-9311
+▁reportedly	-9312
+▁northern	-9313
+▁entitled	-9314
+berries	-9315
+▁vibrant	-9316
+▁inspection	-9317
+incess	-9318
+▁survive	-9319
+▁chronic	-9320
+▁lawyers	-9321
+▁Years	-9322
+▁Pers	-9323
+▁Rober	-9324
+overty	-9325
+▁Tell	-9326
+▁passes	-9327
+▁strip	-9328
+▁gig	-9329
+▁instantly	-9330
+▁Unt	-9331
+▁Remove	-9332
+through	-9333
+▁retired	-9334
+▁cater	-9335
+unate	-9336
+▁shr	-9337
+▁coaches	-9338
+▁retailers	-9339
+▁vert	-9340
+▁enhanced	-9341
+▁appreciated	-9342
+▁Bird	-9343
+▁Machine	-9344
+▁disaster	-9345
+▁bes	-9346
+▁Stadium	-9347
+▁deadline	-9348
+store	-9349
+▁investing	-9350
+shop	-9351
+▁Corp	-9352
+ortion	-9353
+▁hydro	-9354
+▁Tips	-9355
+▁argument	-9356
+▁Gun	-9357
+▁pip	-9358
+aha	-9359
+▁gre	-9360
+▁Tennessee	-9361
+▁Anyone	-9362
+▁brew	-9363
+▁transmission	-9364
+▁Wales	-9365
+urities	-9366
+▁fiction	-9367
+oust	-9368
+gov	-9369
+▁Simon	-9370
+▁curriculum	-9371
+▁mountains	-9372
+olar	-9373
+abil	-9374
+▁Better	-9375
+▁ratio	-9376
+▁reply	-9377
+▁universities	-9378
+▁advertise	-9379
+tical	-9380
+mouth	-9381
+▁Sab	-9382
+build	-9383
+▁Beh	-9384
+▁overnight	-9385
+▁sacr	-9386
+▁soup	-9387
+▁knee	-9388
+▁iconic	-9389
+mo	-9390
+▁tackle	-9391
+mont	-9392
+inst	-9393
+▁hyper	-9394
+▁Unit	-9395
+▁privile	-9396
+▁thousand	-9397
+ussion	-9398
+▁suddenly	-9399
+erk	-9400
+▁Pear	-9401
+idal	-9402
+ami	-9403
+▁Lincoln	-9404
+▁Wire	-9405
+▁shed	-9406
+▁CT	-9407
+encing	-9408
+▁restore	-9409
+▁impressed	-9410
+▁concerning	-9411
+Pe	-9412
+iah	-9413
+amics	-9414
+itty	-9415
+▁cow	-9416
+▁amongst	-9417
+▁Communications	-9418
+▁followers	-9419
+ouver	-9420
+▁Relations	-9421
+▁documentation	-9422
+▁philosophy	-9423
+▁answered	-9424
+▁Commercial	-9425
+sect	-9426
+▁offense	-9427
+▁western	-9428
+After	-9429
+▁HR	-9430
+▁repeat	-9431
+▁decline	-9432
+▁beautifully	-9433
+▁circle	-9434
+▁abroad	-9435
+▁fundamental	-9436
+airy	-9437
+▁championship	-9438
+▁extent	-9439
+osit	-9440
+▁stom	-9441
+owned	-9442
+▁heavily	-9443
+▁explos	-9444
+eters	-9445
+▁servers	-9446
+▁compatible	-9447
+FT	-9448
+fish	-9449
+▁Jean	-9450
+▁PL	-9451
+▁Governor	-9452
+Des	-9453
+▁heritage	-9454
+▁contributed	-9455
+apolis	-9456
+▁Harris	-9457
+▁ink	-9458
+▁credits	-9459
+Not	-9460
+amon	-9461
+▁wildlife	-9462
+▁passengers	-9463
+▁Wine	-9464
+▁interaction	-9465
+inth	-9466
+sis	-9467
+hyth	-9468
+▁quotes	-9469
+▁describes	-9470
+ugs	-9471
+rection	-9472
+Ind	-9473
+▁captured	-9474
+▁phenomen	-9475
+▁Nature	-9476
+▁producers	-9477
+▁Gas	-9478
+▁Kit	-9479
+▁Roy	-9480
+▁Portland	-9481
+▁IC	-9482
+▁beds	-9483
+▁wheels	-9484
+▁Springs	-9485
+▁gathered	-9486
+▁adequ	-9487
+ellectual	-9488
+gorith	-9489
+gged	-9490
+pon	-9491
+▁perm	-9492
+▁User	-9493
+▁semin	-9494
+▁handy	-9495
+▁Burn	-9496
+▁Delhi	-9497
+gener	-9498
+▁discounts	-9499
+ustration	-9500
+▁Turkey	-9501
+▁minds	-9502
+▁fruits	-9503
+▁salary	-9504
+▁Rail	-9505
+▁operational	-9506
+▁sponsored	-9507
+▁dramatic	-9508
+▁currency	-9509
+▁Afghan	-9510
+▁Making	-9511
+▁applicable	-9512
+▁meters	-9513
+▁Ast	-9514
+▁assigned	-9515
+▁SO	-9516
+semble	-9517
+▁Morgan	-9518
+▁Fashion	-9519
+▁generations	-9520
+abling	-9521
+▁Several	-9522
+quarters	-9523
+▁Updated	-9524
+ICE	-9525
+▁customized	-9526
+▁excellence	-9527
+▁overcome	-9528
+▁periods	-9529
+enth	-9530
+▁mature	-9531
+▁happiness	-9532
+ima	-9533
+▁targets	-9534
+▁Cart	-9535
+▁Justin	-9536
+▁rated	-9537
+▁bedrooms	-9538
+▁Bab	-9539
+▁NBA	-9540
+▁Vit	-9541
+▁portra	-9542
+▁indicated	-9543
+▁moisture	-9544
+▁Represent	-9545
+▁Altern	-9546
+▁chairman	-9547
+▁compact	-9548
+▁Rap	-9549
+▁Judge	-9550
+▁cann	-9551
+▁continuous	-9552
+▁Oklahoma	-9553
+▁empower	-9554
+▁divorce	-9555
+▁template	-9556
+FO	-9557
+iere	-9558
+▁representing	-9559
+▁Results	-9560
+enna	-9561
+▁vanilla	-9562
+ancouver	-9563
+▁dancing	-9564
+▁Safe	-9565
+▁scratch	-9566
+▁Doctor	-9567
+▁Cow	-9568
+▁conversations	-9569
+▁Alabama	-9570
+▁alter	-9571
+▁hospitals	-9572
+olds	-9573
+offs	-9574
+▁moon	-9575
+Res	-9576
+▁medication	-9577
+▁sleeping	-9578
+▁Authority	-9579
+▁stack	-9580
+▁accommodate	-9581
+▁Tan	-9582
+racy	-9583
+▁Easter	-9584
+▁Rh	-9585
+▁meaningful	-9586
+▁sectors	-9587
+▁Dress	-9588
+▁lasting	-9589
+▁actively	-9590
+▁scar	-9591
+▁harvest	-9592
+▁apple	-9593
+rot	-9594
+▁observed	-9595
+▁terr	-9596
+▁Lat	-9597
+olid	-9598
+▁races	-9599
+▁Balt	-9600
+ibl	-9601
+iate	-9602
+▁Lam	-9603
+uition	-9604
+▁drum	-9605
+ITY	-9606
+▁Getting	-9607
+▁okay	-9608
+atal	-9609
+▁rehab	-9610
+▁reserve	-9611
+orse	-9612
+▁voting	-9613
+▁hub	-9614
+▁elections	-9615
+▁nail	-9616
+▁gambling	-9617
+▁cust	-9618
+May	-9619
+▁compan	-9620
+▁Coc	-9621
+▁excitement	-9622
+eman	-9623
+▁impression	-9624
+▁entries	-9625
+▁Dor	-9626
+▁Salt	-9627
+works	-9628
+▁comic	-9629
+itzer	-9630
+▁ware	-9631
+rix	-9632
+▁wider	-9633
+▁Chel	-9634
+agan	-9635
+▁potatoes	-9636
+▁shade	-9637
+▁Democrats	-9638
+▁evolution	-9639
+▁entertaining	-9640
+this	-9641
+▁rescue	-9642
+ropical	-9643
+▁straw	-9644
+▁lun	-9645
+▁bell	-9646
+▁Cher	-9647
+▁supporters	-9648
+▁protocol	-9649
+▁pushed	-9650
+ika	-9651
+▁alike	-9652
+▁smoking	-9653
+▁Deep	-9654
+▁hun	-9655
+▁Cash	-9656
+▁Affairs	-9657
+▁Atlantic	-9658
+DC	-9659
+oenix	-9660
+▁Move	-9661
+▁coconut	-9662
+▁chip	-9663
+▁existence	-9664
+▁remem	-9665
+▁Rout	-9666
+igen	-9667
+town	-9668
+aware	-9669
+aux	-9670
+▁Andy	-9671
+▁eliminate	-9672
+ige	-9673
+▁pushing	-9674
+▁Chairman	-9675
+▁pour	-9676
+▁responses	-9677
+▁heaven	-9678
+Welcome	-9679
+▁stops	-9680
+▁Plant	-9681
+oper	-9682
+▁Utah	-9683
+▁inquir	-9684
+▁mirror	-9685
+▁opposition	-9686
+iameter	-9687
+▁developments	-9688
+▁publishing	-9689
+▁frozen	-9690
+opping	-9691
+▁Navy	-9692
+▁Aqu	-9693
+?!	-9694
+▁celebrating	-9695
+▁Holiday	-9696
+▁amenities	-9697
+▁Imp	-9698
+▁sheets	-9699
+▁generous	-9700
+▁viewers	-9701
+▁substantial	-9702
+▁Moore	-9703
+▁withdraw	-9704
+▁objectives	-9705
+▁fired	-9706
+▁Malays	-9707
+was	-9708
+▁chemicals	-9709
+▁Agr	-9710
+▁Ped	-9711
+▁accord	-9712
+▁processed	-9713
+▁Budd	-9714
+▁Py	-9715
+DP	-9716
+▁financing	-9717
+▁flaw	-9718
+▁thoroughly	-9719
+▁wound	-9720
+▁completing	-9721
+▁asks	-9722
+▁chairs	-9723
+Ed	-9724
+▁Lane	-9725
+▁ceiling	-9726
+vo	-9727
+▁relative	-9728
+apped	-9729
+▁robust	-9730
+▁Virt	-9731
+▁hunting	-9732
+▁actors	-9733
+▁Bron	-9734
+▁recall	-9735
+▁regulatory	-9736
+mart	-9737
+attan	-9738
+boards	-9739
+▁gal	-9740
+children	-9741
+▁surrounded	-9742
+▁accompan	-9743
+uild	-9744
+▁instruments	-9745
+▁physician	-9746
+BO	-9747
+▁Besides	-9748
+rer	-9749
+▁Version	-9750
+▁preview	-9751
+▁lawn	-9752
+▁buttons	-9753
+▁extraordinary	-9754
+▁diagnosis	-9755
+▁precious	-9756
+▁Melbourne	-9757
+▁folder	-9758
+▁engineers	-9759
+▁protecting	-9760
+▁spokesman	-9761
+▁virtually	-9762
+▁emotion	-9763
+▁relaxing	-9764
+prof	-9765
+papers	-9766
+▁fib	-9767
+Rep	-9768
+▁habits	-9769
+▁Works	-9770
+▁specialists	-9771
+ez	-9772
+ridge	-9773
+▁courts	-9774
+osa	-9775
+maker	-9776
+unicipal	-9777
+▁Kentucky	-9778
+▁Mand	-9779
+▁soccer	-9780
+▁Nothing	-9781
+▁Cub	-9782
+▁padd	-9783
+▁caps	-9784
+otion	-9785
+▁encouraging	-9786
+▁penalty	-9787
+mann	-9788
+▁Highway	-9789
+▁fingers	-9790
+▁hal	-9791
+elson	-9792
+▁oils	-9793
+ocument	-9794
+▁exclusively	-9795
+▁”	-9796
+▁cooperation	-9797
+elve	-9798
+▁TX	-9799
+▁latter	-9800
+▁Casino	-9801
+▁Gall	-9802
+▁Bureau	-9803
+▁prev	-9804
+▁seal	-9805
+▁desper	-9806
+organ	-9807
+▁Mission	-9808
+▁proved	-9809
+http	-9810
+▁theater	-9811
+▁balanced	-9812
+▁designing	-9813
+▁buyer	-9814
+Us	-9815
+▁Original	-9816
+▁evaluate	-9817
+,'	-9818
+omed	-9819
+▁vulnerable	-9820
+▁libr	-9821
+▁sixth	-9822
+▁backup	-9823
+▁Missouri	-9824
+bean	-9825
+▁surely	-9826
+▁prevention	-9827
+▁Legal	-9828
+▁scope	-9829
+▁resulted	-9830
+▁ward	-9831
+▁Answ	-9832
+▁qualify	-9833
+▁complaint	-9834
+asty	-9835
+▁Jacob	-9836
+▁Phill	-9837
+break	-9838
+▁module	-9839
+▁spell	-9840
+▁dil	-9841
+▁Meeting	-9842
+▁subscribe	-9843
+▁fur	-9844
+▁seller	-9845
+UC	-9846
+These	-9847
+▁Dance	-9848
+▁snap	-9849
+▁DNA	-9850
+▁bug	-9851
+▁cabinet	-9852
+iology	-9853
+▁Economic	-9854
+atern	-9855
+▁Kate	-9856
+▁da	-9857
+liances	-9858
+▁Tai	-9859
+▁tremend	-9860
+▁discovery	-9861
+▁integrity	-9862
+▁Chap	-9863
+%)	-9864
+PP	-9865
+laim	-9866
+ACK	-9867
+▁listings	-9868
+▁dismiss	-9869
+▁rarely	-9870
+▁showcase	-9871
+eness	-9872
+▁USD	-9873
+▁cinem	-9874
+isms	-9875
+▁discrim	-9876
+▁refe	-9877
+▁resolve	-9878
+▁Certified	-9879
+▁exch	-9880
+?)	-9881
+▁toler	-9882
+▁Gary	-9883
+▁musicians	-9884
+▁tennis	-9885
+nut	-9886
+upid	-9887
+▁flavors	-9888
+▁suspend	-9889
+▁guides	-9890
+▁contents	-9891
+▁Moh	-9892
+▁consulting	-9893
+▁divided	-9894
+▁nutri	-9895
+ders	-9896
+▁MB	-9897
+▁greet	-9898
+uan	-9899
+▁favorites	-9900
+▁define	-9901
+▁somehow	-9902
+cho	-9903
+▁Eve	-9904
+itters	-9905
+▁Beautiful	-9906
+▁burning	-9907
+▁Hig	-9908
+NS	-9909
+▁Present	-9910
+emed	-9911
+▁AV	-9912
+Col	-9913
+▁prints	-9914
+ova	-9915
+▁deaths	-9916
+▁edges	-9917
+▁betting	-9918
+▁enjoyable	-9919
+inkle	-9920
+▁Republicans	-9921
+Add	-9922
+▁Sac	-9923
+▁nerv	-9924
+▁Ark	-9925
+market	-9926
+▁produces	-9927
+▁Vancouver	-9928
+▁estimates	-9929
+▁artistic	-9930
+ims	-9931
+▁adj	-9932
+▁bacteria	-9933
+▁Language	-9934
+▁danger	-9935
+▁courtesy	-9936
+▁worship	-9937
+INE	-9938
+▁Assembly	-9939
+▁compos	-9940
+ASE	-9941
+enter	-9942
+▁Maint	-9943
+▁span	-9944
+▁Raj	-9945
+▁powers	-9946
+▁fascinating	-9947
+don	-9948
+▁Technical	-9949
+▁Practice	-9950
+umbing	-9951
+udes	-9952
+▁exterior	-9953
+▁EV	-9954
+Can	-9955
+▁conventional	-9956
+▁principal	-9957
+▁Wy	-9958
+▁Ele	-9959
+▁Diamond	-9960
+▁DE	-9961
+▁popularity	-9962
+▁mystery	-9963
+▁bottles	-9964
+▁screening	-9965
+▁Liver	-9966
+▁summary	-9967
+ahoo	-9968
+▁Together	-9969
+ori	-9970
+▁Subscribe	-9971
+▁Leave	-9972
+▁Lu	-9973
+▁caring	-9974
+▁Jennifer	-9975
+▁grounds	-9976
+▁Ta	-9977
+▁addressed	-9978
+▁dirt	-9979
+▁assembly	-9980
+▁legacy	-9981
+▁outdoors	-9982
+▁coron	-9983
+▁processor	-9984
+estone	-9985
+▁duties	-9986
+▁abund	-9987
+▁toy	-9988
+▁Peace	-9989
+▁settlement	-9990
+arts	-9991
+▁elder	-9992
+yal	-9993
+addy	-9994
+▁rust	-9995
+TR	-9996
+▁YOUR	-9997
+▁divide	-9998
+▁employed	-9999
+▁Photos	-10000
+▁Coffee	-10001
+ologists	-10002
+▁strive	-10003
+▁pound	-10004
+▁exists	-10005
+▁remarkable	-10006
+▁ultra	-10007
+▁acres	-10008
+▁ABC	-10009
+▁Gab	-10010
+▁uncertain	-10011
+▁toilet	-10012
+▁incent	-10013
+▁arrangements	-10014
+▁Scient	-10015
+▁Particip	-10016
+▁Clear	-10017
+▁Beat	-10018
+▁Korean	-10019
+▁Cell	-10020
+shirt	-10021
+eland	-10022
+▁Brooklyn	-10023
+▁keen	-10024
+▁fert	-10025
+▁HIV	-10026
+▁aluminum	-10027
+▁Junior	-10028
+▁frequent	-10029
+▁acquire	-10030
+rep	-10031
+▁lean	-10032
+▁Phoenix	-10033
+▁reven	-10034
+▁emissions	-10035
+LD	-10036
+▁Wedding	-10037
+gets	-10038
+PG	-10039
+▁pants	-10040
+iox	-10041
+fers	-10042
+▁governments	-10043
+▁decrease	-10044
+▁connecting	-10045
+▁Esc	-10046
+owed	-10047
+▁BR	-10048
+▁Flash	-10049
+▁Much	-10050
+inf	-10051
+▁mad	-10052
+Your	-10053
+▁Writing	-10054
+▁navigate	-10055
+obb	-10056
+ito	-10057
+bra	-10058
+▁Leon	-10059
+rett	-10060
+▁Defense	-10061
+▁lightweight	-10062
+▁Cov	-10063
+▁Gene	-10064
+▁utilize	-10065
+▁modified	-10066
+ifier	-10067
+▁Dutch	-10068
+udi	-10069
+▁Rol	-10070
+▁EC	-10071
+gging	-10072
+▁tort	-10073
+▁horses	-10074
+ationally	-10075
+▁dried	-10076
+▁jacket	-10077
+▁defect	-10078
+▁Simple	-10079
+▁Ident	-10080
+▁Hal	-10081
+avirus	-10082
+,’	-10083
+utical	-10084
+bsp	-10085
+▁operators	-10086
+▁Gh	-10087
+▁pup	-10088
+pire	-10089
+▁admission	-10090
+▁Howard	-10091
+▁bust	-10092
+▁overview	-10093
+▁Ton	-10094
+vity	-10095
+▁episodes	-10096
+▁operates	-10097
+tech	-10098
+SU	-10099
+▁inspiring	-10100
+▁Cream	-10101
+mat	-10102
+▁difficulty	-10103
+▁Stra	-10104
+▁myster	-10105
+▁gonna	-10106
+▁Thanksgiving	-10107
+beat	-10108
+AG	-10109
+▁slide	-10110
+▁roughly	-10111
+▁fastest	-10112
+▁SW	-10113
+idth	-10114
+▁refuge	-10115
+▁Death	-10116
+▁worker	-10117
+▁lawsuit	-10118
+about	-10119
+case	-10120
+anches	-10121
+▁Marine	-10122
+▁drops	-10123
+▁anytime	-10124
+▁Dyn	-10125
+▁tomatoes	-10126
+▁Stop	-10127
+▁Craig	-10128
+▁conclusion	-10129
+ded	-10130
+▁denied	-10131
+▁Pant	-10132
+▁wisdom	-10133
+MI	-10134
+▁liability	-10135
+ront	-10136
+stock	-10137
+▁Furthermore	-10138
+▁Cos	-10139
+▁magnet	-10140
+▁charging	-10141
+▁myth	-10142
+emade	-10143
+▁applies	-10144
+▁ratings	-10145
+▁Registration	-10146
+▁assists	-10147
+▁understood	-10148
+▁Fat	-10149
+▁essentially	-10150
+selling	-10151
+▁sophisticated	-10152
+Mar	-10153
+▁Unlike	-10154
+▁removing	-10155
+ICK	-10156
+▁Race	-10157
+iro	-10158
+▁Dean	-10159
+▁rend	-10160
+▁WE	-10161
+▁gam	-10162
+▁imper	-10163
+scale	-10164
+▁algorith	-10165
+▁EP	-10166
+▁Bag	-10167
+▁Player	-10168
+▁threats	-10169
+▁fake	-10170
+▁Magic	-10171
+▁Wa	-10172
+card	-10173
+▁cheaper	-10174
+More	-10175
+▁antib	-10176
+▁cooling	-10177
+acles	-10178
+▁Anne	-10179
+▁Moreover	-10180
+▁Alliance	-10181
+▁Nik	-10182
+▁conversion	-10183
+▁specialized	-10184
+▁versatile	-10185
+▁offerings	-10186
+arest	-10187
+burn	-10188
+▁gardens	-10189
+▁fulfill	-10190
+ITH	-10191
+▁Discover	-10192
+▁waves	-10193
+▁oldest	-10194
+▁conservation	-10195
+▁circuit	-10196
+Please	-10197
+▁fantasy	-10198
+▁adoption	-10199
+▁surfaces	-10200
+▁Dra	-10201
+▁Gil	-10202
+▁unve	-10203
+lend	-10204
+▁memorable	-10205
+table	-10206
+ishop	-10207
+▁stem	-10208
+▁boots	-10209
+▁poster	-10210
+▁supplier	-10211
+▁presidential	-10212
+pur	-10213
+▁piano	-10214
+▁enjoys	-10215
+▁annoy	-10216
+osystem	-10217
+▁paintings	-10218
+▁Pink	-10219
+poons	-10220
+rous	-10221
+asting	-10222
+▁documentary	-10223
+uros	-10224
+▁associate	-10225
+aki	-10226
+ping	-10227
+▁pleasant	-10228
+▁calories	-10229
+▁cha	-10230
+▁permit	-10231
+istol	-10232
+▁renown	-10233
+EX	-10234
+▁soldiers	-10235
+▁sorts	-10236
+Just	-10237
+▁trucks	-10238
+▁Success	-10239
+▁behaviour	-10240
+▁theatre	-10241
+▁Indones	-10242
+open	-10243
+ishers	-10244
+▁Reserved	-10245
+▁Jer	-10246
+▁Feel	-10247
+▁titled	-10248
+▁efficiently	-10249
+▁dozen	-10250
+▁Andre	-10251
+itations	-10252
+ANT	-10253
+▁Pier	-10254
+▁grey	-10255
+▁Fish	-10256
+▁Block	-10257
+iy	-10258
+▁Within	-10259
+▁universe	-10260
+▁Gree	-10261
+En	-10262
+▁Speaking	-10263
+▁Images	-10264
+▁legit	-10265
+▁clinic	-10266
+usal	-10267
+▁washing	-10268
+jection	-10269
+▁eager	-10270
+▁Contract	-10271
+▁Nations	-10272
+▁investigate	-10273
+rend	-10274
+▁opposed	-10275
+▁army	-10276
+▁GP	-10277
+veland	-10278
+after	-10279
+▁tune	-10280
+▁references	-10281
+▁VA	-10282
+▁agenda	-10283
+ilation	-10284
+▁Record	-10285
+▁Insp	-10286
+▁examine	-10287
+▁Ow	-10288
+▁tries	-10289
+▁Mexican	-10290
+▁conj	-10291
+▁practition	-10292
+▁elsewhere	-10293
+post	-10294
+▁que	-10295
+▁knife	-10296
+iva	-10297
+enz	-10298
+▁McK	-10299
+▁drove	-10300
+▁mining	-10301
+▁frequency	-10302
+▁wishes	-10303
+▁gang	-10304
+▁competitors	-10305
+▁angle	-10306
+▁mechanical	-10307
+▁Movie	-10308
+▁Cape	-10309
+▁declared	-10310
+▁cig	-10311
+▁endless	-10312
+OUN	-10313
+▁explo	-10314
+▁funded	-10315
+urches	-10316
+disc	-10317
+phan	-10318
+▁terrible	-10319
+▁Amb	-10320
+▁occasions	-10321
+▁Wik	-10322
+▁Medic	-10323
+▁Analysis	-10324
+▁prest	-10325
+▁barrel	-10326
+▁lbs	-10327
+▁Vent	-10328
+TML	-10329
+isure	-10330
+▁sensor	-10331
+▁hired	-10332
+▁BL	-10333
+▁relaxed	-10334
+▁nervous	-10335
+VP	-10336
+▁Nurs	-10337
+olph	-10338
+▁prizes	-10339
+▁Trib	-10340
+▁defeat	-10341
+▁assured	-10342
+Why	-10343
+▁Audio	-10344
+▁Whit	-10345
+▁Restaurant	-10346
+▁Third	-10347
+▁Nash	-10348
+▁DIY	-10349
+▁fed	-10350
+plates	-10351
+▁departments	-10352
+▁examination	-10353
+▁promised	-10354
+▁profiles	-10355
+ourse	-10356
+▁overwhelming	-10357
+uum	-10358
+▁Apply	-10359
+▁Resort	-10360
+▁Chamber	-10361
+▁promises	-10362
+aved	-10363
+▁kiss	-10364
+def	-10365
+ressed	-10366
+▁API	-10367
+▁Occ	-10368
+▁dash	-10369
+▁candy	-10370
+▁affiliate	-10371
+▁bite	-10372
+▁cruise	-10373
+▁Industrial	-10374
+▁dipl	-10375
+▁Write	-10376
+▁Fant	-10377
+▁mouse	-10378
+▁disappointed	-10379
+▁transformation	-10380
+▁poverty	-10381
+▁nationwide	-10382
+▁fluid	-10383
+NY	-10384
+imore	-10385
+▁gently	-10386
+▁glasses	-10387
+backs	-10388
+▁nuts	-10389
+▁retain	-10390
+ultane	-10391
+▁timely	-10392
+▁placing	-10393
+▁colon	-10394
+only	-10395
+▁Fine	-10396
+▁keyboard	-10397
+▁unem	-10398
+▁emphasis	-10399
+▁contacts	-10400
+▁Pitts	-10401
+▁GB	-10402
+▁Heritage	-10403
+▁neighbors	-10404
+plant	-10405
+▁colorful	-10406
+hr	-10407
+rec	-10408
+▁configuration	-10409
+▁Row	-10410
+▁Krist	-10411
+Or	-10412
+▁footage	-10413
+▁Anyway	-10414
+▁Joy	-10415
+▁administrative	-10416
+erator	-10417
+▁Hop	-10418
+Here	-10419
+▁beloved	-10420
+▁Kings	-10421
+▁Rome	-10422
+▁aggressive	-10423
+▁alleg	-10424
+commend	-10425
+bage	-10426
+▁Pharm	-10427
+ummy	-10428
+secutive	-10429
+istent	-10430
+▁involvement	-10431
+afe	-10432
+▁Import	-10433
+uther	-10434
+▁font	-10435
+▁cookie	-10436
+▁fleet	-10437
+▁fiscal	-10438
+▁audiences	-10439
+▁expecting	-10440
+fly	-10441
+▁advised	-10442
+▁acknowled	-10443
+▁True	-10444
+▁CV	-10445
+▁Lux	-10446
+▁Belg	-10447
+▁Dry	-10448
+km	-10449
+▁priced	-10450
+▁treats	-10451
+▁referr	-10452
+▁attorneys	-10453
+aceutical	-10454
+immer	-10455
+▁Buck	-10456
+▁Bull	-10457
+▁Say	-10458
+▁Fle	-10459
+▁possession	-10460
+aa	-10461
+▁Until	-10462
+gie	-10463
+▁browse	-10464
+▁marijuana	-10465
+▁representation	-10466
+zens	-10467
+▁Tea	-10468
+▁physically	-10469
+▁cloth	-10470
+▁ships	-10471
+▁les	-10472
+▁vocal	-10473
+▁publications	-10474
+▁Intel	-10475
+▁immune	-10476
+▁Li	-10477
+ropri	-10478
+▁sustainability	-10479
+▁tire	-10480
+▁relating	-10481
+pled	-10482
+urrent	-10483
+▁disability	-10484
+Im	-10485
+▁situated	-10486
+▁Pain	-10487
+▁Osc	-10488
+▁Bright	-10489
+▁beaches	-10490
+▁toss	-10491
+▁stomach	-10492
+▁Xbox	-10493
+▁Bear	-10494
+▁profits	-10495
+▁soap	-10496
+mic	-10497
+▁quantity	-10498
+▁Foreign	-10499
+inates	-10500
+▁somebody	-10501
+▁unlikely	-10502
+▁scholarship	-10503
+▁Could	-10504
+▁pics	-10505
+elfare	-10506
+▁exit	-10507
+▁spare	-10508
+▁Agricult	-10509
+▁Blood	-10510
+▁Incre	-10511
+▁combines	-10512
+▁lately	-10513
+▁unlock	-10514
+▁substance	-10515
+▁operator	-10516
+▁backed	-10517
+▁makers	-10518
+▁occasionally	-10519
+▁nations	-10520
+▁tourism	-10521
+▁Mall	-10522
+utors	-10523
+mother	-10524
+▁Portug	-10525
+TP	-10526
+▁advise	-10527
+▁Enterprise	-10528
+▁maker	-10529
+bus	-10530
+▁productive	-10531
+▁exhibit	-10532
+▁Dick	-10533
+even	-10534
+▁thrilled	-10535
+azed	-10536
+▁trails	-10537
+▁Bott	-10538
+▁gene	-10539
+▁Sorry	-10540
+acter	-10541
+▁Metro	-10542
+▁cosmet	-10543
+▁curious	-10544
+▁reveals	-10545
+▁Element	-10546
+▁constructed	-10547
+▁contractor	-10548
+▁solely	-10549
+▁Activ	-10550
+roduct	-10551
+▁wines	-10552
+▁weapon	-10553
+▁molec	-10554
+▁prominent	-10555
+▁steam	-10556
+sl	-10557
+▁Gord	-10558
+▁ordinary	-10559
+▁Castle	-10560
+reach	-10561
+▁shoe	-10562
+▁directory	-10563
+▁CM	-10564
+▁Screen	-10565
+▁Eye	-10566
+▁foam	-10567
+inking	-10568
+cled	-10569
+Star	-10570
+▁odds	-10571
+▁loop	-10572
+▁rounds	-10573
+pha	-10574
+▁PhD	-10575
+▁Falls	-10576
+▁honored	-10577
+▁Had	-10578
+▁bulk	-10579
+gments	-10580
+▁Palest	-10581
+▁sits	-10582
+▁Reserve	-10583
+opt	-10584
+UI	-10585
+▁Wat	-10586
+gra	-10587
+prises	-10588
+▁upset	-10589
+ilitation	-10590
+▁ME	-10591
+wer	-10592
+▁gran	-10593
+iden	-10594
+▁Consider	-10595
+▁grocery	-10596
+▁subtle	-10597
+ORT	-10598
+▁tatt	-10599
+olk	-10600
+vation	-10601
+▁Terms	-10602
+▁merely	-10603
+▁Cleveland	-10604
+▁Liter	-10605
+▁bearing	-10606
+▁IL	-10607
+mind	-10608
+▁ruling	-10609
+▁waist	-10610
+▁Jonathan	-10611
+▁loaded	-10612
+.-	-10613
+ishment	-10614
+▁uncle	-10615
+▁grants	-10616
+▁flooring	-10617
+▁Urban	-10618
+▁migr	-10619
+icked	-10620
+▁Lisa	-10621
+kee	-10622
+leans	-10623
+▁bout	-10624
+Now	-10625
+asket	-10626
+akh	-10627
+▁grat	-10628
+▁Located	-10629
+▁Related	-10630
+▁renowned	-10631
+▁facilitate	-10632
+▁digest	-10633
+Co	-10634
+▁oh	-10635
+omatic	-10636
+bally	-10637
+▁donate	-10638
+omic	-10639
+▁batteries	-10640
+▁allegedly	-10641
+oning	-10642
+▁broker	-10643
+▁bund	-10644
+▁immigration	-10645
+▁Pod	-10646
+▁spacious	-10647
+▁magical	-10648
+▁Deal	-10649
+▁versus	-10650
+▁distingu	-10651
+▁presentations	-10652
+▁Walker	-10653
+▁GR	-10654
+▁counts	-10655
+▁workforce	-10656
+▁Cole	-10657
+▁Alexander	-10658
+▁immigr	-10659
+▁Companies	-10660
+▁carries	-10661
+▁artificial	-10662
+▁movements	-10663
+▁Photography	-10664
+CL	-10665
+cal	-10666
+kind	-10667
+▁supplied	-10668
+▁drag	-10669
+ocracy	-10670
+▁Lot	-10671
+▁regulation	-10672
+enger	-10673
+▁slip	-10674
+▁overseas	-10675
+▁rac	-10676
+▁Antonio	-10677
+aughters	-10678
+▁lovers	-10679
+▁Coord	-10680
+▁Hyd	-10681
+▁shelter	-10682
+▁consent	-10683
+▁urg	-10684
+▁carrier	-10685
+▁width	-10686
+▁enorm	-10687
+▁consecutive	-10688
+▁sust	-10689
+▁sweat	-10690
+heat	-10691
+▁manufactured	-10692
+▁disorder	-10693
+▁Stars	-10694
+▁annually	-10695
+▁Premium	-10696
+▁Later	-10697
+▁Berlin	-10698
+▁stolen	-10699
+html	-10700
+▁paste	-10701
+▁jazz	-10702
+itched	-10703
+ospel	-10704
+▁refused	-10705
+▁Queens	-10706
+ounced	-10707
+▁Charlotte	-10708
+▁pipe	-10709
+▁Spot	-10710
+▁Purch	-10711
+▁Rat	-10712
+▁Der	-10713
+ften	-10714
+▁thro	-10715
+▁striking	-10716
+▁survival	-10717
+▁romance	-10718
+▁oblig	-10719
+▁Bruce	-10720
+who	-10721
+▁Roberts	-10722
+▁Crit	-10723
+▁careers	-10724
+▁Nether	-10725
+▁Mississ	-10726
+▁superb	-10727
+▁stopping	-10728
+▁settled	-10729
+▁Leadership	-10730
+aud	-10731
+▁Maine	-10732
+isp	-10733
+OVE	-10734
+emet	-10735
+▁vegetable	-10736
+flix	-10737
+perform	-10738
+Today	-10739
+▁Alan	-10740
+FP	-10741
+▁compassion	-10742
+inge	-10743
+▁flags	-10744
+▁reverse	-10745
+▁filing	-10746
+▁festiv	-10747
+▁bio	-10748
+▁downloaded	-10749
+▁Wayne	-10750
+▁passenger	-10751
+oops	-10752
+▁Nort	-10753
+issa	-10754
+isan	-10755
+▁begun	-10756
+ugg	-10757
+▁handful	-10758
+▁achievement	-10759
+▁rings	-10760
+ARD	-10761
+▁Got	-10762
+▁Amy	-10763
+▁royal	-10764
+▁Mun	-10765
+▁directors	-10766
+orious	-10767
+▁wrapped	-10768
+bes	-10769
+▁Roc	-10770
+▁IV	-10771
+▁Dubai	-10772
+▁dialogue	-10773
+▁Region	-10774
+▁donated	-10775
+▁genre	-10776
+▁disturb	-10777
+closure	-10778
+▁appliances	-10779
+▁Edward	-10780
+heart	-10781
+▁assemb	-10782
+▁hadn	-10783
+hou	-10784
+▁slots	-10785
+writer	-10786
+▁settle	-10787
+rena	-10788
+▁barely	-10789
+▁lease	-10790
+▁poetry	-10791
+fund	-10792
+ampa	-10793
+▁scream	-10794
+▁Bond	-10795
+▁fancy	-10796
+▁accomplished	-10797
+▁horm	-10798
+▁Homes	-10799
+▁inex	-10800
+Tw	-10801
+▁pasta	-10802
+▁Jen	-10803
+▁Hub	-10804
+▁cub	-10805
+▁veterans	-10806
+▁BY	-10807
+▁secured	-10808
+▁Target	-10809
+xygen	-10810
+isted	-10811
+▁towns	-10812
+wick	-10813
+ateur	-10814
+▁allerg	-10815
+▁Nap	-10816
+▁teens	-10817
+▁fragr	-10818
+dam	-10819
+▁Battle	-10820
+▁pione	-10821
+▁participated	-10822
+▁attendance	-10823
+▁Pil	-10824
+▁incorporate	-10825
+▁Suite	-10826
+▁Too	-10827
+▁preced	-10828
+▁suits	-10829
+asses	-10830
+▁rival	-10831
+▁iT	-10832
+woman	-10833
+▁hybrid	-10834
+SL	-10835
+▁Animal	-10836
+▁Som	-10837
+▁Oxford	-10838
+▁achieving	-10839
+▁composition	-10840
+forward	-10841
+▁Orleans	-10842
+▁Beauty	-10843
+▁Graph	-10844
+▁deleg	-10845
+▁Focus	-10846
+▁demonstrated	-10847
+▁governor	-10848
+▁Susan	-10849
+▁sq	-10850
+▁wherever	-10851
+▁impacts	-10852
+▁intellectual	-10853
+stick	-10854
+innamon	-10855
+omin	-10856
+▁tailored	-10857
+shine	-10858
+▁affects	-10859
+▁aver	-10860
+ifferent	-10861
+▁Anna	-10862
+▁convey	-10863
+▁receip	-10864
+IO	-10865
+▁Hamilton	-10866
+▁Metal	-10867
+▁Compl	-10868
+▁bicy	-10869
+theless	-10870
+bird	-10871
+▁collective	-10872
+hill	-10873
+▁tablespoons	-10874
+irates	-10875
+▁poker	-10876
+cure	-10877
+itative	-10878
+▁Touch	-10879
+▁innings	-10880
+▁ax	-10881
+▁THIS	-10882
+▁Boo	-10883
+otta	-10884
+▁Fig	-10885
+eled	-10886
+▁inters	-10887
+▁shine	-10888
+▁preserve	-10889
+▁labels	-10890
+▁confront	-10891
+▁Emergency	-10892
+▁routes	-10893
+▁contacted	-10894
+▁laundry	-10895
+▁portable	-10896
+▁cord	-10897
+quet	-10898
+tage	-10899
+▁stays	-10900
+▁hood	-10901
+▁Whatever	-10902
+▁dealer	-10903
+comed	-10904
+Pal	-10905
+▁specialty	-10906
+▁complaints	-10907
+▁Corporate	-10908
+apse	-10909
+ader	-10910
+▁shaped	-10911
+▁NYC	-10912
+IB	-10913
+essed	-10914
+Am	-10915
+▁bot	-10916
+▁Charlie	-10917
+guard	-10918
+.;	-10919
+▁accred	-10920
+▁Gov	-10921
+▁accompanied	-10922
+holder	-10923
+▁trials	-10924
+▁Qual	-10925
+▁Alaska	-10926
+▁glow	-10927
+▁optional	-10928
+▁accidents	-10929
+atching	-10930
+▁patio	-10931
+linary	-10932
+▁Apart	-10933
+▁measured	-10934
+best	-10935
+olitan	-10936
+Gu	-10937
+Int	-10938
+▁sisters	-10939
+dem	-10940
+FS	-10941
+fight	-10942
+ippi	-10943
+lando	-10944
+HD	-10945
+▁enabling	-10946
+OB	-10947
+▁simultane	-10948
+adesh	-10949
+▁mathem	-10950
+▁pockets	-10951
+▁Lad	-10952
+▁loads	-10953
+▁Yellow	-10954
+▁formation	-10955
+▁disabled	-10956
+▁ears	-10957
+▁tags	-10958
+▁Cou	-10959
+▁Maria	-10960
+owered	-10961
+▁Bitcoin	-10962
+▁anch	-10963
+▁VP	-10964
+▁Russell	-10965
+▁dressing	-10966
+KS	-10967
+reedom	-10968
+▁Hind	-10969
+acco	-10970
+▁encount	-10971
+gins	-10972
+▁spectrum	-10973
+▁dirty	-10974
+▁capability	-10975
+▁testim	-10976
+ussy	-10977
+▁homeless	-10978
+kers	-10979
+▁Solar	-10980
+ailed	-10981
+▁**	-10982
+▁Gate	-10983
+▁reduces	-10984
+▁purple	-10985
+▁mit	-10986
+▁edited	-10987
+▁intrig	-10988
+▁Skin	-10989
+▁Vietnam	-10990
+▁fitting	-10991
+Donald	-10992
+▁rolled	-10993
+▁realistic	-10994
+▁specifications	-10995
+ushes	-10996
+ACT	-10997
+▁proport	-10998
+abis	-10999
+▁Storage	-11000
+inance	-11001
+▁translation	-11002
+▁snack	-11003
+▁tear	-11004
+esty	-11005
+▁dedication	-11006
+▁consultant	-11007
+▁scrap	-11008
+▁wellness	-11009
+▁NJ	-11010
+▁Luck	-11011
+▁Ky	-11012
+▁survived	-11013
+UB	-11014
+RP	-11015
+▁adventures	-11016
+▁Fix	-11017
+▁AG	-11018
+Ste	-11019
+▁Prize	-11020
+▁mush	-11021
+▁epic	-11022
+adows	-11023
+▁tale	-11024
+▁Commerce	-11025
+▁Aw	-11026
+▁enabled	-11027
+▁Hawaii	-11028
+▁territory	-11029
+▁fitted	-11030
+▁pant	-11031
+emetery	-11032
+▁UV	-11033
+▁optimal	-11034
+▁Pra	-11035
+plus	-11036
+▁plates	-11037
+▁tempt	-11038
+edded	-11039
+iffs	-11040
+▁Ho	-11041
+▁Gulf	-11042
+▁Athlet	-11043
+▁Beck	-11044
+▁Doll	-11045
+anship	-11046
+▁essays	-11047
+ML	-11048
+fortable	-11049
+▁spa	-11050
+Work	-11051
+▁disorders	-11052
+▁conditioning	-11053
+▁jur	-11054
+▁blame	-11055
+stop	-11056
+▁Analy	-11057
+▁Nigeria	-11058
+▁accum	-11059
+▁specified	-11060
+eld	-11061
+▁fallen	-11062
+▁attempted	-11063
+icut	-11064
+▁contractors	-11065
+▁frames	-11066
+▁rally	-11067
+▁designated	-11068
+▁Grace	-11069
+▁Israeli	-11070
+▁thrown	-11071
+azines	-11072
+▁CBS	-11073
+▁ecosystem	-11074
+▁Stewart	-11075
+▁Furn	-11076
+▁Dental	-11077
+angel	-11078
+▁Wonder	-11079
+▁MC	-11080
+▁provision	-11081
+▁Baltimore	-11082
+▁Liverpool	-11083
+▁imagination	-11084
+▁%	-11085
+▁scenario	-11086
+clusive	-11087
+▁horror	-11088
+▁sized	-11089
+aco	-11090
+▁reflects	-11091
+▁Scholars	-11092
+▁dresses	-11093
+▁crimes	-11094
+▁neutral	-11095
+annah	-11096
+first	-11097
+▁Luke	-11098
+▁devast	-11099
+▁Nev	-11100
+spec	-11101
+▁Cars	-11102
+▁Friend	-11103
+▁Posts	-11104
+▁Organization	-11105
+▁Value	-11106
+▁indicates	-11107
+ceive	-11108
+▁tasty	-11109
+▁HTML	-11110
+▁installing	-11111
+▁Si	-11112
+▁Chrome	-11113
+ao	-11114
+▁coupon	-11115
+▁Palm	-11116
+▁Sad	-11117
+robe	-11118
+▁tourist	-11119
+▁attractions	-11120
+odium	-11121
+▁manip	-11122
+▁formats	-11123
+▁pine	-11124
+rapeut	-11125
+▁premier	-11126
+Well	-11127
+asures	-11128
+▁Ec	-11129
+inciple	-11130
+▁courage	-11131
+▁Begin	-11132
+▁enemy	-11133
+▁speaks	-11134
+▁booth	-11135
+kg	-11136
+▁(“	-11137
+lan	-11138
+▁distract	-11139
+▁Culture	-11140
+lay	-11141
+▁dose	-11142
+▁satellite	-11143
+▁lineup	-11144
+▁Given	-11145
+▁Latest	-11146
+▁spoken	-11147
+▁cheer	-11148
+▁cous	-11149
+▁Ground	-11150
+bian	-11151
+▁rhyth	-11152
+▁Fro	-11153
+▁Wolf	-11154
+▁entrepreneur	-11155
+▁Jobs	-11156
+ocated	-11157
+▁landing	-11158
+OUR	-11159
+▁Individual	-11160
+▁FC	-11161
+▁dressed	-11162
+ruptcy	-11163
+▁Netherlands	-11164
+gate	-11165
+▁wing	-11166
+▁tremendous	-11167
+▁collecting	-11168
+▁Imag	-11169
+▁GA	-11170
+▁colored	-11171
+▁Dak	-11172
+▁Transportation	-11173
+ailand	-11174
+shot	-11175
+nered	-11176
+▁Thompson	-11177
+▁nicely	-11178
+▁Virtual	-11179
+▁tears	-11180
+▁wax	-11181
+▁venues	-11182
+PT	-11183
+▁Laura	-11184
+ctic	-11185
+▁Rd	-11186
+rac	-11187
+▁tong	-11188
+▁Lem	-11189
+▁hardly	-11190
+isers	-11191
+▁rush	-11192
+▁diagnosed	-11193
+▁dump	-11194
+▁printer	-11195
+▁warned	-11196
+▁Dise	-11197
+▁Cold	-11198
+▁tuned	-11199
+ipper	-11200
+wind	-11201
+▁Users	-11202
+▁province	-11203
+▁electronics	-11204
+▁Christopher	-11205
+▁girlfriend	-11206
+▁Hours	-11207
+▁merchand	-11208
+▁Scottish	-11209
+▁marine	-11210
+▁destinations	-11211
+▁expense	-11212
+▁selecting	-11213
+▁seating	-11214
+▁relation	-11215
+▁ye	-11216
+▁throwing	-11217
+▁apartments	-11218
+▁ties	-11219
+▁punch	-11220
+▁Valentine	-11221
+▁embrace	-11222
+▁legendary	-11223
+▁Butter	-11224
+andal	-11225
+die	-11226
+▁confirmation	-11227
+uman	-11228
+CAA	-11229
+▁Chelsea	-11230
+enny	-11231
+▁crop	-11232
+▁deput	-11233
+▁irrit	-11234
+▁stakeholders	-11235
+▁Rot	-11236
+▁Ukraine	-11237
+▁Recently	-11238
+▁destroyed	-11239
+▁Benefits	-11240
+▁Chicken	-11241
+▁Regard	-11242
+▁Chocolate	-11243
+▁Carib	-11244
+PE	-11245
+▁Upon	-11246
+▁explanation	-11247
+▁nonprofit	-11248
+asm	-11249
+▁tension	-11250
+▁damages	-11251
+cca	-11252
+▁seeks	-11253
+▁Vin	-11254
+Che	-11255
+there	-11256
+▁seasonal	-11257
+▁Inside	-11258
+▁Trip	-11259
+▁declined	-11260
+▁breathing	-11261
+▁AW	-11262
+▁calc	-11263
+▁mg	-11264
+health	-11265
+▁Cann	-11266
+▁intimate	-11267
+▁aqu	-11268
+▁Summit	-11269
+▁Adobe	-11270
+▁GT	-11271
+ounces	-11272
+URE	-11273
+▁Lib	-11274
+▁resistant	-11275
+▁foster	-11276
+▁nest	-11277
+▁Mars	-11278
+▁locked	-11279
+▁handed	-11280
+▁lobby	-11281
+▁stupid	-11282
+▁peaceful	-11283
+▁vertical	-11284
+conf	-11285
+ribution	-11286
+▁welcomed	-11287
+▁steady	-11288
+▁grain	-11289
+▁cyber	-11290
+▁vitamin	-11291
+owship	-11292
+▁figured	-11293
+gun	-11294
+▁unlimited	-11295
+▁hill	-11296
+▁healthier	-11297
+ni	-11298
+▁Han	-11299
+▁gel	-11300
+▁engineer	-11301
+VC	-11302
+auc	-11303
+▁handled	-11304
+▁insect	-11305
+Oh	-11306
+▁nobody	-11307
+▁Sean	-11308
+lar	-11309
+▁internationally	-11310
+▁cents	-11311
+▁verify	-11312
+▁gains	-11313
+ixel	-11314
+▁exploration	-11315
+▁appet	-11316
+▁Track	-11317
+father	-11318
+▁PO	-11319
+▁Nich	-11320
+▁anybody	-11321
+▁timing	-11322
+▁pork	-11323
+▁publicly	-11324
+▁feeding	-11325
+Date	-11326
+▁amid	-11327
+▁GPS	-11328
+vol	-11329
+izations	-11330
+BN	-11331
+▁Extra	-11332
+tends	-11333
+speed	-11334
+▁colleges	-11335
+▁travelling	-11336
+▁tires	-11337
+▁gross	-11338
+News	-11339
+▁Housing	-11340
+bet	-11341
+▁Active	-11342
+▁iTunes	-11343
+▁ordering	-11344
+▁Netflix	-11345
+▁Strateg	-11346
+ricane	-11347
+▁differently	-11348
+▁publisher	-11349
+▁competing	-11350
+▁touchdown	-11351
+▁branches	-11352
+▁voices	-11353
+▁spo	-11354
+▁Ready	-11355
+▁reads	-11356
+▁blessed	-11357
+Point	-11358
+uisine	-11359
+writing	-11360
+▁Lawrence	-11361
+▁tent	-11362
+▁locate	-11363
+▁collaborative	-11364
+ELL	-11365
+▁Emb	-11366
+commerce	-11367
+▁placement	-11368
+aro	-11369
+cat	-11370
+erals	-11371
+▁bits	-11372
+▁immers	-11373
+▁earning	-11374
+▁Om	-11375
+▁ought	-11376
+▁semester	-11377
+▁petition	-11378
+▁mixing	-11379
+▁assignment	-11380
+▁implementing	-11381
+ascular	-11382
+party	-11383
+▁Bloom	-11384
+▁dont	-11385
+▁cultiv	-11386
+▁pulling	-11387
+▁phrase	-11388
+▁Associate	-11389
+▁Official	-11390
+allel	-11391
+▁Investment	-11392
+▁ISO	-11393
+▁Coun	-11394
+▁medications	-11395
+▁corpor	-11396
+▁leak	-11397
+▁Technologies	-11398
+▁dozens	-11399
+▁Wol	-11400
+▁measurements	-11401
+▁onions	-11402
+isode	-11403
+▁humor	-11404
+▁effectiveness	-11405
+▁facial	-11406
+▁grid	-11407
+▁automated	-11408
+▁guns	-11409
+▁shades	-11410
+▁Pittsburgh	-11411
+▁proposals	-11412
+shore	-11413
+▁discussing	-11414
+▁cultures	-11415
+▁adopt	-11416
+▁lyrics	-11417
+▁cum	-11418
+▁duration	-11419
+▁mailing	-11420
+▁minim	-11421
+▁trigger	-11422
+▁toxic	-11423
+UE	-11424
+▁treating	-11425
+ividually	-11426
+▁Lif	-11427
+▁Ott	-11428
+empl	-11429
+▁rack	-11430
+▁pond	-11431
+▁nurse	-11432
+▁bay	-11433
+▁difficulties	-11434
+▁burg	-11435
+▁diameter	-11436
+▁Strong	-11437
+lishing	-11438
+▁Chi	-11439
+necess	-11440
+▁painful	-11441
+▁receives	-11442
+gic	-11443
+▁reminder	-11444
+▁synd	-11445
+▁GO	-11446
+▁submission	-11447
+▁WITH	-11448
+▁countless	-11449
+▁armed	-11450
+acent	-11451
+▁Yoga	-11452
+▁addiction	-11453
+▁Assess	-11454
+▁Ring	-11455
+▁applicants	-11456
+▁RS	-11457
+▁Includes	-11458
+quer	-11459
+▁Starting	-11460
+▁Ga	-11461
+▁mechanism	-11462
+▁vinyl	-11463
+atherine	-11464
+MB	-11465
+▁WA	-11466
+▁Ali	-11467
+▁fool	-11468
+chestra	-11469
+▁Ka	-11470
+▁pron	-11471
+▁Hunter	-11472
+▁Barbara	-11473
+▁tablets	-11474
+▁overs	-11475
+▁launching	-11476
+unct	-11477
+▁Sout	-11478
+▁femin	-11479
+▁fence	-11480
+▁aging	-11481
+umble	-11482
+▁suspended	-11483
+▁Parliament	-11484
+▁Repair	-11485
+▁bun	-11486
+▁Captain	-11487
+cend	-11488
+▁Category	-11489
+▁subsequent	-11490
+▁hiking	-11491
+▁Designed	-11492
+▁desert	-11493
+▁rewards	-11494
+▁Nelson	-11495
+▁zip	-11496
+▁Bor	-11497
+▁CBD	-11498
+▁uniform	-11499
+▁Adult	-11500
+abs	-11501
+▁ESP	-11502
+▁fu	-11503
+▁Intellig	-11504
+▁Essential	-11505
+▁suspension	-11506
+▁HE	-11507
+Some	-11508
+iches	-11509
+▁hack	-11510
+▁yarn	-11511
+▁Marg	-11512
+▁Pas	-11513
+▁absence	-11514
+▁Gib	-11515
+Ph	-11516
+▁Afghanistan	-11517
+▁narrative	-11518
+▁judges	-11519
+▁concluded	-11520
+▁Chen	-11521
+▁angry	-11522
+▁checkout	-11523
+ictions	-11524
+vet	-11525
+▁innoc	-11526
+▁restoration	-11527
+▁jam	-11528
+▁automotive	-11529
+▁Innovation	-11530
+▁robot	-11531
+▁Earlier	-11532
+▁NBC	-11533
+▁delicate	-11534
+▁stroke	-11535
+▁boyfriend	-11536
+▁motivated	-11537
+▁Funeral	-11538
+sk	-11539
+DAY	-11540
+▁ridic	-11541
+▁Partner	-11542
+▁prescription	-11543
+▁Ridge	-11544
+ju	-11545
+▁reliability	-11546
+▁Drug	-11547
+▁Orlando	-11548
+iper	-11549
+▁Connecticut	-11550
+▁Morning	-11551
+▁Former	-11552
+▁Kay	-11553
+breaking	-11554
+DR	-11555
+▁Something	-11556
+itivity	-11557
+▁Malaysia	-11558
+▁Math	-11559
+amily	-11560
+▁Half	-11561
+▁conservative	-11562
+▁Bachelor	-11563
+▁seniors	-11564
+▁ML	-11565
+▁brace	-11566
+▁Na	-11567
+▁marketplace	-11568
+▁Mississippi	-11569
+▁Adams	-11570
+▁Survey	-11571
+▁Protect	-11572
+▁tang	-11573
+▁Feature	-11574
+▁anticipated	-11575
+▁forgotten	-11576
+▁Used	-11577
+▁motivation	-11578
+▁homeowners	-11579
+▁Bou	-11580
+poon	-11581
+▁albums	-11582
+good	-11583
+▁climbing	-11584
+chain	-11585
+har	-11586
+▁Wash	-11587
+▁welcoming	-11588
+▁Length	-11589
+▁buck	-11590
+▁prospects	-11591
+izable	-11592
+▁prestigious	-11593
+▁Independent	-11594
+▁luxurious	-11595
+▁winds	-11596
+▁outer	-11597
+cribed	-11598
+▁troops	-11599
+▁Info	-11600
+▁Tow	-11601
+▁buzz	-11602
+▁ende	-11603
+▁Taking	-11604
+▁cush	-11605
+▁owns	-11606
+▁Domin	-11607
+▁picks	-11608
+count	-11609
+▁Publ	-11610
+▁Harvard	-11611
+▁Artist	-11612
+▁inject	-11613
+▁Organic	-11614
+▁Advert	-11615
+▁Convention	-11616
+▁preferences	-11617
+▁scal	-11618
+IGHT	-11619
+▁theft	-11620
+▁Sweden	-11621
+▁manif	-11622
+▁accordance	-11623
+▁Draw	-11624
+▁Michelle	-11625
+around	-11626
+▁funeral	-11627
+▁Vac	-11628
+▁Multi	-11629
+▁concentration	-11630
+▁Islands	-11631
+▁churches	-11632
+eneath	-11633
+▁Ay	-11634
+▁neuro	-11635
+oard	-11636
+▁DR	-11637
+▁celebrity	-11638
+▁Gordon	-11639
+▁Thailand	-11640
+Sub	-11641
+lington	-11642
+olesale	-11643
+▁analytics	-11644
+▁guided	-11645
+thon	-11646
+uru	-11647
+▁measurement	-11648
+▁shelf	-11649
+▁Islam	-11650
+▁Kal	-11651
+arrass	-11652
+▁Effect	-11653
+▁Recommend	-11654
+called	-11655
+▁abstract	-11656
+etooth	-11657
+▁violent	-11658
+▁Hey	-11659
+▁Aaron	-11660
+▁Grey	-11661
+▁demanding	-11662
+▁Sheriff	-11663
+▁identification	-11664
+▁brick	-11665
+▁cancel	-11666
+Yes	-11667
+▁Treatment	-11668
+▁console	-11669
+▁confidential	-11670
+ogs	-11671
+▁Volunte	-11672
+▁captain	-11673
+▁lips	-11674
+▁Question	-11675
+▁Temple	-11676
+▁Hello	-11677
+▁Notes	-11678
+▁casinos	-11679
+▁Voice	-11680
+▁genetic	-11681
+depth	-11682
+▁laboratory	-11683
+▁harmful	-11684
+▁Therapy	-11685
+▁retreat	-11686
+▁Louisiana	-11687
+▁Madison	-11688
+icity	-11689
+▁regards	-11690
+▁sponsor	-11691
+OME	-11692
+uy	-11693
+IE	-11694
+▁intelligent	-11695
+ictionary	-11696
+iar	-11697
+▁apolog	-11698
+▁friendship	-11699
+▁appreciation	-11700
+▁vel	-11701
+▁jeans	-11702
+▁enormous	-11703
+▁Sens	-11704
+eddings	-11705
+▁RA	-11706
+▁Manh	-11707
+school	-11708
+▁Rab	-11709
+▁folk	-11710
+▁Rent	-11711
+▁closure	-11712
+▁Francis	-11713
+▁seventh	-11714
+▁kilomet	-11715
+▁boil	-11716
+▁protective	-11717
+▁Toyota	-11718
+▁Islamic	-11719
+▁canvas	-11720
+NC	-11721
+▁threw	-11722
+atar	-11723
+paren	-11724
+▁crystal	-11725
+idity	-11726
+▁Fly	-11727
+GS	-11728
+▁Nice	-11729
+▁Door	-11730
+▁devoted	-11731
+▁forums	-11732
+▁fridge	-11733
+▁tourists	-11734
+▁valued	-11735
+thur	-11736
+olas	-11737
+kyo	-11738
+uity	-11739
+▁airline	-11740
+▁Hug	-11741
+code	-11742
+▁athletic	-11743
+▁separately	-11744
+▁alternatives	-11745
+▁Sleep	-11746
+▁Philippines	-11747
+▁grows	-11748
+▁Range	-11749
+▁convin	-11750
+asion	-11751
+▁requiring	-11752
+▁Cambridge	-11753
+author	-11754
+▁dimensions	-11755
+▁shorter	-11756
+▁ranking	-11757
+onde	-11758
+▁Boot	-11759
+Are	-11760
+ilarly	-11761
+▁Desk	-11762
+test	-11763
+▁Paint	-11764
+ching	-11765
+encil	-11766
+▁disabilities	-11767
+▁portal	-11768
+▁crafts	-11769
+▁execution	-11770
+▁Weather	-11771
+▁noon	-11772
+▁agricultural	-11773
+▁neat	-11774
+▁camping	-11775
+▁reminded	-11776
+▁Chef	-11777
+▁sons	-11778
+edu	-11779
+▁varied	-11780
+▁vegan	-11781
+▁pad	-11782
+▁fundraising	-11783
+▁Cass	-11784
+▁executives	-11785
+▁copper	-11786
+▁Khan	-11787
+▁Jessica	-11788
+▁Tag	-11789
+inery	-11790
+▁confused	-11791
+ento	-11792
+▁Kennedy	-11793
+▁Item	-11794
+▁Ian	-11795
+ceptions	-11796
+onge	-11797
+▁Options	-11798
+▁detection	-11799
+▁accompany	-11800
+▁Rachel	-11801
+▁toll	-11802
+▁adjustable	-11803
+▁refreshing	-11804
+▁navigation	-11805
+rowave	-11806
+▁inflamm	-11807
+▁wrest	-11808
+▁cer	-11809
+▁Perry	-11810
+Net	-11811
+▁recommendation	-11812
+zo	-11813
+▁filters	-11814
+cha	-11815
+▁attendees	-11816
+▁troubles	-11817
+▁custody	-11818
+▁Spa	-11819
+▁ethnic	-11820
+▁heal	-11821
+▁partnerships	-11822
+")	-11823
+icop	-11824
+▁Crim	-11825
+▁homemade	-11826
+▁Christians	-11827
+oria	-11828
+▁attempting	-11829
+▁jury	-11830
+▁qualities	-11831
+itarian	-11832
+▁Fu	-11833
+▁Bapt	-11834
+▁stere	-11835
+▁boundaries	-11836
+oven	-11837
+▁transparent	-11838
+▁tomato	-11839
+▁Clar	-11840
+▁breed	-11841
+▁Fan	-11842
+whe	-11843
+ancers	-11844
+▁replacing	-11845
+▁Caribbean	-11846
+▁Dad	-11847
+▁demo	-11848
+ylon	-11849
+▁blogging	-11850
+ante	-11851
+▁handles	-11852
+▁organize	-11853
+Tra	-11854
+▁Yan	-11855
+▁Path	-11856
+apters	-11857
+▁Tools	-11858
+▁instructor	-11859
+▁dessert	-11860
+▁Bah	-11861
+▁burst	-11862
+▁Production	-11863
+▁unfortunately	-11864
+▁Medicare	-11865
+▁Century	-11866
+▁identifying	-11867
+▁crafted	-11868
+▁Retail	-11869
+▁lat	-11870
+▁Greece	-11871
+▁districts	-11872
+ipedia	-11873
+ethe	-11874
+▁Surg	-11875
+▁tower	-11876
+▁ster	-11877
+▁hesitate	-11878
+▁headquarters	-11879
+▁establishment	-11880
+blo	-11881
+▁speeds	-11882
+▁Sett	-11883
+▁Dragon	-11884
+!).	-11885
+▁Tower	-11886
+▁sacrif	-11887
+▁Wright	-11888
+cs	-11889
+▁boring	-11890
+▁Pool	-11891
+▁magazines	-11892
+Go	-11893
+▁Leader	-11894
+SD	-11895
+chair	-11896
+▁adequate	-11897
+track	-11898
+▁Sustain	-11899
+▁grip	-11900
+▁supportive	-11901
+▁Obs	-11902
+ostic	-11903
+▁arrangement	-11904
+▁judgment	-11905
+▁cogn	-11906
+▁divers	-11907
+▁propri	-11908
+▁Bio	-11909
+▁hint	-11910
+▁Vision	-11911
+▁Java	-11912
+▁Todd	-11913
+▁inev	-11914
+▁reporter	-11915
+▁lap	-11916
+arma	-11917
+pres	-11918
+ingle	-11919
+iev	-11920
+▁rides	-11921
+▁Kr	-11922
+▁Administr	-11923
+▁Visual	-11924
+▁Princip	-11925
+▁lined	-11926
+▁Between	-11927
+▁Really	-11928
+eals	-11929
+▁Fill	-11930
+▁weakness	-11931
+▁indu	-11932
+▁fears	-11933
+▁medic	-11934
+▁utilizing	-11935
+▁saves	-11936
+▁Campus	-11937
+▁Edge	-11938
+▁rocks	-11939
+▁hem	-11940
+IES	-11941
+▁Hem	-11942
+▁Baker	-11943
+▁closet	-11944
+loyd	-11945
+▁Grade	-11946
+▁audit	-11947
+master	-11948
+▁Graham	-11949
+▁vaccine	-11950
+▁boats	-11951
+parency	-11952
+▁Ful	-11953
+ASH	-11954
+▁affairs	-11955
+▁mounted	-11956
+mitting	-11957
+▁NASA	-11958
+▁finishes	-11959
+▁Bart	-11960
+hard	-11961
+▁shadow	-11962
+▁precision	-11963
+elta	-11964
+▁arranged	-11965
+▁teaches	-11966
+▁Profile	-11967
+▁promising	-11968
+▁Dest	-11969
+elect	-11970
+▁gray	-11971
+▁tricks	-11972
+▁sporting	-11973
+▁LE	-11974
+▁Basic	-11975
+▁signals	-11976
+working	-11977
+▁highlighted	-11978
+▁lightly	-11979
+▁Anim	-11980
+▁Excellent	-11981
+▁Fal	-11982
+▁intervention	-11983
+▁lighter	-11984
+▁underlying	-11985
+▁sequence	-11986
+▁contributing	-11987
+▁dealers	-11988
+▁talents	-11989
+▁ruled	-11990
+▁Universal	-11991
+▁recruitment	-11992
+▁Listen	-11993
+▁composed	-11994
+▁specially	-11995
+▁caf	-11996
+celona	-11997
+▁claiming	-11998
+▁Larry	-11999
+▁daughters	-12000
+▁dup	-12001
+▁Born	-12002
+▁recipient	-12003
+▁logged	-12004
+▁tastes	-12005
+pling	-12006
+itzerland	-12007
+will	-12008
+▁RO	-12009
+▁outf	-12010
+▁Ward	-12011
+▁oxygen	-12012
+▁Yahoo	-12013
+▁peer	-12014
+▁Fem	-12015
+▁Argent	-12016
+▁snacks	-12017
+▁Swiss	-12018
+anth	-12019
+See	-12020
+▁integrate	-12021
+anut	-12022
+they	-12023
+▁..	-12024
+▁complimentary	-12025
+▁suicide	-12026
+▁structural	-12027
+▁eastern	-12028
+amins	-12029
+▁suited	-12030
+▁Prices	-12031
+▁advocate	-12032
+▁ignore	-12033
+▁quarterback	-12034
+oft	-12035
+rary	-12036
+▁bin	-12037
+▁weekends	-12038
+▁FDA	-12039
+▁dear	-12040
+▁ranks	-12041
+▁SK	-12042
+▁fut	-12043
+▁spam	-12044
+▁hockey	-12045
+▁pollution	-12046
+Lear	-12047
+▁Greater	-12048
+▁costume	-12049
+▁bikes	-12050
+▁Hend	-12051
+mediate	-12052
+▁Stories	-12053
+▁builds	-12054
+held	-12055
+▁wore	-12056
+▁squee	-12057
+▁sponsors	-12058
+▁SL	-12059
+▁Tokyo	-12060
+▁kits	-12061
+▁License	-12062
+▁individually	-12063
+▁Customers	-12064
+▁clim	-12065
+bled	-12066
+▁Duke	-12067
+▁Turk	-12068
+▁cure	-12069
+▁tile	-12070
+▁Sugar	-12071
+▁Improve	-12072
+▁Avoid	-12073
+▁adorable	-12074
+▁refers	-12075
+▁Anti	-12076
+▁Woman	-12077
+▁Stream	-12078
+▁discipline	-12079
+▁interactions	-12080
+▁twin	-12081
+▁surviv	-12082
+▁reporters	-12083
+▁appointments	-12084
+▁Carter	-12085
+▁Drop	-12086
+green	-12087
+▁beliefs	-12088
+▁spouse	-12089
+▁disag	-12090
+ibe	-12091
+▁embra	-12092
+▁Beth	-12093
+▁boasts	-12094
+▁Veh	-12095
+▁Lex	-12096
+▁presenting	-12097
+▁Ach	-12098
+▁expects	-12099
+▁tops	-12100
+▁mobility	-12101
+▁Tar	-12102
+▁Freedom	-12103
+▁fabrics	-12104
+▁Equipment	-12105
+▁rect	-12106
+▁infections	-12107
+▁Hamp	-12108
+▁thankful	-12109
+▁principle	-12110
+▁Jerry	-12111
+▁Ultra	-12112
+▁moral	-12113
+assador	-12114
+▁Parker	-12115
+irmingham	-12116
+ernand	-12117
+▁Pros	-12118
+▁compelling	-12119
+Time	-12120
+▁ox	-12121
+herent	-12122
+▁appearances	-12123
+▁agriculture	-12124
+glas	-12125
+While	-12126
+▁Robinson	-12127
+▁argue	-12128
+▁floral	-12129
+▁parameters	-12130
+▁counties	-12131
+▁moderate	-12132
+ieces	-12133
+onia	-12134
+▁stitch	-12135
+▁Mas	-12136
+▁trademark	-12137
+Let	-12138
+mel	-12139
+▁MI	-12140
+▁analyze	-12141
+▁agreements	-12142
+uz	-12143
+quest	-12144
+▁Career	-12145
+▁unnecess	-12146
+▁Instruct	-12147
+▁hungry	-12148
+▁booked	-12149
+▁Tig	-12150
+▁Ale	-12151
+▁libraries	-12152
+▁quit	-12153
+▁Condition	-12154
+PI	-12155
+▁Stir	-12156
+▁thy	-12157
+▁dentist	-12158
+▁failing	-12159
+▁Manhattan	-12160
+▁addressing	-12161
+▁Flex	-12162
+ottage	-12163
+▁Rice	-12164
+▁Syria	-12165
+▁Hud	-12166
+▁Originally	-12167
+▁margin	-12168
+IDS	-12169
+▁Sel	-12170
+▁Associated	-12171
+illance	-12172
+▁fer	-12173
+▁Campbell	-12174
+▁od	-12175
+effective	-12176
+▁charts	-12177
+urers	-12178
+▁horn	-12179
+arel	-12180
+▁Weekly	-12181
+▁Slo	-12182
+▁cleans	-12183
+▁promotional	-12184
+▁bones	-12185
+▁gut	-12186
+▁sunny	-12187
+▁Emily	-12188
+▁strain	-12189
+▁Others	-12190
+▁seemingly	-12191
+▁BMW	-12192
+▁Broadway	-12193
+▁Frame	-12194
+▁Native	-12195
+▁thesis	-12196
+▁wheat	-12197
+▁Jazz	-12198
+▁embarrass	-12199
+▁Alb	-12200
+▁Fer	-12201
+▁Nevada	-12202
+▁remod	-12203
+▁Tampa	-12204
+▁appealing	-12205
+acht	-12206
+▁delete	-12207
+▁spotted	-12208
+▁beating	-12209
+▁worthy	-12210
+▁satisfy	-12211
+abad	-12212
+▁Imm	-12213
+▁backyard	-12214
+▁jumped	-12215
+▁Usually	-12216
+▁Ger	-12217
+▁patent	-12218
+▁stadium	-12219
+▁Singh	-12220
+resist	-12221
+BR	-12222
+▁computing	-12223
+▁ginger	-12224
+▁Hopefully	-12225
+▁pays	-12226
+▁harsh	-12227
+▁transferred	-12228
+nell	-12229
+▁dying	-12230
+▁chill	-12231
+▁grill	-12232
+▁Feed	-12233
+aus	-12234
+▁Boys	-12235
+umbai	-12236
+▁tribute	-12237
+▁Rating	-12238
+▁Brun	-12239
+▁charming	-12240
+▁Abs	-12241
+▁Fell	-12242
+▁tropical	-12243
+▁substitute	-12244
+▁multip	-12245
+achers	-12246
+▁Collins	-12247
+▁Floor	-12248
+▁glory	-12249
+size	-12250
+▁converted	-12251
+▁Morris	-12252
+neath	-12253
+▁Links	-12254
+▁globally	-12255
+▁recycling	-12256
+▁containers	-12257
+marks	-12258
+▁pear	-12259
+▁Soul	-12260
+▁secretary	-12261
+▁MORE	-12262
+▁Studios	-12263
+▁portray	-12264
+Phot	-12265
+▁Franklin	-12266
+▁Gay	-12267
+▁honestly	-12268
+▁crisp	-12269
+▁beta	-12270
+▁bron	-12271
+itchell	-12272
+▁encourages	-12273
+▁scroll	-12274
+▁closest	-12275
+▁hike	-12276
+▁conferences	-12277
+▁accepting	-12278
+▁technological	-12279
+▁Warri	-12280
+▁Match	-12281
+anne	-12282
+▁varieties	-12283
+▁freel	-12284
+▁Senator	-12285
+▁basement	-12286
+▁soy	-12287
+▁convinced	-12288
+erald	-12289
+▁inbox	-12290
+▁wings	-12291
+▁SEC	-12292
+▁duo	-12293
+▁Communication	-12294
+▁Lay	-12295
+▁Primary	-12296
+Car	-12297
+service	-12298
+▁vendor	-12299
+▁peers	-12300
+oline	-12301
+uh	-12302
+▁intim	-12303
+▁Nob	-12304
+▁cleaned	-12305
+▁sliced	-12306
+▁measuring	-12307
+▁receiver	-12308
+SM	-12309
+▁grades	-12310
+▁variations	-12311
+▁Karen	-12312
+▁authorized	-12313
+▁animation	-12314
+▁lying	-12315
+▁Verm	-12316
+▁deter	-12317
+▁Soph	-12318
+▁Beyond	-12319
+CR	-12320
+▁sail	-12321
+▁Consumer	-12322
+▁educate	-12323
+▁investigating	-12324
+▁modules	-12325
+▁universal	-12326
+▁parad	-12327
+▁Transfer	-12328
+ogy	-12329
+leading	-12330
+▁outlets	-12331
+▁prospective	-12332
+▁knowledgeable	-12333
+▁thumb	-12334
+▁Regular	-12335
+▁invested	-12336
+▁ber	-12337
+orient	-12338
+otype	-12339
+▁herbs	-12340
+▁investor	-12341
+▁stones	-12342
+▁controversial	-12343
+▁whereas	-12344
+▁Managing	-12345
+western	-12346
+rons	-12347
+▁Fisher	-12348
+▁stepped	-12349
+▁Sri	-12350
+▁hills	-12351
+▁awa	-12352
+▁criticism	-12353
+▁clever	-12354
+lette	-12355
+mental	-12356
+▁gaining	-12357
+▁liver	-12358
+▁consolid	-12359
+▁Od	-12360
+▁jet	-12361
+▁syrup	-12362
+▁Honda	-12363
+▁Respons	-12364
+▁neighbour	-12365
+▁dive	-12366
+▁elderly	-12367
+▁prosecut	-12368
+sub	-12369
+▁traditions	-12370
+rogen	-12371
+▁motorcycle	-12372
+▁EST	-12373
+encer	-12374
+▁Payment	-12375
+ventions	-12376
+▁Kid	-12377
+▁patience	-12378
+▁Gray	-12379
+Eng	-12380
+▁startup	-12381
+▁lover	-12382
+struct	-12383
+▁rolls	-12384
+▁refresh	-12385
+▁dropping	-12386
+ele	-12387
+▁Syn	-12388
+▁praise	-12389
+▁communic	-12390
+▁excessive	-12391
+Sm	-12392
+▁riders	-12393
+▁lounge	-12394
+▁Guy	-12395
+▁Built	-12396
+▁visitor	-12397
+▁roster	-12398
+▁distinctive	-12399
+▁highway	-12400
+▁Linked	-12401
+azy	-12402
+▁Hit	-12403
+▁packing	-12404
+mons	-12405
+▁Olympics	-12406
+▁corners	-12407
+▁cere	-12408
+▁shame	-12409
+arer	-12410
+▁Oscar	-12411
+▁visibility	-12412
+user	-12413
+▁Worth	-12414
+▁Ora	-12415
+Aut	-12416
+▁bride	-12417
+▁independently	-12418
+▁Commissioner	-12419
+▁alumni	-12420
+▁clips	-12421
+isting	-12422
+lig	-12423
+▁Guest	-12424
+▁durability	-12425
+▁analysts	-12426
+▁Wheel	-12427
+▁Indonesia	-12428
+▁bother	-12429
+▁Thai	-12430
+esc	-12431
+mg	-12432
+Home	-12433
+▁Palestin	-12434
+idges	-12435
+▁Jimmy	-12436
+▁flip	-12437
+▁Warren	-12438
+▁overlook	-12439
+uv	-12440
+▁Opport	-12441
+Bay	-12442
+▁reflection	-12443
+▁Crystal	-12444
+▁merchandise	-12445
+▁Pul	-12446
+▁Saudi	-12447
+TO	-12448
+▁Players	-12449
+▁departure	-12450
+▁Murray	-12451
+▁nick	-12452
+▁relate	-12453
+▁triple	-12454
+▁NCAA	-12455
+▁customize	-12456
+▁envelop	-12457
+▁fought	-12458
+▁Expert	-12459
+owing	-12460
+▁exempt	-12461
+ielder	-12462
+appro	-12463
+intendo	-12464
+▁coins	-12465
+▁Vi	-12466
+auge	-12467
+▁Switzerland	-12468
+▁Proble	-12469
+eks	-12470
+▁Programs	-12471
+▁Tyler	-12472
+▁graduation	-12473
+▁Deputy	-12474
+igenous	-12475
+quire	-12476
+▁cocktail	-12477
+lon	-12478
+▁Barcelona	-12479
+▁maximize	-12480
+▁inflation	-12481
+▁Hung	-12482
+▁potato	-12483
+▁Rather	-12484
+▁Pad	-12485
+▁trash	-12486
+▁dependent	-12487
+▁invitation	-12488
+▁secrets	-12489
+▁rab	-12490
+▁Conserv	-12491
+▁subscribers	-12492
+mentation	-12493
+▁Panel	-12494
+oin	-12495
+▁burden	-12496
+”)	-12497
+Jan	-12498
+rors	-12499
+ndant	-12500
+kle	-12501
+▁wage	-12502
+▁Hans	-12503
+▁wool	-12504
+▁beneath	-12505
+▁automation	-12506
+▁worlds	-12507
+▁drawings	-12508
+GBT	-12509
+▁jar	-12510
+▁mattress	-12511
+▁intake	-12512
+erver	-12513
+▁priorities	-12514
+▁comply	-12515
+▁continuously	-12516
+▁bacon	-12517
+▁naked	-12518
+▁RAM	-12519
+▁Fra	-12520
+▁Favor	-12521
+▁technicians	-12522
+▁Operations	-12523
+▁Close	-12524
+▁momentum	-12525
+▁draws	-12526
+▁Cit	-12527
+▁programmes	-12528
+▁warehouse	-12529
+▁Often	-12530
+senal	-12531
+First	-12532
+isy	-12533
+▁acceptable	-12534
+▁Fitness	-12535
+worthy	-12536
+▁drew	-12537
+▁Schedule	-12538
+▁baked	-12539
+▁citizen	-12540
+ols	-12541
+▁Choice	-12542
+▁ARE	-12543
+▁Document	-12544
+▁Evans	-12545
+▁batch	-12546
+▁NOW	-12547
+▁renewable	-12548
+▁Racing	-12549
+▁Breakfast	-12550
+▁Elementary	-12551
+▁Birmingham	-12552
+▁Pret	-12553
+▁companion	-12554
+▁gluten	-12555
+TON	-12556
+▁centuries	-12557
+▁blocked	-12558
+▁scary	-12559
+▁preventing	-12560
+▁Kel	-12561
+Click	-12562
+▁Applications	-12563
+cr	-12564
+▁Egg	-12565
+▁Joh	-12566
+aws	-12567
+▁Pictures	-12568
+▁hass	-12569
+▁Hell	-12570
+▁dairy	-12571
+istical	-12572
+▁pumpkin	-12573
+arat	-12574
+▁safer	-12575
+▁Hat	-12576
+▁shit	-12577
+▁simultaneously	-12578
+idding	-12579
+▁Clinic	-12580
+GO	-12581
+▁cycling	-12582
+▁Dal	-12583
+stairs	-12584
+▁reun	-12585
+▁Kath	-12586
+▁notification	-12587
+▁considerable	-12588
+▁basics	-12589
+▁Rene	-12590
+▁Resource	-12591
+▁nutrients	-12592
+▁glue	-12593
+▁magnific	-12594
+▁Added	-12595
+▁Directors	-12596
+▁rejected	-12597
+▁almond	-12598
+▁Clinical	-12599
+▁psychological	-12600
+▁shore	-12601
+▁welfare	-12602
+▁coronavirus	-12603
+▁acknowledge	-12604
+▁Sin	-12605
+▁aesthetic	-12606
+wed	-12607
+DI	-12608
+▁cree	-12609
+written	-12610
+▁surgical	-12611
+bin	-12612
+▁endorse	-12613
+▁Taiwan	-12614
+▁Sau	-12615
+▁vig	-12616
+ivic	-12617
+▁islands	-12618
+▁Citiz	-12619
+▁FO	-12620
+▁disk	-12621
+▁shake	-12622
+▁graduates	-12623
+▁horiz	-12624
+bn	-12625
+check	-12626
+Pub	-12627
+▁undergraduate	-12628
+vd	-12629
+▁Vide	-12630
+▁Certificate	-12631
+▁Dakota	-12632
+othy	-12633
+▁Swe	-12634
+ropolitan	-12635
+▁progressive	-12636
+▁smartphones	-12637
+Tech	-12638
+▁starter	-12639
+▁commented	-12640
+▁Fel	-12641
+fulness	-12642
+specific	-12643
+eta	-12644
+▁Lower	-12645
+▁Healthcare	-12646
+▁VIP	-12647
+▁chick	-12648
+▁drunk	-12649
+fi	-12650
+hist	-12651
+ometer	-12652
+borough	-12653
+olo	-12654
+▁deserves	-12655
+rylic	-12656
+▁Cards	-12657
+driven	-12658
+▁carb	-12659
+▁Display	-12660
+▁neglect	-12661
+▁bonds	-12662
+▁Rele	-12663
+irable	-12664
+About	-12665
+roph	-12666
+▁leisure	-12667
+▁vacuum	-12668
+smith	-12669
+▁backing	-12670
+▁Rank	-12671
+▁anger	-12672
+▁passage	-12673
+▁metab	-12674
+Mon	-12675
+▁Risk	-12676
+▁iPod	-12677
+▁killer	-12678
+▁vinegar	-12679
+agu	-12680
+ONE	-12681
+▁Patri	-12682
+▁Exhib	-12683
+oub	-12684
+opes	-12685
+ificates	-12686
+▁Terry	-12687
+▁Around	-12688
+▁Gardens	-12689
+▁Alt	-12690
+▁opponents	-12691
+▁Lov	-12692
+▁cited	-12693
+▁outlet	-12694
+▁steep	-12695
+▁corporations	-12696
+▁mothers	-12697
+▁rede	-12698
+▁politicians	-12699
+Oct	-12700
+isd	-12701
+▁Mitchell	-12702
+aments	-12703
+▁mainstream	-12704
+▁easiest	-12705
+asive	-12706
+uitive	-12707
+▁premiere	-12708
+▁classical	-12709
+igs	-12710
+▁Masters	-12711
+▁resil	-12712
+▁heated	-12713
+▁dip	-12714
+ORD	-12715
+FR	-12716
+attered	-12717
+dated	-12718
+▁ghost	-12719
+▁hence	-12720
+▁cinnamon	-12721
+▁Parks	-12722
+▁independence	-12723
+▁handmade	-12724
+▁accent	-12725
+▁Costa	-12726
+▁emerged	-12727
+▁bree	-12728
+▁Serve	-12729
+▁enemies	-12730
+▁Commons	-12731
+rapeutic	-12732
+▁pose	-12733
+▁Reports	-12734
+▁Cleaning	-12735
+▁delet	-12736
+▁longest	-12737
+Day	-12738
+▁creatures	-12739
+▁packs	-12740
+▁cuisine	-12741
+pin	-12742
+▁conducting	-12743
+▁encry	-12744
+▁Browse	-12745
+▁tongue	-12746
+John	-12747
+▁Dimensions	-12748
+▁grandchildren	-12749
+▁poem	-12750
+▁templates	-12751
+▁Snap	-12752
+▁Theater	-12753
+▁Diet	-12754
+▁fare	-12755
+▁opponent	-12756
+▁Dublin	-12757
+▁deciding	-12758
+▁LOVE	-12759
+▁lid	-12760
+▁Palace	-12761
+▁reaches	-12762
+▁***	-12763
+▁unemployment	-12764
+chi	-12765
+▁Clay	-12766
+▁supplements	-12767
+▁EM	-12768
+▁Buffalo	-12769
+▁torn	-12770
+▁Marvel	-12771
+▁Arkansas	-12772
+▁Marshall	-12773
+▁memorial	-12774
+▁conce	-12775
+▁cleaner	-12776
+▁Guid	-12777
+▁crust	-12778
+▁Written	-12779
+▁retailer	-12780
+▁Gran	-12781
+wy	-12782
+▁silent	-12783
+ropriate	-12784
+▁Outdoor	-12785
+▁soda	-12786
+▁affecting	-12787
+▁Impact	-12788
+▁holder	-12789
+▁Ori	-12790
+▁ESPN	-12791
+▁slice	-12792
+▁transit	-12793
+▁variable	-12794
+▁Stage	-12795
+ASS	-12796
+▁superm	-12797
+▁negotiations	-12798
+▁Robin	-12799
+omore	-12800
+▁Physical	-12801
+isor	-12802
+▁gad	-12803
+▁achievements	-12804
+Art	-12805
+▁travels	-12806
+▁Hunt	-12807
+▁quoted	-12808
+▁hazard	-12809
+▁observe	-12810
+▁consistency	-12811
+Med	-12812
+▁Mason	-12813
+erto	-12814
+▁ramp	-12815
+▁permitted	-12816
+▁blast	-12817
+mi	-12818
+▁Sold	-12819
+▁Seven	-12820
+▁trainer	-12821
+rowing	-12822
+▁Pap	-12823
+▁Yeah	-12824
+▁Sent	-12825
+▁digit	-12826
+▁bake	-12827
+▁Soon	-12828
+▁hoped	-12829
+▁underground	-12830
+▁travelers	-12831
+▁Agree	-12832
+▁meditation	-12833
+▁inning	-12834
+produ	-12835
+ocket	-12836
+▁Faculty	-12837
+▁Volume	-12838
+▁mysterious	-12839
+▁recycled	-12840
+▁RV	-12841
+▁Campaign	-12842
+▁ingredient	-12843
+▁Honey	-12844
+▁pert	-12845
+▁Fight	-12846
+▁bullet	-12847
+▁Left	-12848
+▁Request	-12849
+▁thirty	-12850
+oting	-12851
+aria	-12852
+▁....	-12853
+Apr	-12854
+▁Douglas	-12855
+▁Keith	-12856
+▁compound	-12857
+▁shoulders	-12858
+izard	-12859
+▁limitations	-12860
+▁pedest	-12861
+▁eco	-12862
+▁municipal	-12863
+▁improves	-12864
+▁slides	-12865
+▁bail	-12866
+hh	-12867
+▁costly	-12868
+▁CAN	-12869
+▁Fit	-12870
+▁restricted	-12871
+rosc	-12872
+▁Ideas	-12873
+▁bugs	-12874
+▁bump	-12875
+▁groom	-12876
+▁gem	-12877
+▁allegations	-12878
+▁paths	-12879
+▁incorporated	-12880
+iplinary	-12881
+liminary	-12882
+cies	-12883
+agraph	-12884
+▁Federation	-12885
+drop	-12886
+▁loading	-12887
+enery	-12888
+▁Associates	-12889
+▁whisk	-12890
+▁architectural	-12891
+▁threatened	-12892
+▁underneath	-12893
+▁journalist	-12894
+▁Empire	-12895
+▁Tob	-12896
+ardo	-12897
+▁Revolution	-12898
+▁UP	-12899
+▁archive	-12900
+▁Storm	-12901
+▁litigation	-12902
+▁Military	-12903
+▁retro	-12904
+▁Shan	-12905
+hop	-12906
+▁Zone	-12907
+etheless	-12908
+▁tempor	-12909
+▁barg	-12910
+mented	-12911
+stage	-12912
+▁Platform	-12913
+▁tablespoon	-12914
+laimed	-12915
+opic	-12916
+IAL	-12917
+alia	-12918
+▁mutual	-12919
+aul	-12920
+▁Interior	-12921
+▁Episode	-12922
+▁Roger	-12923
+▁Cameron	-12924
+▁respected	-12925
+▁ic	-12926
+▁lining	-12927
+RO	-12928
+▁Hus	-12929
+▁terminal	-12930
+▁remembered	-12931
+▁lamp	-12932
+▁creator	-12933
+▁releasing	-12934
+▁understands	-12935
+▁Bake	-12936
+lla	-12937
+inction	-12938
+▁ups	-12939
+iasm	-12940
+▁Horn	-12941
+▁connectivity	-12942
+bec	-12943
+▁radical	-12944
+▁vegg	-12945
+arently	-12946
+ateral	-12947
+trans	-12948
+▁incom	-12949
+▁influenced	-12950
+▁bankruptcy	-12951
+▁inval	-12952
+Reg	-12953
+▁acceptance	-12954
+afood	-12955
+eps	-12956
+pectives	-12957
+▁shelves	-12958
+▁Rate	-12959
+▁sensors	-12960
+▁Crown	-12961
+▁visa	-12962
+▁skirt	-12963
+ui	-12964
+▁proceeds	-12965
+ooting	-12966
+▁Ranch	-12967
+▁revenues	-12968
+ulator	-12969
+▁Cable	-12970
+▁maj	-12971
+WA	-12972
+ACE	-12973
+▁Initi	-12974
+▁Tip	-12975
+▁Aid	-12976
+remy	-12977
+▁Gill	-12978
+▁embedded	-12979
+▁clarity	-12980
+ritional	-12981
+▁gradually	-12982
+▁attraction	-12983
+▁essence	-12984
+girl	-12985
+▁Bluetooth	-12986
+▁Legisl	-12987
+▁shout	-12988
+▁Linda	-12989
+▁ate	-12990
+outube	-12991
+▁inaug	-12992
+▁Columbus	-12993
+rison	-12994
+▁specializes	-12995
+▁Blues	-12996
+click	-12997
+▁physicians	-12998
+ergy	-12999
+▁struggled	-13000
+▁abandoned	-13001
+aired	-13002
+▁Depending	-13003
+▁appearing	-13004
+▁vessel	-13005
+▁Materials	-13006
+▁ranges	-13007
+▁incidents	-13008
+illo	-13009
+▁browsing	-13010
+▁nominated	-13011
+▁sketch	-13012
+ango	-13013
+▁defeated	-13014
+▁Lakes	-13015
+ummer	-13016
+ped	-13017
+▁sewing	-13018
+Over	-13019
+▁inclusion	-13020
+▁Horse	-13021
+▁Shopping	-13022
+▁symbols	-13023
+zech	-13024
+bat	-13025
+bey	-13026
+apor	-13027
+▁discrimination	-13028
+oded	-13029
+▁stressed	-13030
+igue	-13031
+▁unp	-13032
+▁BA	-13033
+▁implications	-13034
+▁trains	-13035
+▁Ple	-13036
+di	-13037
+▁Princess	-13038
+▁notable	-13039
+umper	-13040
+▁manufacture	-13041
+▁Sit	-13042
+▁wides	-13043
+▁struggles	-13044
+▁stroll	-13045
+''	-13046
+assy	-13047
+dig	-13048
+▁Ing	-13049
+arness	-13050
+People	-13051
+▁journalists	-13052
+▁sells	-13053
+▁Rivers	-13054
+ean	-13055
+▁heels	-13056
+▁tar	-13057
+▁Griff	-13058
+▁Liberty	-13059
+agne	-13060
+▁plugin	-13061
+▁regime	-13062
+▁fract	-13063
+▁Parents	-13064
+esis	-13065
+▁Nashville	-13066
+▁Mumbai	-13067
+verages	-13068
+aft	-13069
+▁strengths	-13070
+▁Treas	-13071
+▁attracted	-13072
+▁Prob	-13073
+▁lone	-13074
+▁gloss	-13075
+▁continually	-13076
+▁Knight	-13077
+▁fails	-13078
+▁Eat	-13079
+obs	-13080
+▁Spons	-13081
+▁minimize	-13082
+▁Fri	-13083
+▁solving	-13084
+pers	-13085
+▁introducing	-13086
+▁stap	-13087
+cover	-13088
+Dr	-13089
+▁cabinets	-13090
+▁reminds	-13091
+Down	-13092
+UK	-13093
+▁Wireless	-13094
+▁determination	-13095
+▁Eagles	-13096
+▁Pages	-13097
+▁hospitality	-13098
+amas	-13099
+▁joke	-13100
+▁Pic	-13101
+▁decorated	-13102
+▁editorial	-13103
+▁conjunction	-13104
+itcher	-13105
+▁complexity	-13106
+atility	-13107
+▁Madrid	-13108
+▁Lag	-13109
+▁Nintendo	-13110
+lu	-13111
+▁chemistry	-13112
+▁Manual	-13113
+▁topped	-13114
+▁mayor	-13115
+▁Strategy	-13116
+▁Delta	-13117
+▁revers	-13118
+▁classified	-13119
+▁pairs	-13120
+▁Baptist	-13121
+▁shield	-13122
+▁permits	-13123
+umbers	-13124
+ocks	-13125
+Per	-13126
+▁nationally	-13127
+▁Cotton	-13128
+ivan	-13129
+▁Aer	-13130
+▁Letter	-13131
+edar	-13132
+▁notified	-13133
+▁humanity	-13134
+Back	-13135
+▁mart	-13136
+ensed	-13137
+such	-13138
+▁Knowledge	-13139
+▁intensity	-13140
+▁TN	-13141
+arms	-13142
+▁Different	-13143
+▁meantime	-13144
+pread	-13145
+plete	-13146
+▁practicing	-13147
+▁brut	-13148
+▁barriers	-13149
+▁fel	-13150
+orted	-13151
+▁nails	-13152
+▁Brothers	-13153
+ocur	-13154
+▁elite	-13155
+▁FM	-13156
+▁tasting	-13157
+▁deemed	-13158
+ontal	-13159
+▁slices	-13160
+▁calculated	-13161
+▁woods	-13162
+▁Offer	-13163
+▁locals	-13164
+▁Merc	-13165
+▁manages	-13166
+▁coastal	-13167
+▁Teacher	-13168
+▁BBQ	-13169
+▁ho	-13170
+▁Disease	-13171
+imens	-13172
+Su	-13173
+▁Throughout	-13174
+▁Indeed	-13175
+▁cannabis	-13176
+anean	-13177
+▁Height	-13178
+aho	-13179
+▁Guarant	-13180
+▁Woods	-13181
+▁Faith	-13182
+▁Glen	-13183
+▁unnecessary	-13184
+LP	-13185
+boro	-13186
+VI	-13187
+gary	-13188
+▁traveled	-13189
+ulu	-13190
+rock	-13191
+pired	-13192
+▁runners	-13193
+ACH	-13194
+▁plumbing	-13195
+rates	-13196
+▁sticks	-13197
+▁tiles	-13198
+▁Tes	-13199
+▁Spect	-13200
+▁rebuild	-13201
+▁pension	-13202
+▁nearest	-13203
+▁Kyle	-13204
+▁Monte	-13205
+compass	-13206
+dered	-13207
+▁Package	-13208
+▁Imagine	-13209
+inks	-13210
+▁Stri	-13211
+▁approached	-13212
+▁LGBT	-13213
+▁None	-13214
+▁Airl	-13215
+▁TA	-13216
+news	-13217
+▁crossed	-13218
+arrings	-13219
+▁inclusive	-13220
+oustic	-13221
+alling	-13222
+▁Arena	-13223
+▁landed	-13224
+ropract	-13225
+▁Pinterest	-13226
+▁coating	-13227
+▁Adjust	-13228
+ocial	-13229
+▁turb	-13230
+▁joins	-13231
+▁volunt	-13232
+ORK	-13233
+▁novels	-13234
+cedes	-13235
+▁bathrooms	-13236
+▁Popular	-13237
+▁straightforward	-13238
+▁Lost	-13239
+▁ports	-13240
+thod	-13241
+▁Coal	-13242
+▁puzzle	-13243
+▁instances	-13244
+▁establishing	-13245
+ographer	-13246
+▁recruiting	-13247
+▁Crime	-13248
+▁rewarding	-13249
+▁runner	-13250
+▁touched	-13251
+▁CNN	-13252
+▁defence	-13253
+connect	-13254
+▁strap	-13255
+▁py	-13256
+Best	-13257
+▁Fif	-13258
+▁junk	-13259
+▁convention	-13260
+omer	-13261
+▁DM	-13262
+▁Points	-13263
+▁paired	-13264
+▁Jamie	-13265
+▁scared	-13266
+quisite	-13267
+▁REAL	-13268
+▁todd	-13269
+▁Flat	-13270
+itual	-13271
+▁Murphy	-13272
+▁Wor	-13273
+some	-13274
+▁Billy	-13275
+Black	-13276
+▁tbsp	-13277
+umm	-13278
+▁alerts	-13279
+ocate	-13280
+rella	-13281
+atinum	-13282
+▁targeting	-13283
+▁Ster	-13284
+▁receipt	-13285
+▁Electronic	-13286
+▁lane	-13287
+▁Northwest	-13288
+▁quilt	-13289
+ijing	-13290
+▁trauma	-13291
+▁Marie	-13292
+▁controller	-13293
+▁harass	-13294
+HC	-13295
+▁warming	-13296
+▁Spl	-13297
+▁coin	-13298
+elli	-13299
+izers	-13300
+▁dispute	-13301
+▁sooner	-13302
+▁lenses	-13303
+▁fatal	-13304
+anka	-13305
+▁delayed	-13306
+▁electro	-13307
+▁combining	-13308
+lett	-13309
+uming	-13310
+▁identical	-13311
+▁Romney	-13312
+▁Chand	-13313
+▁abortion	-13314
+▁explaining	-13315
+▁shar	-13316
+▁Rio	-13317
+▁wardrobe	-13318
+▁promotions	-13319
+ITE	-13320
+▁Driver	-13321
+▁cooler	-13322
+▁Vista	-13323
+▁Barry	-13324
+▁fireplace	-13325
+pg	-13326
+function	-13327
+▁experimental	-13328
+ogurt	-13329
+▁analys	-13330
+▁Poland	-13331
+bris	-13332
+▁chains	-13333
+roe	-13334
+uffy	-13335
+▁Carm	-13336
+attery	-13337
+▁Emma	-13338
+▁vessels	-13339
+▁rebounds	-13340
+may	-13341
+▁ethical	-13342
+OUT	-13343
+▁floating	-13344
+▁Medium	-13345
+▁loyalty	-13346
+ede	-13347
+▁creamy	-13348
+▁volumes	-13349
+XX	-13350
+cos	-13351
+▁retrie	-13352
+Look	-13353
+rise	-13354
+▁transformed	-13355
+▁homework	-13356
+ixtures	-13357
+▁advisor	-13358
+pace	-13359
+▁shred	-13360
+mal	-13361
+▁Thor	-13362
+▁Mob	-13363
+▁tob	-13364
+▁Graduate	-13365
+▁cant	-13366
+commun	-13367
+▁reactions	-13368
+▁Pump	-13369
+▁Vill	-13370
+GM	-13371
+▁explores	-13372
+▁searches	-13373
+▁occasional	-13374
+uated	-13375
+▁Capit	-13376
+▁Ens	-13377
+▁warmth	-13378
+▁density	-13379
+▁eighth	-13380
+strong	-13381
+dis	-13382
+▁incorrect	-13383
+▁gest	-13384
+▁Oracle	-13385
+▁separated	-13386
+▁derived	-13387
+▁relatives	-13388
+▁serial	-13389
+itudes	-13390
+▁MLS	-13391
+▁Progress	-13392
+▁McDonald	-13393
+▁restored	-13394
+forced	-13395
+▁Agent	-13396
+▁Bot	-13397
+aneous	-13398
+▁enthusiasm	-13399
+▁''	-13400
+▁Birthday	-13401
+▁transparency	-13402
+▁nude	-13403
+ometric	-13404
+▁backgrounds	-13405
+IME	-13406
+▁parallel	-13407
+Out	-13408
+▁Brandon	-13409
+▁Spark	-13410
+▁Mosc	-13411
+▁Chat	-13412
+▁literary	-13413
+▁Teen	-13414
+▁chic	-13415
+▁Mine	-13416
+▁utilized	-13417
+▁Barack	-13418
+▁socks	-13419
+▁Budget	-13420
+▁kicked	-13421
+▁Conditions	-13422
+▁sustained	-13423
+▁roller	-13424
+▁Counsel	-13425
+▁freely	-13426
+▁reservation	-13427
+▁penet	-13428
+▁Richards	-13429
+▁Ry	-13430
+▁uncomfortable	-13431
+▁excuse	-13432
+▁medal	-13433
+▁arrives	-13434
+position	-13435
+▁Dent	-13436
+sor	-13437
+ques	-13438
+▁Cav	-13439
+▁recovered	-13440
+Aug	-13441
+▁Furniture	-13442
+▁unpre	-13443
+▁Going	-13444
+iov	-13445
+▁inviting	-13446
+▁populations	-13447
+▁oak	-13448
+▁coupons	-13449
+▁founding	-13450
+▁NA	-13451
+▁holders	-13452
+▁Alice	-13453
+▁Basket	-13454
+World	-13455
+▁grandmother	-13456
+▁Excel	-13457
+▁biological	-13458
+▁Healthy	-13459
+▁Prop	-13460
+wl	-13461
+SH	-13462
+▁Route	-13463
+ancies	-13464
+▁Tun	-13465
+▁destruction	-13466
+▁Nancy	-13467
+▁rod	-13468
+END	-13469
+▁thermal	-13470
+imedia	-13471
+▁professionally	-13472
+arios	-13473
+bul	-13474
+Friday	-13475
+▁comed	-13476
+▁jumping	-13477
+▁Lang	-13478
+▁Nat	-13479
+▁inhib	-13480
+▁challenged	-13481
+rah	-13482
+aukee	-13483
+▁upgrades	-13484
+▁Grove	-13485
+▁Economics	-13486
+▁Pine	-13487
+▁Doug	-13488
+Good	-13489
+▁Lots	-13490
+▁ampl	-13491
+▁FAQ	-13492
+▁broader	-13493
+▁preference	-13494
+▁profound	-13495
+oni	-13496
+▁Eagle	-13497
+▁Hampshire	-13498
+▁texts	-13499
+idel	-13500
+▁attacked	-13501
+type	-13502
+▁surprisingly	-13503
+▁Circle	-13504
+▁deadly	-13505
+mir	-13506
+▁silk	-13507
+▁Lions	-13508
+▁accurately	-13509
+▁portrait	-13510
+▁Candid	-13511
+▁grams	-13512
+▁barrier	-13513
+▁convicted	-13514
+▁Items	-13515
+base	-13516
+▁Designer	-13517
+▁musician	-13518
+▁reprodu	-13519
+▁dub	-13520
+▁Hotels	-13521
+▁fortunate	-13522
+onz	-13523
+▁Priv	-13524
+▁Ru	-13525
+▁adapted	-13526
+▁IBM	-13527
+▁Tol	-13528
+▁apples	-13529
+▁Multiple	-13530
+▁Typ	-13531
+▁suppose	-13532
+▁lands	-13533
+▁adjusted	-13534
+▁Johnny	-13535
+▁Peters	-13536
+▁Viol	-13537
+▁ug	-13538
+▁fier	-13539
+▁evident	-13540
+▁delays	-13541
+hole	-13542
+▁turkey	-13543
+rek	-13544
+▁mandatory	-13545
+▁Workshop	-13546
+▁pending	-13547
+iners	-13548
+phony	-13549
+▁Vitamin	-13550
+▁Ashley	-13551
+▁Luther	-13552
+Life	-13553
+▁Hudson	-13554
+umph	-13555
+▁ribbon	-13556
+▁Fortunately	-13557
+pensive	-13558
+pal	-13559
+▁stimul	-13560
+▁referring	-13561
+▁fork	-13562
+▁nurses	-13563
+cam	-13564
+▁Fo	-13565
+Part	-13566
+▁Defin	-13567
+SO	-13568
+▁frost	-13569
+▁yeah	-13570
+SI	-13571
+urd	-13572
+▁Ni	-13573
+▁farming	-13574
+▁PT	-13575
+▁blade	-13576
+anas	-13577
+▁pressing	-13578
+▁cakes	-13579
+▁repeatedly	-13580
+▁sellers	-13581
+aning	-13582
+▁buried	-13583
+▁Mechan	-13584
+urdy	-13585
+hess	-13586
+▁contend	-13587
+▁panc	-13588
+▁Supply	-13589
+▁MY	-13590
+oooo	-13591
+watch	-13592
+▁argued	-13593
+▁deliber	-13594
+▁TM	-13595
+▁Neil	-13596
+▁exams	-13597
+▁prosec	-13598
+eded	-13599
+▁NHL	-13600
+▁Swedish	-13601
+Orig	-13602
+rupted	-13603
+▁Status	-13604
+▁newer	-13605
+▁entity	-13606
+▁salmon	-13607
+▁premises	-13608
+▁FBI	-13609
+▁organised	-13610
+▁twelve	-13611
+▁catching	-13612
+ingo	-13613
+▁planted	-13614
+▁pickup	-13615
+adays	-13616
+▁Tab	-13617
+ponse	-13618
+▁violation	-13619
+artered	-13620
+▁leverage	-13621
+adi	-13622
+▁silly	-13623
+▁cozy	-13624
+▁relaxation	-13625
+ski	-13626
+▁smoothly	-13627
+▁assisted	-13628
+your	-13629
+▁chapters	-13630
+▁Covid	-13631
+▁promotes	-13632
+▁orientation	-13633
+▁Standards	-13634
+eli	-13635
+▁Myst	-13636
+▁Warner	-13637
+due	-13638
+Cal	-13639
+▁interpretation	-13640
+▁provisions	-13641
+▁traged	-13642
+▁dramatically	-13643
+▁("	-13644
+▁Jeremy	-13645
+▁midnight	-13646
+closed	-13647
+▁Opera	-13648
+▁needing	-13649
+▁Climate	-13650
+▁PRE	-13651
+▁Recovery	-13652
+▁SUV	-13653
+▁Surv	-13654
+▁flavour	-13655
+▁SER	-13656
+▁Remov	-13657
+▁strips	-13658
+atson	-13659
+▁AF	-13660
+founder	-13661
+▁steal	-13662
+▁thrive	-13663
+otto	-13664
+▁balcon	-13665
+▁liberal	-13666
+▁matched	-13667
+▁Richmond	-13668
+▁Circuit	-13669
+▁slim	-13670
+color	-13671
+▁logistics	-13672
+▁centres	-13673
+▁Decor	-13674
+sal	-13675
+▁Parking	-13676
+▁wrist	-13677
+▁intensive	-13678
+▁significance	-13679
+▁Agriculture	-13680
+▁silence	-13681
+omas	-13682
+▁camps	-13683
+▁overt	-13684
+▁revol	-13685
+▁cycl	-13686
+▁Pent	-13687
+▁dict	-13688
+▁prayers	-13689
+▁arc	-13690
+arium	-13691
+▁spice	-13692
+ournals	-13693
+okia	-13694
+▁freshman	-13695
+▁scent	-13696
+▁integral	-13697
+▁avo	-13698
+▁Skills	-13699
+▁Len	-13700
+▁glam	-13701
+▁Isa	-13702
+▁Arc	-13703
+NG	-13704
+▁mos	-13705
+▁counseling	-13706
+▁Almost	-13707
+▁reflected	-13708
+hero	-13709
+▁strikes	-13710
+▁moistur	-13711
+▁magnificent	-13712
+▁exped	-13713
+▁tattoo	-13714
+▁urged	-13715
+▁Kick	-13716
+▁responsive	-13717
+nor	-13718
+▁pipeline	-13719
+▁Naz	-13720
+ptic	-13721
+▁WiFi	-13722
+▁precisely	-13723
+▁animated	-13724
+▁Pour	-13725
+▁skip	-13726
+▁aggreg	-13727
+iterr	-13728
+▁Pure	-13729
+▁banned	-13730
+▁nurt	-13731
+▁Parts	-13732
+▁//	-13733
+▁Arthur	-13734
+▁concerts	-13735
+▁streak	-13736
+normal	-13737
+▁lime	-13738
+▁cargo	-13739
+▁kg	-13740
+▁besides	-13741
+▁shorts	-13742
+▁shy	-13743
+▁Hab	-13744
+▁newspapers	-13745
+▁Sr	-13746
+▁Mos	-13747
+▁enthusiasts	-13748
+▁differ	-13749
+▁Airlines	-13750
+▁hott	-13751
+igo	-13752
+eston	-13753
+▁Rules	-13754
+▁affair	-13755
+▁Moving	-13756
+group	-13757
+▁Haz	-13758
+▁resc	-13759
+▁Kl	-13760
+osph	-13761
+ioned	-13762
+.:	-13763
+▁protects	-13764
+▁backs	-13765
+▁brass	-13766
+▁Interview	-13767
+amous	-13768
+ucker	-13769
+ellery	-13770
+▁imaging	-13771
+start	-13772
+hester	-13773
+▁subt	-13774
+▁unders	-13775
+▁urge	-13776
+ais	-13777
+▁Either	-13778
+▁melt	-13779
+▁ancest	-13780
+▁decorative	-13781
+▁manifest	-13782
+▁sandwich	-13783
+▁heroes	-13784
+mus	-13785
+▁temple	-13786
+▁stabil	-13787
+reation	-13788
+▁Upper	-13789
+▁confusion	-13790
+ulent	-13791
+▁Answer	-13792
+▁hilar	-13793
+▁Leather	-13794
+▁Dear	-13795
+▁widespread	-13796
+▁Script	-13797
+rimp	-13798
+waukee	-13799
+▁towel	-13800
+arten	-13801
+▁pursuing	-13802
+ographical	-13803
+▁OH	-13804
+▁modes	-13805
+▁Average	-13806
+Who	-13807
+▁Picture	-13808
+▁cosmetic	-13809
+▁crossing	-13810
+olester	-13811
+▁Especially	-13812
+ammed	-13813
+▁branding	-13814
+bitious	-13815
+▁suggestion	-13816
+▁spreading	-13817
+▁Veterans	-13818
+▁Camera	-13819
+isons	-13820
+▁intend	-13821
+▁Issue	-13822
+▁indul	-13823
+▁Specific	-13824
+Posted	-13825
+hesive	-13826
+▁Switch	-13827
+▁desires	-13828
+▁pile	-13829
+▁styl	-13830
+▁Stanley	-13831
+▁Beijing	-13832
+▁festivals	-13833
+▁additionally	-13834
+▁forgot	-13835
+▁cleared	-13836
+▁Solid	-13837
+hyd	-13838
+▁surroundings	-13839
+▁outlook	-13840
+▁fires	-13841
+▁downloads	-13842
+▁mesh	-13843
+▁organizing	-13844
+........	-13845
+▁bases	-13846
+▁radiation	-13847
+▁shirts	-13848
+▁Actually	-13849
+incinn	-13850
+▁XP	-13851
+▁maintains	-13852
+▁surveillance	-13853
+named	-13854
+▁credential	-13855
+▁blogger	-13856
+▁Archives	-13857
+hu	-13858
+▁Tap	-13859
+▁hassle	-13860
+fox	-13861
+▁immense	-13862
+▁autumn	-13863
+▁vocals	-13864
+▁inhab	-13865
+▁poison	-13866
+Station	-13867
+▁Competition	-13868
+▁clay	-13869
+thal	-13870
+▁stiff	-13871
+▁sung	-13872
+▁Kenya	-13873
+▁spoon	-13874
+▁wallet	-13875
+▁Degree	-13876
+▁coordinator	-13877
+▁Tags	-13878
+▁Mes	-13879
+▁coinc	-13880
+▁treasure	-13881
+▁Publishing	-13882
+aman	-13883
+▁Ble	-13884
+lie	-13885
+▁Running	-13886
+▁promoted	-13887
+▁reson	-13888
+heimer	-13889
+▁surveys	-13890
+rolled	-13891
+▁curve	-13892
+▁Subject	-13893
+oriented	-13894
+allic	-13895
+building	-13896
+▁tuition	-13897
+▁Danny	-13898
+▁obligation	-13899
+▁fond	-13900
+▁suspected	-13901
+▁Tigers	-13902
+ellar	-13903
+▁prosper	-13904
+▁gloves	-13905
+▁Prevention	-13906
+▁Late	-13907
+▁competitions	-13908
+▁Amazing	-13909
+▁bored	-13910
+▁Ye	-13911
+TY	-13912
+astics	-13913
+jan	-13914
+▁Hack	-13915
+▁generating	-13916
+▁Intelligence	-13917
+▁elementary	-13918
+umbled	-13919
+▁tends	-13920
+ritis	-13921
+▁predicted	-13922
+▁develops	-13923
+▁mineral	-13924
+▁Curt	-13925
+agg	-13926
+▁SQL	-13927
+▁cyl	-13928
+▁vou	-13929
+▁arise	-13930
+▁Jake	-13931
+incinnati	-13932
+▁cit	-13933
+▁Alf	-13934
+mad	-13935
+▁magnetic	-13936
+▁honors	-13937
+▁Academic	-13938
+▁NOTE	-13939
+▁Lion	-13940
+▁approaching	-13941
+▁arriving	-13942
+minded	-13943
+▁Bass	-13944
+▁modify	-13945
+pert	-13946
+▁Norm	-13947
+▁Labour	-13948
+▁TC	-13949
+▁stats	-13950
+lesh	-13951
+▁monu	-13952
+ountain	-13953
+OOK	-13954
+▁Advisory	-13955
+▁schedules	-13956
+▁legitimate	-13957
+▁mast	-13958
+advant	-13959
+tic	-13960
+pher	-13961
+▁Czech	-13962
+▁governance	-13963
+bery	-13964
+▁anonymous	-13965
+▁lb	-13966
+▁jurisd	-13967
+iterranean	-13968
+ref	-13969
+omi	-13970
+▁Kap	-13971
+▁fried	-13972
+▁watches	-13973
+▁Wildlife	-13974
+▁weddings	-13975
+▁traded	-13976
+▁optimization	-13977
+▁Regardless	-13978
+je	-13979
+▁PD	-13980
+▁fucking	-13981
+▁clouds	-13982
+lift	-13983
+▁Ultimate	-13984
+▁advocacy	-13985
+▁rh	-13986
+▁inher	-13987
+eing	-13988
+addle	-13989
+▁Mt	-13990
+▁Abd	-13991
+atched	-13992
+▁radi	-13993
+PH	-13994
+product	-13995
+▁helicop	-13996
+▁ACC	-13997
+▁beaten	-13998
+▁dia	-13999
+▁entities	-14000
+▁Cad	-14001
+▁lol	-14002
+▁IF	-14003
+▁profitable	-14004
+▁Cant	-14005
+▁households	-14006
+.||	-14007
+▁Command	-14008
+brew	-14009
+▁domin	-14010
+▁Downtown	-14011
+▁Adventure	-14012
+icular	-14013
+▁Julie	-14014
+▁earthqu	-14015
+Sec	-14016
+castle	-14017
+▁prox	-14018
+▁Historical	-14019
+IZ	-14020
+▁qualifying	-14021
+cipe	-14022
+▁na	-14023
+▁Steam	-14024
+▁Championships	-14025
+anium	-14026
+▁Moscow	-14027
+▁footprint	-14028
+▁Nord	-14029
+▁valve	-14030
+▁flows	-14031
+rots	-14032
+▁wallpaper	-14033
+▁lace	-14034
+▁goodness	-14035
+▁Specialist	-14036
+▁Lauren	-14037
+▁Finding	-14038
+white	-14039
+▁collar	-14040
+cient	-14041
+▁lear	-14042
+▁corruption	-14043
+▁perception	-14044
+▁licensing	-14045
+ifle	-14046
+▁arguments	-14047
+▁touches	-14048
+urring	-14049
+▁Cop	-14050
+▁addict	-14051
+flu	-14052
+▁celebrities	-14053
+▁Cafe	-14054
+▁arbit	-14055
+▁Speaker	-14056
+▁concentrate	-14057
+▁disappear	-14058
+Fl	-14059
+ERY	-14060
+▁carriers	-14061
+▁debris	-14062
+▁Purchase	-14063
+giene	-14064
+pring	-14065
+▁Ted	-14066
+▁Fame	-14067
+▁masc	-14068
+▁Enc	-14069
+▁Nep	-14070
+▁pest	-14071
+▁nit	-14072
+▁Stick	-14073
+▁vitamins	-14074
+▁privilege	-14075
+▁Pope	-14076
+▁GOP	-14077
+NET	-14078
+▁Foods	-14079
+▁forming	-14080
+▁Constitution	-14081
+▁commentary	-14082
+▁Wikipedia	-14083
+▁mint	-14084
+▁uncertainty	-14085
+▁congr	-14086
+▁EL	-14087
+▁Init	-14088
+▁parliament	-14089
+▁accessed	-14090
+PO	-14091
+▁Neigh	-14092
+▁farms	-14093
+tails	-14094
+amine	-14095
+kward	-14096
+▁Pun	-14097
+large	-14098
+▁recreational	-14099
+▁adjustments	-14100
+▁ambitious	-14101
+ME	-14102
+▁legislative	-14103
+Off	-14104
+▁paragraph	-14105
+▁Norway	-14106
+▁Coming	-14107
+matic	-14108
+▁badly	-14109
+▁MAR	-14110
+▁Ship	-14111
+Gener	-14112
+▁exotic	-14113
+▁connects	-14114
+▁tray	-14115
+▁Corps	-14116
+▁Turkish	-14117
+▁Listing	-14118
+▁functioning	-14119
+▁queries	-14120
+▁Montreal	-14121
+▁Recipe	-14122
+▁adjacent	-14123
+▁microwave	-14124
+▁zones	-14125
+▁Pred	-14126
+▁synthetic	-14127
+keeper	-14128
+▁meter	-14129
+chant	-14130
+▁sake	-14131
+▁Katie	-14132
+▁RT	-14133
+▁deficit	-14134
+▁evolved	-14135
+▁Lie	-14136
+▁PayPal	-14137
+olesterol	-14138
+▁borders	-14139
+▁provin	-14140
+▁Suz	-14141
+▁Surgery	-14142
+▁disagree	-14143
+fil	-14144
+gence	-14145
+▁mins	-14146
+▁chamber	-14147
+▁sore	-14148
+▁persu	-14149
+Christ	-14150
+▁UA	-14151
+hedral	-14152
+iw	-14153
+▁pussy	-14154
+IRE	-14155
+▁OFF	-14156
+▁marry	-14157
+▁damp	-14158
+ourt	-14159
+▁Edin	-14160
+▁manually	-14161
+▁LG	-14162
+▁counting	-14163
+▁SN	-14164
+▁athlete	-14165
+▁Plans	-14166
+▁democracy	-14167
+▁melted	-14168
+ratulations	-14169
+▁Sandy	-14170
+▁Montana	-14171
+▁RC	-14172
+rose	-14173
+▁buses	-14174
+▁BO	-14175
+fare	-14176
+▁verified	-14177
+▁educators	-14178
+▁partial	-14179
+▁Roof	-14180
+▁infected	-14181
+▁pharmaceutical	-14182
+ioxid	-14183
+▁practitioners	-14184
+▁sacrifice	-14185
+PER	-14186
+▁Apparently	-14187
+otherapy	-14188
+▁consume	-14189
+▁facebook	-14190
+▁experiments	-14191
+▁legally	-14192
+▁upgraded	-14193
+stit	-14194
+▁SF	-14195
+▁pel	-14196
+▁congress	-14197
+▁physics	-14198
+▁reservations	-14199
+▁Bishop	-14200
+▁Belgium	-14201
+▁trace	-14202
+▁affiliated	-14203
+▁maxim	-14204
+▁Dow	-14205
+▁Wel	-14206
+▁Higher	-14207
+▁Sud	-14208
+imm	-14209
+▁thri	-14210
+▁obtaining	-14211
+▁constra	-14212
+▁brave	-14213
+▁Villa	-14214
+ierra	-14215
+▁breakdown	-14216
+▁reset	-14217
+▁satisfying	-14218
+▁pars	-14219
+▁Harbor	-14220
+▁Kirk	-14221
+annon	-14222
+▁query	-14223
+oca	-14224
+▁editors	-14225
+▁pupp	-14226
+udge	-14227
+▁streams	-14228
+▁Provide	-14229
+▁Nursing	-14230
+▁elastic	-14231
+appropriate	-14232
+IONS	-14233
+istance	-14234
+▁Pel	-14235
+▁institutional	-14236
+illet	-14237
+▁toug	-14238
+▁discusses	-14239
+▁workflow	-14240
+corn	-14241
+▁annoying	-14242
+▁ONE	-14243
+half	-14244
+▁Heavy	-14245
+fold	-14246
+▁Joint	-14247
+▁carp	-14248
+ido	-14249
+▁Bos	-14250
+▁remed	-14251
+▁psychology	-14252
+▁Potter	-14253
+▁rankings	-14254
+▁sofa	-14255
+▁pools	-14256
+▁contam	-14257
+▁deployment	-14258
+▁circles	-14259
+▁astron	-14260
+▁bubble	-14261
+ippers	-14262
+▁tobacco	-14263
+▁charter	-14264
+▁defic	-14265
+inee	-14266
+QU	-14267
+▁viral	-14268
+▁mentor	-14269
+▁vo	-14270
+▁lifting	-14271
+▁distribute	-14272
+▁Weeke	-14273
+▁avoiding	-14274
+▁LP	-14275
+▁Gear	-14276
+borne	-14277
+▁submitting	-14278
+▁NHS	-14279
+▁Cany	-14280
+▁mounting	-14281
+▁Vintage	-14282
+bling	-14283
+▁relie	-14284
+ancell	-14285
+▁Amend	-14286
+▁HV	-14287
+▁photographers	-14288
+▁planting	-14289
+▁RES	-14290
+▁Previous	-14291
+▁enrolled	-14292
+▁Shore	-14293
+▁Oakland	-14294
+▁unveiled	-14295
+ometry	-14296
+▁Nathan	-14297
+rele	-14298
+▁Cake	-14299
+▁LinkedIn	-14300
+chet	-14301
+▁Discount	-14302
+▁logic	-14303
+▁sometime	-14304
+▁innocent	-14305
+▁landl	-14306
+▁Mario	-14307
+▁readily	-14308
+▁regarded	-14309
+▁Mercedes	-14310
+▁regret	-14311
+▁refrigerator	-14312
+▁Ku	-14313
+▁Podcast	-14314
+▁mall	-14315
+▁missions	-14316
+▁assert	-14317
+▁modeling	-14318
+acies	-14319
+▁Lodge	-14320
+▁crops	-14321
+▁Teaching	-14322
+▁informative	-14323
+▁Patt	-14324
+▁Replace	-14325
+GR	-14326
+▁Wear	-14327
+▁shocked	-14328
+▁Dating	-14329
+▁deputy	-14330
+▁critics	-14331
+oric	-14332
+▁steering	-14333
+▁theore	-14334
+▁NT	-14335
+▁storyt	-14336
+elong	-14337
+▁pole	-14338
+▁tel	-14339
+▁scenarios	-14340
+▁Rug	-14341
+▁structured	-14342
+▁sends	-14343
+▁Growth	-14344
+abul	-14345
+olved	-14346
+▁sealed	-14347
+▁darkness	-14348
+▁caution	-14349
+grad	-14350
+▁posters	-14351
+rik	-14352
+▁administrator	-14353
+▁suggesting	-14354
+SN	-14355
+▁AA	-14356
+aph	-14357
+cedented	-14358
+▁Dennis	-14359
+▁diesel	-14360
+▁Thunder	-14361
+▁reserves	-14362
+asa	-14363
+▁determining	-14364
+▁arena	-14365
+▁Magn	-14366
+unge	-14367
+▁updating	-14368
+▁Sha	-14369
+▁Centers	-14370
+▁greens	-14371
+/.	-14372
+▁compiled	-14373
+▁assignments	-14374
+▁prevents	-14375
+erse	-14376
+▁Words	-14377
+▁voltage	-14378
+Feb	-14379
+▁complain	-14380
+OWN	-14381
+▁priorit	-14382
+▁Ferr	-14383
+ounding	-14384
+▁amateur	-14385
+▁lenders	-14386
+iop	-14387
+▁perpet	-14388
+▁Cincinnati	-14389
+▁diving	-14390
+▁Champion	-14391
+posed	-14392
+▁Allow	-14393
+▁Videos	-14394
+▁absorb	-14395
+▁Tournament	-14396
+▁advances	-14397
+▁cognitive	-14398
+▁Baseball	-14399
+▁metres	-14400
+▁IRS	-14401
+▁wre	-14402
+▁strictly	-14403
+▁Cruz	-14404
+istle	-14405
+▁pupils	-14406
+▁terrorist	-14407
+ourage	-14408
+▁uploaded	-14409
+▁blues	-14410
+▁blanket	-14411
+▁couch	-14412
+haw	-14413
+▁lecture	-14414
+▁Attend	-14415
+▁freeze	-14416
+▁racial	-14417
+▁dys	-14418
+▁Dinner	-14419
+▁Walt	-14420
+thening	-14421
+▁Excellence	-14422
+▁pads	-14423
+▁candle	-14424
+▁Branch	-14425
+▁anth	-14426
+▁migration	-14427
+▁Nokia	-14428
+▁Investig	-14429
+▁explicit	-14430
+▁damn	-14431
+▁summit	-14432
+▁IR	-14433
+▁inspir	-14434
+-)	-14435
+stan	-14436
+▁unwanted	-14437
+▁Jews	-14438
+▁Tiger	-14439
+▁coloring	-14440
+▁adjustment	-14441
+▁Scar	-14442
+▁influences	-14443
+▁Explorer	-14444
+▁inquiry	-14445
+▁revel	-14446
+gends	-14447
+▁Maintenance	-14448
+acre	-14449
+▁fertil	-14450
+▁fame	-14451
+▁firmly	-14452
+▁hiding	-14453
+ector	-14454
+▁palm	-14455
+▁counterpart	-14456
+▁Reuters	-14457
+▁dimension	-14458
+▁Kam	-14459
+fy	-14460
+▁starring	-14461
+lad	-14462
+▁Ax	-14463
+▁Rangers	-14464
+Thank	-14465
+▁dealt	-14466
+▁Someone	-14467
+▁examined	-14468
+▁spicy	-14469
+▁corresponding	-14470
+▁Mack	-14471
+▁disposal	-14472
+▁sleeves	-14473
+▁supermarket	-14474
+emor	-14475
+▁Client	-14476
+▁cherry	-14477
+▁reass	-14478
+▁attributes	-14479
+▁waterproof	-14480
+▁Nutrition	-14481
+▁Sty	-14482
+▁optical	-14483
+ellers	-14484
+GH	-14485
+▁gum	-14486
+▁wand	-14487
+inis	-14488
+▁dilig	-14489
+▁downloading	-14490
+have	-14491
+bye	-14492
+▁journalism	-14493
+▁joints	-14494
+▁Chase	-14495
+▁styling	-14496
+▁commod	-14497
+▁evaluated	-14498
+mends	-14499
+▁Ghost	-14500
+▁demonstrates	-14501
+▁Statistics	-14502
+reated	-14503
+▁Pos	-14504
+Ty	-14505
+▁monster	-14506
+▁participant	-14507
+▁spokesperson	-14508
+▁mechanisms	-14509
+▁TW	-14510
+▁Opening	-14511
+▁Measure	-14512
+ository	-14513
+▁reviewing	-14514
+▁Benn	-14515
+▁VAT	-14516
+▁bicycle	-14517
+▁Estab	-14518
+▁Hour	-14519
+▁Alexand	-14520
+▁fisher	-14521
+▁Honor	-14522
+▁Articles	-14523
+▁Mediterranean	-14524
+▁Transl	-14525
+rez	-14526
+▁seamless	-14527
+listed	-14528
+comm	-14529
+Ge	-14530
+▁Cultural	-14531
+▁outline	-14532
+irs	-14533
+▁deer	-14534
+▁Require	-14535
+▁Mi	-14536
+▁revised	-14537
+▁queen	-14538
+▁Cinem	-14539
+▁isolated	-14540
+▁wondered	-14541
+▁Nike	-14542
+▁Perm	-14543
+▁variation	-14544
+▁euro	-14545
+Hi	-14546
+▁Kindle	-14547
+camp	-14548
+▁spirits	-14549
+▁cryptocur	-14550
+cap	-14551
+rine	-14552
+apple	-14553
+▁playground	-14554
+▁Chan	-14555
+▁duplic	-14556
+▁Object	-14557
+▁Philip	-14558
+▁beverage	-14559
+▁complications	-14560
+▁performs	-14561
+▁keywords	-14562
+gered	-14563
+▁spices	-14564
+▁proceedings	-14565
+bank	-14566
+▁Gard	-14567
+opard	-14568
+▁bloggers	-14569
+▁pretend	-14570
+▁renovation	-14571
+▁RBI	-14572
+engu	-14573
+▁execute	-14574
+anners	-14575
+▁Motors	-14576
+▁segments	-14577
+▁march	-14578
+▁Leaf	-14579
+▁obstacles	-14580
+▁Juan	-14581
+▁dat	-14582
+jamin	-14583
+SF	-14584
+▁tones	-14585
+▁Loan	-14586
+direct	-14587
+▁alternate	-14588
+ername	-14589
+▁Yesterday	-14590
+▁enterprises	-14591
+hesis	-14592
+▁valley	-14593
+▁Wow	-14594
+▁Truth	-14595
+▁intuitive	-14596
+pop	-14597
+▁freshly	-14598
+▁executed	-14599
+keeping	-14600
+▁Jefferson	-14601
+▁Bristol	-14602
+lower	-14603
+▁roofing	-14604
+▁Lip	-14605
+▁parade	-14606
+▁messaging	-14607
+▁Guests	-14608
+uki	-14609
+▁sheer	-14610
+COM	-14611
+ggy	-14612
+ANCE	-14613
+amboo	-14614
+▁detected	-14615
+shirts	-14616
+▁BT	-14617
+▁modest	-14618
+▁sleeve	-14619
+▁sleek	-14620
+▁knit	-14621
+chron	-14622
+▁pipes	-14623
+▁rehabilitation	-14624
+▁desperate	-14625
+ometown	-14626
+▁perspectives	-14627
+orus	-14628
+story	-14629
+▁insulation	-14630
+▁Optim	-14631
+▁Giants	-14632
+▁sciences	-14633
+▁minerals	-14634
+▁illustrations	-14635
+▁unfor	-14636
+▁Planet	-14637
+BER	-14638
+▁Shaw	-14639
+▁consultants	-14640
+braska	-14641
+▁inexpensive	-14642
+▁enhancing	-14643
+▁icons	-14644
+▁resolved	-14645
+▁attachment	-14646
+▁stirring	-14647
+▁Niger	-14648
+boys	-14649
+▁replied	-14650
+▁Lanc	-14651
+▁proudly	-14652
+▁SMS	-14653
+▁Holland	-14654
+▁fate	-14655
+▁recreation	-14656
+IX	-14657
+▁syndrome	-14658
+rams	-14659
+▁Instit	-14660
+▁twitter	-14661
+▁mock	-14662
+▁Idaho	-14663
+▁deployed	-14664
+▁compromise	-14665
+▁lawmakers	-14666
+▁sunlight	-14667
+▁taxi	-14668
+▁Happ	-14669
+oodles	-14670
+DER	-14671
+▁Enh	-14672
+▁preliminary	-14673
+▁descriptions	-14674
+▁cinema	-14675
+▁bod	-14676
+▁therapeutic	-14677
+▁Plaza	-14678
+paid	-14679
+imity	-14680
+▁cancelled	-14681
+▁gown	-14682
+▁Watson	-14683
+nan	-14684
+▁Quant	-14685
+oples	-14686
+▁hobby	-14687
+▁nerve	-14688
+▁ankle	-14689
+▁gameplay	-14690
+second	-14691
+▁finances	-14692
+▁responding	-14693
+▁Ts	-14694
+allow	-14695
+▁economics	-14696
+▁recognised	-14697
+▁Edinburgh	-14698
+▁perfection	-14699
+struction	-14700
+▁elabor	-14701
+▁epid	-14702
+▁Canon	-14703
+▁Root	-14704
+▁rape	-14705
+▁violations	-14706
+▁Turner	-14707
+poke	-14708
+▁tactics	-14709
+▁spine	-14710
+▁ritual	-14711
+▁adverse	-14712
+▁Assessment	-14713
+aternity	-14714
+▁bears	-14715
+▁MT	-14716
+mill	-14717
+▁belongs	-14718
+▁breathe	-14719
+▁outreach	-14720
+▁Brazilian	-14721
+two	-14722
+owski	-14723
+▁advisory	-14724
+sterdam	-14725
+▁Oliver	-14726
+▁masks	-14727
+▁trades	-14728
+▁performers	-14729
+▁inflammation	-14730
+▁stressful	-14731
+▁Kum	-14732
+uez	-14733
+▁Fans	-14734
+▁Flight	-14735
+▁Reader	-14736
+▁touching	-14737
+▁villages	-14738
+▁drill	-14739
+▁refuse	-14740
+▁Score	-14741
+▁Rogers	-14742
+▁cricket	-14743
+▁Milwaukee	-14744
+▁decides	-14745
+▁Wes	-14746
+▁Shar	-14747
+▁puzz	-14748
+uri	-14749
+▁installations	-14750
+▁barn	-14751
+idy	-14752
+shaped	-14753
+▁urgent	-14754
+▁Coordinator	-14755
+▁horrible	-14756
+▁alien	-14757
+tees	-14758
+▁investigations	-14759
+available	-14760
+▁brake	-14761
+▁washed	-14762
+▁organizational	-14763
+ISH	-14764
+powered	-14765
+▁Swim	-14766
+lasting	-14767
+▁penalties	-14768
+▁Bike	-14769
+▁raises	-14770
+urred	-14771
+▁bitter	-14772
+▁shoppers	-14773
+▁encompass	-14774
+osity	-14775
+esity	-14776
+▁cables	-14777
+▁lever	-14778
+▁comprised	-14779
+▁Approx	-14780
+▁Required	-14781
+▁pp	-14782
+▁depict	-14783
+▁tan	-14784
+▁median	-14785
+▁Chev	-14786
+▁rentals	-14787
+▁tanks	-14788
+ools	-14789
+▁Beng	-14790
+▁Wells	-14791
+▁Colon	-14792
+▁Internal	-14793
+▁nan	-14794
+▁educated	-14795
+▁Obviously	-14796
+▁Cemetery	-14797
+▁mud	-14798
+atical	-14799
+▁pale	-14800
+▁Similar	-14801
+▁binding	-14802
+▁locks	-14803
+▁Warrant	-14804
+Mod	-14805
+▁varying	-14806
+▁mi	-14807
+▁Canyon	-14808
+Most	-14809
+▁activists	-14810
+Ke	-14811
+▁Cheese	-14812
+ulin	-14813
+▁Pizza	-14814
+▁consuming	-14815
+▁abundance	-14816
+▁neighborhoods	-14817
+▁galleries	-14818
+▁decreased	-14819
+▁beings	-14820
+▁throat	-14821
+▁bonuses	-14822
+▁mushrooms	-14823
+▁Creating	-14824
+▁Quarter	-14825
+▁Ald	-14826
+▁Harrison	-14827
+bone	-14828
+▁Luckily	-14829
+nings	-14830
+▁Bryan	-14831
+uate	-14832
+▁Pete	-14833
+▁generic	-14834
+▁deleted	-14835
+▁ounces	-14836
+▁circulation	-14837
+▁Sacram	-14838
+▁Arsenal	-14839
+▁wages	-14840
+▁Wide	-14841
+▁Factory	-14842
+▁acknowledged	-14843
+agger	-14844
+▁immigrants	-14845
+El	-14846
+▁gri	-14847
+▁intr	-14848
+▁bou	-14849
+▁Firm	-14850
+▁afterwards	-14851
+▁Eb	-14852
+▁corporation	-14853
+▁donors	-14854
+▁Amanda	-14855
+▁stove	-14856
+leton	-14857
+arse	-14858
+▁delightful	-14859
+▁Membership	-14860
+▁Arn	-14861
+▁Edwards	-14862
+arity	-14863
+arnish	-14864
+▁witnessed	-14865
+million	-14866
+▁flowing	-14867
+▁Notice	-14868
+aky	-14869
+ettes	-14870
+▁Mis	-14871
+▁associations	-14872
+▁niche	-14873
+▁casting	-14874
+than	-14875
+▁belly	-14876
+▁Vermont	-14877
+▁unprecedented	-14878
+▁Conservation	-14879
+reath	-14880
+▁Twin	-14881
+icone	-14882
+▁Harm	-14883
+▁google	-14884
+bane	-14885
+▁Okay	-14886
+▁Margaret	-14887
+▁Pretty	-14888
+▁spat	-14889
+▁Beer	-14890
+▁quil	-14891
+▁Hood	-14892
+▁assure	-14893
+▁Ja	-14894
+orie	-14895
+USA	-14896
+itants	-14897
+ampagne	-14898
+▁rivers	-14899
+▁Pearl	-14900
+▁rook	-14901
+uscript	-14902
+▁nutritional	-14903
+▁Coup	-14904
+ourmet	-14905
+▁collaborate	-14906
+Top	-14907
+▁lending	-14908
+▁ES	-14909
+BL	-14910
+MM	-14911
+▁condem	-14912
+jack	-14913
+anga	-14914
+▁dated	-14915
+▁DA	-14916
+cost	-14917
+▁RF	-14918
+filled	-14919
+▁offline	-14920
+▁remarks	-14921
+▁frustrating	-14922
+umatic	-14923
+▁combinations	-14924
+▁ceramic	-14925
+▁Et	-14926
+ivest	-14927
+▁Poll	-14928
+▁Ze	-14929
+▁Heights	-14930
+▁forests	-14931
+▁Frequ	-14932
+▁Abb	-14933
+▁Cyber	-14934
+impse	-14935
+▁Against	-14936
+▁assisting	-14937
+rating	-14938
+retion	-14939
+▁Educational	-14940
+▁ACT	-14941
+▁pointing	-14942
+▁scholarships	-14943
+▁Blake	-14944
+▁christ	-14945
+uba	-14946
+▁intersection	-14947
+▁schemes	-14948
+▁happily	-14949
+▁awkward	-14950
+High	-14951
+▁nowhere	-14952
+uxe	-14953
+▁rhythm	-14954
+▁demonstration	-14955
+urable	-14956
+gow	-14957
+▁Favorite	-14958
+loe	-14959
+▁thickness	-14960
+▁festive	-14961
+▁proprietary	-14962
+▁headlines	-14963
+▁invites	-14964
+▁opener	-14965
+plan	-14966
+▁Raw	-14967
+▁catering	-14968
+▁minority	-14969
+ussels	-14970
+▁wholesale	-14971
+patient	-14972
+occ	-14973
+▁Plastic	-14974
+▁behaviors	-14975
+▁Chron	-14976
+Any	-14977
+uda	-14978
+▁switching	-14979
+▁convince	-14980
+▁muff	-14981
+▁Featuring	-14982
+ymph	-14983
+▁expend	-14984
+▁yogurt	-14985
+▁banner	-14986
+Web	-14987
+▁Train	-14988
+▁labour	-14989
+▁Reed	-14990
+▁Draft	-14991
+▁Heaven	-14992
+▁seminar	-14993
+OK	-14994
+▁prohibited	-14995
+▁phenomenon	-14996
+comfort	-14997
+yon	-14998
+FOR	-14999
+▁ballot	-15000
+▁infant	-15001
+▁Accept	-15002
+▁sophomore	-15003
+▁VR	-15004
+▁nic	-15005
+▁culinary	-15006
+▁cartoon	-15007
+▁ugly	-15008
+▁particles	-15009
+fu	-15010
+▁stairs	-15011
+▁pillow	-15012
+▁scheduling	-15013
+▁monetary	-15014
+▁decorating	-15015
+▁suspects	-15016
+▁Wait	-15017
+▁burned	-15018
+▁subsequently	-15019
+▁ridiculous	-15020
+▁disrupt	-15021
+▁thereby	-15022
+▁noting	-15023
+▁Owner	-15024
+▁hottest	-15025
+▁sturdy	-15026
+▁Ticket	-15027
+▁worries	-15028
+▁controlling	-15029
+▁projected	-15030
+▁acute	-15031
+Red	-15032
+▁accordingly	-15033
+▁toe	-15034
+▁Fantasy	-15035
+▁investigators	-15036
+▁protests	-15037
+▁antique	-15038
+▁Maj	-15039
+▁unh	-15040
+▁Projects	-15041
+▁Thread	-15042
+oped	-15043
+▁influential	-15044
+▁energ	-15045
+▁resemb	-15046
+UP	-15047
+kn	-15048
+▁utter	-15049
+iosity	-15050
+▁consisting	-15051
+▁Architecture	-15052
+▁dynamics	-15053
+▁partially	-15054
+▁Political	-15055
+▁GL	-15056
+▁Lan	-15057
+▁embark	-15058
+▁Township	-15059
+flower	-15060
+▁impacted	-15061
+▁environmentally	-15062
+March	-15063
+▁transfers	-15064
+▁Understanding	-15065
+▁kicks	-15066
+▁Fabric	-15067
+▁defending	-15068
+mega	-15069
+logy	-15070
+▁Stephan	-15071
+sa	-15072
+▁playoffs	-15073
+▁Employment	-15074
+▁Colomb	-15075
+▁lam	-15076
+rapes	-15077
+psy	-15078
+rene	-15079
+▁congrat	-15080
+▁Million	-15081
+▁underway	-15082
+▁needle	-15083
+▁Mental	-15084
+▁Fold	-15085
+▁fever	-15086
+▁Initiative	-15087
+▁recommends	-15088
+▁Occup	-15089
+block	-15090
+▁proteins	-15091
+▁bom	-15092
+▁resign	-15093
+▁webinar	-15094
+▁qualifications	-15095
+aways	-15096
+ipher	-15097
+▁congreg	-15098
+▁Nag	-15099
+▁Neck	-15100
+▁supervision	-15101
+▁polish	-15102
+..."	-15103
+▁Orders	-15104
+▁eBay	-15105
+▁Session	-15106
+▁accredited	-15107
+▁GRE	-15108
+▁UFC	-15109
+▁Analytics	-15110
+wealth	-15111
+▁CF	-15112
+becca	-15113
+▁Rou	-15114
+▁harness	-15115
+▁Milan	-15116
+third	-15117
+▁certificates	-15118
+rant	-15119
+▁RSS	-15120
+▁Ric	-15121
+▁recordings	-15122
+ainted	-15123
+▁Memory	-15124
+▁tragedy	-15125
+▁sheep	-15126
+▁Phillips	-15127
+▁Unless	-15128
+mile	-15129
+▁Democrat	-15130
+▁Tu	-15131
+▁sentenced	-15132
+▁notifications	-15133
+Health	-15134
+▁sensitivity	-15135
+▁sentiment	-15136
+▁indication	-15137
+▁ignored	-15138
+▁Fest	-15139
+▁Celt	-15140
+▁confusing	-15141
+▁Apps	-15142
+▁therapist	-15143
+ontin	-15144
+▁Truck	-15145
+▁Helen	-15146
+ilant	-15147
+century	-15148
+▁PRO	-15149
+▁awful	-15150
+▁celebrations	-15151
+▁Historic	-15152
+▁databases	-15153
+▁rotation	-15154
+▁Scholarship	-15155
+▁Calendar	-15156
+▁Bonus	-15157
+usalem	-15158
+▁recept	-15159
+▁lottery	-15160
+▁foil	-15161
+▁hed	-15162
+▁verification	-15163
+▁Portugal	-15164
+chnology	-15165
+getic	-15166
+▁laughing	-15167
+▁timeless	-15168
+▁blessing	-15169
+April	-15170
+▁interviewed	-15171
+scape	-15172
+▁puppy	-15173
+▁Traffic	-15174
+▁Certain	-15175
+▁aug	-15176
+▁Sox	-15177
+▁freezer	-15178
+▁eaten	-15179
+ARS	-15180
+June	-15181
+▁Earn	-15182
+▁cave	-15183
+▁lug	-15184
+▁Datab	-15185
+Spe	-15186
+▁Rescue	-15187
+▁Makes	-15188
+claimer	-15189
+▁Nebraska	-15190
+bag	-15191
+osures	-15192
+utt	-15193
+▁hilarious	-15194
+▁Walter	-15195
+▁Southeast	-15196
+▁advocates	-15197
+▁Phase	-15198
+▁picnic	-15199
+▁acids	-15200
+fighters	-15201
+▁lender	-15202
+aque	-15203
+▁offset	-15204
+▁Nevertheless	-15205
+▁financially	-15206
+▁Kle	-15207
+▁Chemical	-15208
+Acc	-15209
+▁enrich	-15210
+▁promo	-15211
+arre	-15212
+???	-15213
+▁LCD	-15214
+▁terrific	-15215
+▁Experts	-15216
+▁rounded	-15217
+▁Inj	-15218
+▁museums	-15219
+▁Benjamin	-15220
+▁researcher	-15221
+▁assessments	-15222
+▁ethics	-15223
+La	-15224
+▁indicator	-15225
+▁Vik	-15226
+▁sequel	-15227
+▁evenly	-15228
+▁frustrated	-15229
+▁bang	-15230
+▁publishers	-15231
+changing	-15232
+▁glimpse	-15233
+▁Nicole	-15234
+▁Views	-15235
+▁Delaware	-15236
+▁briefly	-15237
+▁guarantees	-15238
+▁tin	-15239
+▁careg	-15240
+▁Massage	-15241
+▁roasted	-15242
+MO	-15243
+tops	-15244
+Two	-15245
+▁beats	-15246
+▁battles	-15247
+▁Biden	-15248
+icaid	-15249
+▁tricky	-15250
+igma	-15251
+▁Programme	-15252
+▁pursuit	-15253
+erie	-15254
+etta	-15255
+▁evolving	-15256
+hett	-15257
+▁exchanges	-15258
+▁crunch	-15259
+▁Stu	-15260
+▁playoff	-15261
+▁Dell	-15262
+▁quad	-15263
+▁Puerto	-15264
+▁costumes	-15265
+etics	-15266
+▁rumors	-15267
+▁disadvant	-15268
+▁ric	-15269
+▁innovations	-15270
+▁polished	-15271
+▁coupled	-15272
+▁intric	-15273
+▁Bun	-15274
+▁sights	-15275
+brook	-15276
+▁Mountains	-15277
+▁trademarks	-15278
+▁downt	-15279
+▁comics	-15280
+pered	-15281
+▁nap	-15282
+▁themed	-15283
+▁Oz	-15284
+▁Glas	-15285
+▁formerly	-15286
+requ	-15287
+▁dign	-15288
+▁Teachers	-15289
+▁bucket	-15290
+▁Ko	-15291
+▁EVER	-15292
+West	-15293
+avan	-15294
+▁Defe	-15295
+▁Garc	-15296
+Sept	-15297
+▁Alberta	-15298
+▁Walking	-15299
+umbs	-15300
+vance	-15301
+NE	-15302
+▁dominated	-15303
+▁Haven	-15304
+▁assumed	-15305
+eu	-15306
+Bus	-15307
+vine	-15308
+▁Dual	-15309
+MD	-15310
+▁Response	-15311
+▁Ug	-15312
+▁AU	-15313
+▁finely	-15314
+▁Expect	-15315
+storm	-15316
+▁Extract	-15317
+Care	-15318
+▁Publisher	-15319
+wiches	-15320
+iquid	-15321
+▁hind	-15322
+▁sou	-15323
+▁stating	-15324
+▁Guardian	-15325
+▁Agreement	-15326
+OLD	-15327
+▁dense	-15328
+▁prompted	-15329
+▁Gabri	-15330
+▁specialize	-15331
+▁Traditional	-15332
+▁Il	-15333
+▁holy	-15334
+▁Abu	-15335
+parents	-15336
+▁necklace	-15337
+▁Testing	-15338
+▁Amsterdam	-15339
+insula	-15340
+▁machinery	-15341
+▁positioned	-15342
+hma	-15343
+▁retention	-15344
+food	-15345
+prom	-15346
+▁Bund	-15347
+▁Flore	-15348
+▁dominant	-15349
+▁Engineer	-15350
+Stud	-15351
+Will	-15352
+▁whis	-15353
+▁fragrance	-15354
+usters	-15355
+▁Mortgage	-15356
+efficient	-15357
+▁sailing	-15358
+▁perf	-15359
+▁Otherwise	-15360
+ivia	-15361
+▁prol	-15362
+▁grabbed	-15363
+▁Dogs	-15364
+also	-15365
+▁dot	-15366
+▁nour	-15367
+dr	-15368
+▁pumps	-15369
+▁securely	-15370
+▁Visa	-15371
+▁gardening	-15372
+▁antioxid	-15373
+▁ample	-15374
+▁interf	-15375
+▁dece	-15376
+▁saus	-15377
+rencies	-15378
+▁investigated	-15379
+▁Kan	-15380
+▁mph	-15381
+role	-15382
+▁Jama	-15383
+▁asleep	-15384
+unks	-15385
+▁polls	-15386
+▁Crew	-15387
+▁peanut	-15388
+▁jaw	-15389
+▁habitat	-15390
+▁Jet	-15391
+▁contacting	-15392
+▁Stanford	-15393
+inkles	-15394
+inse	-15395
+ammar	-15396
+Pre	-15397
+▁cav	-15398
+▁gauge	-15399
+▁Kil	-15400
+▁sew	-15401
+iblings	-15402
+▁Chapel	-15403
+▁Neg	-15404
+▁NW	-15405
+▁Vel	-15406
+aba	-15407
+▁Same	-15408
+▁Consulting	-15409
+▁happier	-15410
+▁extends	-15411
+▁rational	-15412
+▁knees	-15413
+gans	-15414
+▁biology	-15415
+▁cope	-15416
+▁Mode	-15417
+▁waited	-15418
+cond	-15419
+▁bronze	-15420
+▁gamers	-15421
+▁diver	-15422
+▁Victorian	-15423
+▁tens	-15424
+▁circular	-15425
+▁Message	-15426
+▁NV	-15427
+rien	-15428
+arett	-15429
+▁Hero	-15430
+washer	-15431
+▁Rodrig	-15432
+crafted	-15433
+keys	-15434
+▁PlayStation	-15435
+▁Eval	-15436
+▁Outside	-15437
+▁Polish	-15438
+asha	-15439
+▁Secure	-15440
+▁midst	-15441
+▁Asked	-15442
+▁quicker	-15443
+▁kidney	-15444
+▁reck	-15445
+▁Whole	-15446
+.),	-15447
+▁HS	-15448
+▁vet	-15449
+▁Superv	-15450
+mount	-15451
+▁notion	-15452
+▁Recruit	-15453
+ctuary	-15454
+▁bachelor	-15455
+ocratic	-15456
+▁unclear	-15457
+irk	-15458
+oodle	-15459
+▁earl	-15460
+▁cupc	-15461
+▁threatening	-15462
+▁devastating	-15463
+▁preval	-15464
+▁pdf	-15465
+▁SAP	-15466
+attering	-15467
+▁quart	-15468
+▁reign	-15469
+▁Banks	-15470
+▁ours	-15471
+▁shiny	-15472
+▁poems	-15473
+▁CE	-15474
+▁lod	-15475
+▁umb	-15476
+gger	-15477
+▁gaps	-15478
+▁Raven	-15479
+▁Dust	-15480
+AVE	-15481
+▁panic	-15482
+payers	-15483
+▁EUR	-15484
+▁Vine	-15485
+Min	-15486
+▁Milk	-15487
+ere	-15488
+▁crowds	-15489
+▁Ub	-15490
+▁Resear	-15491
+▁Television	-15492
+▁fixtures	-15493
+akespe	-15494
+▁equality	-15495
+▁decorations	-15496
+▁scoop	-15497
+eem	-15498
+▁Founder	-15499
+▁Claim	-15500
+▁partnered	-15501
+Cor	-15502
+▁lengthy	-15503
+ieval	-15504
+▁sincere	-15505
+▁cel	-15506
+inspired	-15507
+▁Exc	-15508
+▁fatigue	-15509
+▁substances	-15510
+ania	-15511
+ghai	-15512
+▁NSW	-15513
+▁FE	-15514
+▁youngest	-15515
+▁beads	-15516
+ennial	-15517
+▁teenager	-15518
+▁auton	-15519
+▁kinda	-15520
+▁moder	-15521
+▁EPA	-15522
+▁Lucas	-15523
+▁translated	-15524
+oulder	-15525
+▁Dol	-15526
+▁Carbon	-15527
+▁Publication	-15528
+ema	-15529
+▁Aub	-15530
+▁WHAT	-15531
+▁tweets	-15532
+▁gratitude	-15533
+▁proportion	-15534
+▁Samuel	-15535
+▁clinics	-15536
+▁listened	-15537
+▁Karl	-15538
+▁administrators	-15539
+▁optimize	-15540
+▁hats	-15541
+▁lifted	-15542
+▁crying	-15543
+▁separation	-15544
+▁deposits	-15545
+▁herb	-15546
+ibles	-15547
+avender	-15548
+leen	-15549
+▁angel	-15550
+▁surgeon	-15551
+▁learners	-15552
+alg	-15553
+▁Cel	-15554
+glass	-15555
+▁touring	-15556
+▁Jerusalem	-15557
+▁Leban	-15558
+▁Parad	-15559
+▁traders	-15560
+▁acclaimed	-15561
+▁lakes	-15562
+oubted	-15563
+▁Battery	-15564
+erness	-15565
+asis	-15566
+▁Ana	-15567
+▁extensions	-15568
+▁refined	-15569
+▁seafood	-15570
+▁tv	-15571
+▁occupied	-15572
+▁ip	-15573
+ucked	-15574
+phis	-15575
+▁Collabor	-15576
+▁Elite	-15577
+▁Founded	-15578
+▁chaos	-15579
+▁automobile	-15580
+▁describing	-15581
+▁metals	-15582
+▁approve	-15583
+▁disciplines	-15584
+unted	-15585
+▁trio	-15586
+▁deduct	-15587
+▁feather	-15588
+▁Bangl	-15589
+▁practically	-15590
+▁Trading	-15591
+▁Zero	-15592
+▁Solution	-15593
+▁pap	-15594
+▁obligations	-15595
+Wed	-15596
+ested	-15597
+▁grasp	-15598
+▁Pric	-15599
+▁Writer	-15600
+OCK	-15601
+▁linear	-15602
+▁dimin	-15603
+▁seasoned	-15604
+▁XL	-15605
+eton	-15606
+Wind	-15607
+▁Ot	-15608
+▁cares	-15609
+▁notify	-15610
+▁Gem	-15611
+▁sunshine	-15612
+▁Tourism	-15613
+▁Bears	-15614
+▁pencil	-15615
+aton	-15616
+▁Shah	-15617
+▁cryst	-15618
+▁Eco	-15619
+▁Reference	-15620
+BE	-15621
+▁honour	-15622
+▁Kend	-15623
+.).	-15624
+Hey	-15625
+▁lively	-15626
+Monday	-15627
+▁Designs	-15628
+▁MAN	-15629
+▁temporarily	-15630
+GC	-15631
+▁neglig	-15632
+▁Denmark	-15633
+▁spotlight	-15634
+resa	-15635
+▁Rule	-15636
+hal	-15637
+▁tenants	-15638
+formed	-15639
+▁bik	-15640
+▁cousin	-15641
+▁submissions	-15642
+▁Summary	-15643
+▁commands	-15644
+▁meth	-15645
+▁quarters	-15646
+▁OP	-15647
+▁nomination	-15648
+▁pix	-15649
+otional	-15650
+ohyd	-15651
+intendent	-15652
+▁fet	-15653
+imated	-15654
+▁mum	-15655
+▁accessory	-15656
+width	-15657
+▁mamm	-15658
+▁sentences	-15659
+ois	-15660
+ahl	-15661
+▁Complex	-15662
+▁Indians	-15663
+▁aspir	-15664
+when	-15665
+▁Edit	-15666
+alone	-15667
+▁Participants	-15668
+▁BU	-15669
+▁Medicaid	-15670
+▁Cu	-15671
+▁testimony	-15672
+▁enthusiastic	-15673
+▁teenage	-15674
+▁instrumental	-15675
+▁variables	-15676
+▁selections	-15677
+▁algorithm	-15678
+▁tweeted	-15679
+somet	-15680
+▁coordination	-15681
+▁assembled	-15682
+▁flooding	-15683
+▁Southwest	-15684
+▁lasts	-15685
+zel	-15686
+▁lazy	-15687
+▁Electrical	-15688
+think	-15689
+▁terrain	-15690
+phant	-15691
+▁Vehicle	-15692
+▁Weekend	-15693
+ampton	-15694
+▁palette	-15695
+▁Hook	-15696
+▁explored	-15697
+▁Jar	-15698
+▁Discovery	-15699
+▁Moz	-15700
+▁Industries	-15701
+degree	-15702
+▁beside	-15703
+▁enrollment	-15704
+▁sculpture	-15705
+▁peppers	-15706
+Cent	-15707
+akespeare	-15708
+heastern	-15709
+▁comparable	-15710
+jud	-15711
+say	-15712
+houses	-15713
+▁pier	-15714
+▁encountered	-15715
+▁ninth	-15716
+▁UT	-15717
+▁Saints	-15718
+quare	-15719
+try	-15720
+HR	-15721
+▁terrace	-15722
+▁Bobby	-15723
+▁frustration	-15724
+▁Dian	-15725
+▁Ruth	-15726
+▁accessibility	-15727
+keley	-15728
+▁Hence	-15729
+▁zoom	-15730
+--------	-15731
+▁Alz	-15732
+▁Liz	-15733
+▁threads	-15734
+▁scandal	-15735
+▁Caf	-15736
+show	-15737
+lit	-15738
+▁Orchestra	-15739
+acts	-15740
+▁Newcastle	-15741
+▁decoration	-15742
+weg	-15743
+▁disclosure	-15744
+▁Keeping	-15745
+▁HVAC	-15746
+▁redes	-15747
+▁Carlos	-15748
+▁scientist	-15749
+▁pressed	-15750
+▁Created	-15751
+▁Nom	-15752
+▁assessed	-15753
+fix	-15754
+Play	-15755
+GL	-15756
+▁unde	-15757
+▁giveaway	-15758
+IDE	-15759
+▁elevated	-15760
+▁Chile	-15761
+▁fights	-15762
+▁Queensland	-15763
+Link	-15764
+▁Pattern	-15765
+▁beverages	-15766
+▁Above	-15767
+gettable	-15768
+▁coated	-15769
+version	-15770
+artha	-15771
+lasses	-15772
+▁pinch	-15773
+▁Vincent	-15774
+▁creations	-15775
+arded	-15776
+▁Exerc	-15777
+ogg	-15778
+issan	-15779
+▁Installation	-15780
+pdf	-15781
+▁laying	-15782
+▁Interested	-15783
+▁Spread	-15784
+▁controversy	-15785
+▁Carn	-15786
+orig	-15787
+▁collapse	-15788
+▁Garage	-15789
+▁zomb	-15790
+iaries	-15791
+▁harmon	-15792
+issance	-15793
+iovascular	-15794
+▁Fried	-15795
+Ass	-15796
+▁renewed	-15797
+oubtedly	-15798
+▁xxx	-15799
+HP	-15800
+▁Task	-15801
+▁wellbeing	-15802
+▁castle	-15803
+▁foss	-15804
+▁Behav	-15805
+▁immun	-15806
+▁showers	-15807
+ulously	-15808
+▁Laboratory	-15809
+▁bundle	-15810
+▁ONLY	-15811
+▁throws	-15812
+▁rope	-15813
+piring	-15814
+▁injection	-15815
+▁FROM	-15816
+▁journals	-15817
+▁deny	-15818
+▁undergo	-15819
+▁shareholders	-15820
+enez	-15821
+▁Zoom	-15822
+▁Austria	-15823
+▁permanently	-15824
+▁Julia	-15825
+▁Sierra	-15826
+▁kills	-15827
+▁Cham	-15828
+itime	-15829
+▁Wend	-15830
+▁sunset	-15831
+▁Operating	-15832
+Trans	-15833
+atches	-15834
+ATED	-15835
+▁partly	-15836
+ourcing	-15837
+▁unions	-15838
+▁repay	-15839
+atories	-15840
+▁endeav	-15841
+▁outfits	-15842
+▁Partnership	-15843
+▁Bent	-15844
+▁towels	-15845
+▁incentives	-15846
+ampoo	-15847
+▁Argentina	-15848
+aturated	-15849
+▁mes	-15850
+uced	-15851
+▁memor	-15852
+▁additions	-15853
+▁comfortably	-15854
+▁Male	-15855
+▁chore	-15856
+Big	-15857
+▁Rebecca	-15858
+isha	-15859
+▁veggies	-15860
+▁fundament	-15861
+▁lung	-15862
+▁enquir	-15863
+▁static	-15864
+yr	-15865
+▁flesh	-15866
+▁Photosh	-15867
+istically	-15868
+▁Alpha	-15869
+▁dismissed	-15870
+▁constitu	-15871
+kh	-15872
+▁Privacy	-15873
+▁phrases	-15874
+▁Chuck	-15875
+▁SR	-15876
+▁Dale	-15877
+▁fortune	-15878
+▁customs	-15879
+station	-15880
+▁Palestinian	-15881
+path	-15882
+cra	-15883
+▁answering	-15884
+▁Patients	-15885
+▁Recreation	-15886
+alph	-15887
+stown	-15888
+▁Brooks	-15889
+▁Blvd	-15890
+▁QU	-15891
+▁Statement	-15892
+fficial	-15893
+▁chase	-15894
+▁Criminal	-15895
+▁Flower	-15896
+mare	-15897
+▁crushed	-15898
+akery	-15899
+▁diagnostic	-15900
+▁shifts	-15901
+▁scenery	-15902
+Where	-15903
+Many	-15904
+▁Eight	-15905
+▁rivals	-15906
+▁Zoo	-15907
+▁backpack	-15908
+raul	-15909
+▁enjoyment	-15910
+WE	-15911
+urf	-15912
+▁earliest	-15913
+▁boutique	-15914
+▁hardest	-15915
+web	-15916
+▁outlined	-15917
+▁correction	-15918
+▁Profession	-15919
+▁CPU	-15920
+▁adaptation	-15921
+▁Assistance	-15922
+ullivan	-15923
+▁Foster	-15924
+five	-15925
+▁TD	-15926
+▁offshore	-15927
+▁attain	-15928
+▁inquiries	-15929
+▁grape	-15930
+ikh	-15931
+▁metrics	-15932
+▁beers	-15933
+AME	-15934
+itle	-15935
+▁flagship	-15936
+▁wonders	-15937
+Act	-15938
+▁Miles	-15939
+▁discomfort	-15940
+Max	-15941
+▁landscapes	-15942
+▁breach	-15943
+▁Capitol	-15944
+▁Ideal	-15945
+▁Bradley	-15946
+▁succ	-15947
+uffs	-15948
+Pres	-15949
+▁balcony	-15950
+isle	-15951
+▁Cities	-15952
+▁Gonz	-15953
+▁Tall	-15954
+▁perceived	-15955
+▁Scout	-15956
+▁extrem	-15957
+▁varies	-15958
+▁UI	-15959
+▁Sterling	-15960
+▁Warm	-15961
+▁MBA	-15962
+▁Bald	-15963
+▁paperwork	-15964
+emen	-15965
+entry	-15966
+▁sliding	-15967
+▁documented	-15968
+Hello	-15969
+▁extending	-15970
+▁jewellery	-15971
+▁RM	-15972
+▁vibe	-15973
+frame	-15974
+▁payroll	-15975
+▁teenagers	-15976
+cience	-15977
+curement	-15978
+▁Bloomberg	-15979
+▁Soccer	-15980
+▁Sor	-15981
+▁Wake	-15982
+▁highlighting	-15983
+▁Rib	-15984
+▁doubles	-15985
+▁observations	-15986
+▁fairy	-15987
+▁consumed	-15988
+AK	-15989
+▁Ghana	-15990
+seeing	-15991
+▁earrings	-15992
+▁Lynn	-15993
+▁tunnel	-15994
+▁Unique	-15995
+▁Warranty	-15996
+▁literacy	-15997
+Have	-15998
+▁porch	-15999
+focus	-16000
+▁boom	-16001
+▁thoughtful	-16002
+▁Contemporary	-16003
+▁revealing	-16004
+▁Lights	-16005
+▁cholesterol	-16006
+xual	-16007
+▁acrylic	-16008
+▁overhead	-16009
+▁Cer	-16010
+▁scrub	-16011
+▁grief	-16012
+pad	-16013
+asma	-16014
+▁traditionally	-16015
+▁marathon	-16016
+▁clearance	-16017
+Love	-16018
+▁blocking	-16019
+phew	-16020
+)(	-16021
+▁Lemon	-16022
+▁Entry	-16023
+▁Prest	-16024
+neapolis	-16025
+▁viable	-16026
+▁drying	-16027
+▁females	-16028
+▁Submit	-16029
+▁billing	-16030
+▁hygiene	-16031
+▁eligibility	-16032
+ifi	-16033
+▁dread	-16034
+map	-16035
+eking	-16036
+▁Laser	-16037
+▁searched	-16038
+▁	-16039
+e	-16040
+t	-16041
+a	-16042
+o	-16043
+i	-16044
+n	-16045
+s	-16046
+r	-16047
+h	-16048
+l	-16049
+d	-16050
+c	-16051
+u	-16052
+m	-16053
+p	-16054
+g	-16055
+f	-16056
+y	-16057
+w	-16058
+b	-16059
+.	-16060
+v	-16061
+,	-16062
+k	-16063
+T	-16064
+S	-16065
+I	-16066
+A	-16067
+-	-16068
+C	-16069
+0	-16070
+M	-16071
+1	-16072
+P	-16073
+x	-16074
+B	-16075
+2	-16076
+W	-16077
+D	-16078
+R	-16079
+E	-16080
+H	-16081
+F	-16082
+’	-16083
+L	-16084
+N	-16085
+O	-16086
+:	-16087
+'	-16088
+G	-16089
+j	-16090
+)	-16091
+(	-16092
+z	-16093
+3	-16094
+5	-16095
+q	-16096
+4	-16097
+U	-16098
+"	-16099
+9	-16100
+J	-16101
+8	-16102
+6	-16103
+V	-16104
+Y	-16105
+K	-16106
+|	-16107
+7	-16108
+!	-16109
+/	-16110
+“	-16111
+”	-16112
+?	-16113
+–	-16114
+;	-16115
+&	-16116
+$	-16117
+Q	-16118
+%	-16119
+—	-16120
+X	-16121
+Z	-16122
+*	-16123
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/quinary_seed1337.txt b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/quinary_seed1337.txt
new file mode 100644
index 0000000000..38ade8fc94
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/quinary_seed1337.txt
@@ -0,0 +1,1689 @@
+"Quinary training script for OpenAI's Parameter Golf Challenge. Based on ternary submission by Ciprian-Florin Ifrim (24 March 2026). Quinary: {-2,-1,0,+1,+2} with base-5 packing (3 quins/byte = 2.667 bpw vs ternary 1.6 bpw)."
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import tempfile
+import time
+import lzma
+from pathlib import Path
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+from flash_attn_interface import flash_attn_func
+
+def _e(k, d, t=str):
+    v = os.environ.get(k, str(d))
+    if t == bool: return bool(int(v))
+    return t(v)
+
+class Hyperparameters:
+    # Defaults below match the canonical SP16384 quinary submission config —
+    # i.e. exactly what `run.sh` passes through to torchrun. A bare
+    # `torchrun --standalone --nproc_per_node=8 train_gpt.py` (no env vars)
+    # therefore reproduces the submission. `run.sh` is still the
+    # documentation-of-record for the canonical config; these defaults are
+    # mirrored from it so the two never silently disagree.
+    data_path = _e("DATA_PATH", "./data/canonical/datasets/fineweb10B_sp16384")
+    # [0-9] prefix matches only NNNNNN-suffixed shard files, ignoring any
+    # sibling parallel-array files that might share the prefix.
+    train_files = os.path.join(data_path, "fineweb_train_[0-9]*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_[0-9]*.bin")
+    tokenizer_path = _e("TOKENIZER_PATH", "./data/canonical/tokenizers/fineweb_16384_bpe.model")
+    run_id = os.environ.get("RUN_ID", f"run_{int(time.time())}")
+    seed = _e("SEED", 42, int)
+    compile_mode = _e("COMPILE_MODE", "default")
+    val_batch_size = _e("VAL_BATCH_SIZE", 524288, int)
+    val_loss_every = _e("VAL_LOSS_EVERY", 0, int)  # 0 = no in-training val
+    train_log_every = _e("TRAIN_LOG_EVERY", 1000, int)
+    iterations = _e("ITERATIONS", 10000, int)
+    warmdown_fraction = _e("WARMDOWN_FRACTION", 0.2, float)
+    min_lr = _e("MIN_LR", 0.0, float)  # floor on the LR multiplier (fraction of base LR)
+    warmup_steps = _e("WARMUP_STEPS", 5, int)
+    train_batch_tokens = _e("TRAIN_BATCH_TOKENS", 524288, int)
+    train_seq_len = _e("TRAIN_SEQ_LEN", 1024, int)
+    max_wallclock_seconds = _e("MAX_WALLCLOCK_SECONDS", 599.0, float)
+    vocab_size = _e("VOCAB_SIZE", 16384, int)
+    num_layers = _e("NUM_LAYERS", 10, int)
+    num_kv_heads = _e("NUM_KV_HEADS", 3, int)
+    model_dim = _e("MODEL_DIM", 576, int)
+    num_heads = _e("NUM_HEADS", 6, int)
+    mlp_mult = _e("MLP_MULT", 4, int)
+    tie_embeddings = _e("TIE_EMBEDDINGS", 1, int)
+    rope_base = _e("ROPE_BASE", 5000.0, float)
+    rope_type = _e("ROPE_TYPE", "yarn")
+    yarn_max_len = _e("YARN_MAX_LEN", 2048, int)
+    logit_softcap = _e("LOGIT_SOFTCAP", 10.0, float)
+    softcap_type = _e("SOFTCAP_TYPE", "poly")
+    tied_embed_init_std = _e("TIED_EMBED_INIT_STD", 0.005, float)
+    qk_gain_init = _e("QK_GAIN_INIT", 5.0, float)
+    activation_type = _e("ACTIVATION", "relu2")
+    embed_dim = _e("EMBED_DIM", 380, int)
+    embed_lr = _e("EMBED_LR", 0.6, float)
+    head_lr = _e("HEAD_LR", 0.02, float)
+    adam_lr = _e("ADAM_LR", 0.05, float)
+    adam_wd = _e("ADAM_WD", 0.05, float)
+    tied_embed_lr = _e("TIED_EMBED_LR", 0.02, float)
+    seq_len_start = _e("SEQ_LEN_START", 0, int)
+    seq_schedule_fraction = _e("SEQ_SCHEDULE_FRACTION", 0.0, float)
+    batch_tokens_start = _e("BATCH_TOKENS_START", 0, int)
+    batch_schedule_fraction = _e("BATCH_SCHEDULE_FRACTION", 0.33, float)
+    churn_log_every = _e("CHURN_LOG_EVERY", 0, int)
+    matrix_lr = _e("MATRIX_LR", 0.035, float)
+    scalar_lr = _e("SCALAR_LR", 0.02, float)
+    muon_momentum = _e("MUON_MOMENTUM", 0.95, float)
+    muon_backend_steps = _e("MUON_BACKEND_STEPS", 3, int)
+    muon_wd = _e("MUON_WD", 0.0, float)
+    matrix_optimizer = _e("MATRIX_OPTIMIZER", "muon")
+    muon_momentum_warmup_start = _e("MUON_MOMENTUM_WARMUP_START", 0.85, float)
+    muon_momentum_warmup_steps = _e("MUON_MOMENTUM_WARMUP_STEPS", 500, int)
+    beta1 = _e("BETA1", 0.9, float)
+    beta2 = _e("BETA2", 0.95, float)
+    adam_eps = _e("ADAM_EPS", 1e-8, float)
+    grad_clip_norm = _e("GRAD_CLIP_NORM", 0.0, float)
+    bitnet_group_size = _e("BITNET_GROUP_SIZE", 192, int)
+    ttt_steps = _e("TTT_STEPS", 3, int)
+    ttt_lr = _e("TTT_LR", 0.005, float)
+    ttt_tokens = _e("TTT_TOKENS", 32768, int)
+    skip_training = _e("SKIP_TRAINING", 0, bool)
+    scale_quant_bits = _e("SCALE_QUANT_BITS", 5, int)  # 5-bit log-delta scale quantization
+    fp_storage = True  # FP8 storage for non-quinary (small) tensors
+
+# Calibration / fp16-stored scalar+vector params adapted by score-first TTT.
+# `scale_correction` was previously listed here but its gradient is blocked by
+# the STE detach in QuinaryLinear.forward, so including it as a TTT target was
+# a no-op. Excluded from the CTP tuple so the TTT optimizer reflects what
+# actually receives gradients (~42k params, not 232k). The parameter still
+# exists in the state-dict and is stored fp16 in the artifact (via the ndim<2
+# bucket); it is just not selected for TTT adaptation.
+CTP = ("attn_scale","mlp_scale","resid_mix","q_gain","skip_weights","vocab_bias")
+
+def pack_quinary(q: Tensor):
+    f = (q.reshape(-1).to(torch.int8) + 2).numpy()  # {-2..2} -> {0..4}
+    n = len(f)
+    p = (3 - n % 3) % 3
+    if p: f = np.concatenate([f, np.zeros(p, dtype=np.int8)])
+    g = f.reshape(-1, 3).astype(np.uint8)
+    return (g[:,0] + g[:,1]*5 + g[:,2]*25).tobytes(), n  # max = 4+20+100 = 124
+
+def unpack_quinary(data: bytes, n: int) -> Tensor:
+    v = np.frombuffer(data, dtype=np.uint8).astype(np.int16)
+    t = np.zeros((len(v), 3), dtype=np.int8)
+    for i in range(3): t[:,i] = v % 5; v //= 5
+    return torch.from_numpy(t.reshape(-1)[:n].astype(np.int8) - 2)
+
+
+def pack_quinary_bitmask(q) -> tuple[bytes, int, int]:
+    """Pack quinary symbols ∈ {-2..2} as three concatenated bit-planes.
+
+    Layout (big-endian within bytes via numpy.packbits):
+      [zero_mask:    ceil(n_total / 8) bytes — 1 bit/symbol, 1=zero]
+      [sign_bits:    ceil(n_nonzero / 8) bytes — 1 bit/nonzero, 1=negative]
+      [mag2_bits:    ceil(n_nonzero / 8) bytes — 1 bit/nonzero, 1=|symbol|==2]
+
+    Returns: (concat_bytes, n_total, n_nonzero).
+    Each plane has homogeneous bit statistics so the downstream compressor
+    can model them independently rather than fighting a multimodal mixture.
+    """
+    if isinstance(q, torch.Tensor):
+        a = q.reshape(-1).to(torch.int8).numpy()
+    else:
+        a = np.asarray(q, dtype=np.int8).reshape(-1)
+    n_total = a.size
+    nz_mask = (a != 0)                            # bool, len n_total
+    n_nonzero = int(nz_mask.sum())
+    nz_vals = a[nz_mask]                          # int8, len n_nonzero
+    sign_bits = (nz_vals < 0).astype(np.uint8)    # 1 = negative
+    mag2_bits = (np.abs(nz_vals) == 2).astype(np.uint8)
+    zero_packed = np.packbits((~nz_mask).astype(np.uint8))  # 1 bit means "this symbol is zero"
+    sign_packed = np.packbits(sign_bits)
+    mag2_packed = np.packbits(mag2_bits)
+    return (zero_packed.tobytes() + sign_packed.tobytes() + mag2_packed.tobytes(),
+            n_total, n_nonzero)
+
+
+def unpack_quinary_bitmask(data: bytes, n_total: int, n_nonzero: int) -> Tensor:
+    """Inverse of pack_quinary_bitmask. Returns int8 tensor of length n_total."""
+    z_len = (n_total + 7) // 8
+    s_len = (n_nonzero + 7) // 8
+    m_len = (n_nonzero + 7) // 8
+    expected = z_len + s_len + m_len
+    if len(data) != expected:
+        raise ValueError(f"bitmask data size {len(data)} != expected {expected} "
+                         f"(n_total={n_total}, n_nonzero={n_nonzero})")
+    z_bytes = np.frombuffer(data[:z_len], dtype=np.uint8)
+    s_bytes = np.frombuffer(data[z_len:z_len + s_len], dtype=np.uint8)
+    m_bytes = np.frombuffer(data[z_len + s_len:], dtype=np.uint8)
+    is_zero = np.unpackbits(z_bytes)[:n_total].astype(bool)
+    sign = np.unpackbits(s_bytes)[:n_nonzero].astype(bool)
+    mag2 = np.unpackbits(m_bytes)[:n_nonzero].astype(bool)
+    nz_vals = np.where(mag2, 2, 1).astype(np.int8)
+    nz_vals = np.where(sign, -nz_vals, nz_vals)
+    out = np.zeros(n_total, dtype=np.int8)
+    out[~is_zero] = nz_vals
+    return torch.from_numpy(out)
+
+
+# Artifact archive: layout-aware per-stream v2 (header byte 0x03).
+#
+#   - For each quinary tensor: screen the 4 layouts {base5, base5_T,
+#     bitmask, bitmask_T} by LZMA9-compressed size, then run LZMA9 vs
+#     lrzip-zpaq -L9 only on the winning layout. Bounded heuristic
+#     with an LZMA floor — *not* an exhaustive 4×2 search.
+#   - For c_qkv.weight: split rows into Q / K / V sub-payloads each chosen
+#     independently (Q, K, V have different trained distributions).
+#   - For other bulk fields (FP8 embeddings, large fp16 tensors): torch.save
+#     the value and compress with min(lzma, lrzip).
+#   - Robust to the seed-dependent lrzip cliff observed on full-blob
+#     compression: even if lrzip ZPAQ underperforms on one tensor's bytes,
+#     lzma takes over for that tensor specifically.
+_COMPRESSOR_LZMA = 0
+_COMPRESSOR_LRZIP_ZPAQ = 1
+_COMPRESSOR_PER_STREAM_V2 = 3
+
+# Threshold for treating a value as "bulk" (compressed independently).
+_PER_STREAM_BULK_BYTES = 64 * 1024
+
+# v2 layout IDs — what kind of body is stored in a bulk payload.
+_LAYOUT_RAW         = 0  # opaque bytes (torch.save output for non-quinary fields)
+_LAYOUT_Q_BASE5     = 1  # base-5 packed quinary symbols, canonical row-major order
+_LAYOUT_Q_BASE5_T   = 2  # base-5 packed quinary symbols, transposed (column-major)
+_LAYOUT_Q_BITMASK   = 3  # bitmask packed (zero|sign|mag2), canonical
+_LAYOUT_Q_BITMASK_T = 4  # bitmask packed, transposed
+_LAYOUT_NAMES = {
+    _LAYOUT_RAW: "raw",
+    _LAYOUT_Q_BASE5: "base5",
+    _LAYOUT_Q_BASE5_T: "base5_T",
+    _LAYOUT_Q_BITMASK: "bitmask",
+    _LAYOUT_Q_BITMASK_T: "bitmask_T",
+}
+
+# v2 sentinel for metadata refs (distinct string from v1 to avoid version confusion).
+_BULK_SENTINEL_V2 = "__BULK_REF_V2__"
+
+
+def _lrzip_compress_bytes(data: bytes, level: int = 9) -> bytes:
+    """Compress raw bytes via `lrzip -z` (ZPAQ). Returns body only (no header)."""
+    with tempfile.NamedTemporaryFile(delete=False) as f:
+        f.write(data); in_path = f.name
+    out_path = in_path + ".lrz"
+    try:
+        try:
+            subprocess.run(["lrzip", "-z", "-L", str(level), "-q", "-f", in_path],
+                           check=True, capture_output=True)
+        except FileNotFoundError as e:
+            raise RuntimeError("lrzip binary not found; run `apt-get install lrzip`") from e
+        with open(out_path, "rb") as f:
+            return f.read()
+    finally:
+        for p in (in_path, out_path):
+            try: os.unlink(p)
+            except FileNotFoundError: pass
+
+
+def _lrzip_decompress_bytes(body: bytes) -> bytes:
+    """Decompress an lrzip ZPAQ body (no header)."""
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".lrz") as f:
+        f.write(body); in_path = f.name
+    out_path = in_path[:-4]
+    try:
+        try:
+            subprocess.run(["lrzip", "-d", "-q", "-f", in_path],
+                           check=True, capture_output=True)
+        except FileNotFoundError as e:
+            raise RuntimeError("lrzip binary not found; required to load this artifact") from e
+        with open(out_path, "rb") as f:
+            return f.read()
+    finally:
+        for p in (in_path, out_path):
+            try: os.unlink(p)
+            except FileNotFoundError: pass
+
+
+def _pick_best_compressor(data: bytes, level: int = 9, has_lrzip: bool = True) -> tuple[int, bytes]:
+    """Try lzma + (optionally) lrzip; return (method_id, body) for the smaller.
+
+    This is the per-stream robustness: even if one compressor cliffs on a
+    given byte distribution (the seed=7 issue), the other usually doesn't.
+    Any failure (missing binary, runtime error, broken lrzip backend) on
+    lrzip is silently skipped — lzma is the always-available floor.
+    """
+    candidates: list[tuple[int, bytes]] = [(_COMPRESSOR_LZMA, lzma.compress(data, preset=level))]
+    if has_lrzip:
+        try:
+            candidates.append((_COMPRESSOR_LRZIP_ZPAQ, _lrzip_compress_bytes(data, level)))
+        except (RuntimeError, subprocess.CalledProcessError, OSError):
+            pass
+    return min(candidates, key=lambda c: len(c[1]))
+
+
+def _is_bulk(value) -> bool:
+    """Decide whether a state_dict field should be peeled off for per-stream compression."""
+    if isinstance(value, (bytes, bytearray)):
+        return len(value) >= _PER_STREAM_BULK_BYTES
+    if isinstance(value, torch.Tensor):
+        return value.numel() * value.element_size() >= _PER_STREAM_BULK_BYTES
+    return False
+
+
+def _load_artifact(blob: bytes) -> dict:
+    """Load a per-stream v2 archive (header byte 0x03) into a state_dict."""
+    return _deserialize_per_stream_v2(blob)
+
+
+# ---------------------------------------------------------------------------
+# v2 archive: layout-aware per-stream compression
+# ---------------------------------------------------------------------------
+
+def _gen_quinary_layout_candidates(symbols_2d: np.ndarray) -> list[tuple[int, bytes, dict]]:
+    """For a 2D int8 symbol matrix in {-2..2}, return a list of
+    (layout_id, body_bytes, layout_meta) tuples — every supported layout.
+
+    layout_meta carries the per-layout fields needed at deserialize time
+    (n_quins, n_nonzero, etc.).
+    """
+    rows, cols = symbols_2d.shape
+    flat = symbols_2d.reshape(-1)
+    transposed = symbols_2d.T.reshape(-1).copy()  # contiguous transpose
+
+    # base-5 canonical
+    body_b5, n_b5 = pack_quinary(torch.from_numpy(flat))
+    # base-5 transposed
+    body_b5_t, n_b5_t = pack_quinary(torch.from_numpy(transposed))
+    # bitmask canonical
+    body_bm, nt_bm, nz_bm = pack_quinary_bitmask(torch.from_numpy(flat))
+    # bitmask transposed
+    body_bm_t, nt_bm_t, nz_bm_t = pack_quinary_bitmask(torch.from_numpy(transposed))
+
+    return [
+        (_LAYOUT_Q_BASE5,     body_b5,   {"n_quins": n_b5}),
+        (_LAYOUT_Q_BASE5_T,   body_b5_t, {"n_quins": n_b5_t}),
+        (_LAYOUT_Q_BITMASK,   body_bm,   {"n_total": nt_bm, "n_nonzero": nz_bm}),
+        (_LAYOUT_Q_BITMASK_T, body_bm_t, {"n_total": nt_bm_t, "n_nonzero": nz_bm_t}),
+    ]
+
+
+def _layout_to_canonical_bytes(layout: int, body: bytes, rows: int, cols: int,
+                               layout_meta: dict) -> bytes:
+    """Inverse of _gen_quinary_layout_candidates: take a body in one of the
+    layout encodings and return the canonical base-5-packed bytes that
+    `deq_sd` expects in entry["packed"]."""
+    if layout == _LAYOUT_Q_BASE5:
+        # Already canonical.
+        return body
+    if layout == _LAYOUT_Q_BASE5_T:
+        symbols_t = unpack_quinary(body, layout_meta["n_quins"]).numpy()
+        symbols = symbols_t.reshape(cols, rows).T.reshape(-1).copy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    if layout == _LAYOUT_Q_BITMASK:
+        symbols = unpack_quinary_bitmask(body, layout_meta["n_total"], layout_meta["n_nonzero"]).numpy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    if layout == _LAYOUT_Q_BITMASK_T:
+        symbols_t = unpack_quinary_bitmask(body, layout_meta["n_total"], layout_meta["n_nonzero"]).numpy()
+        symbols = symbols_t.reshape(cols, rows).T.reshape(-1).copy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    raise ValueError(f"unknown layout id {layout}")
+
+
+def _qkv_split_sizes(name: str, rows: int, cols: int) -> tuple[int, int] | None:
+    """For a c_qkv weight, derive (q_rows, kv_rows) row-counts.
+
+    Standard transformer convention: q_size = num_heads * head_dim = model_dim
+    (the square attention assumption). So q_rows == cols. The remaining rows
+    are split equally between K and V: kv_rows = (rows - cols) // 2.
+
+    Returns None if name doesn't look like c_qkv or the ratio doesn't decompose
+    cleanly (in which case we don't split, just compress as one tensor).
+    """
+    if not name.endswith("c_qkv.weight"):
+        return None
+    if rows <= cols:
+        return None  # not a GQA shape; or single-head case where Q==K==V
+    extra = rows - cols  # 2 * kv_rows
+    if extra % 2 != 0:
+        return None
+    kv_rows = extra // 2
+    q_rows = cols
+    if q_rows + 2 * kv_rows != rows:
+        return None
+    return q_rows, kv_rows
+
+
+def _serialize_per_stream_v2(state_dict: dict, level: int = 9) -> tuple[bytes, str]:
+    """v2 archive: layout-aware per-stream compression.
+
+    For each quinary entry, generate up to 4 layout candidates
+    (base5, base5_T, bitmask, bitmask_T), screen them by LZMA9-compressed
+    size, then compress the winning layout with min(LZMA9, lrzip-zpaq).
+    For c_qkv weights, the row-block is split into Q/K/V sub-payloads
+    handled independently. All other bulk fields use the v1 raw-bytes path.
+
+    This is a bounded-cost heuristic with an LZMA floor — *not* an
+    exhaustive 4×2 search. It can in principle miss a (layout, compressor)
+    pair where the LZMA-screen-loser would have won under lrzip; in
+    practice this is rare on this stack and the LZMA floor caps the
+    worst case at the canonical base5+LZMA encoding.
+
+    Returns (archive_bytes, audit_string) so the caller can route the
+    audit line through the run's logger.
+    """
+    from shutil import which
+    has_lrzip = which("lrzip") is not None
+
+    bulk_bodies: list[tuple[int, bytes]] = []  # (compressor_method, compressed_body)
+    metadata: dict = {}
+    audit_lines: list[str] = []
+    layout_counts = {n: 0 for n in _LAYOUT_NAMES.values()}
+    method_counts = {"lzma": 0, "lrzip": 0}
+    qkv_split_count = 0
+
+    def _store(layout: int, body: bytes) -> tuple[int, int, int]:
+        """Compress and store; return (payload_idx, method_id, compressed_size)."""
+        method, comp = _pick_best_compressor(body, level, has_lrzip)
+        bulk_bodies.append((method, comp))
+        method_counts["lzma" if method == _COMPRESSOR_LZMA else "lrzip"] += 1
+        layout_counts[_LAYOUT_NAMES[layout]] += 1
+        return len(bulk_bodies) - 1, method, len(comp)
+
+    def _best_quinary_layout(symbols_2d: np.ndarray) -> tuple[int, int, int, dict, int]:
+        """Generate all layout candidates, screen with lzma to pick best layout,
+        then compress winner with min(lzma, lrzip) for the final body.
+
+        Cheap-screen + refine cuts serialize compute from O(layouts*compressors)
+        to O(layouts) lzma + 1 lrzip, keeping serialize time bounded even when
+        lrzip is slow on bad streams.
+
+        Returns (chosen_layout, payload_idx, compressed_size, layout_meta, n_candidates_evaluated).
+        """
+        cands = _gen_quinary_layout_candidates(symbols_2d)
+        best_layout, best_body, best_meta = None, None, None
+        best_lzma_size = None
+        for layout, body, meta in cands:
+            lzma_size = len(lzma.compress(body, preset=level))
+            if best_lzma_size is None or lzma_size < best_lzma_size:
+                best_lzma_size = lzma_size
+                best_layout, best_body, best_meta = layout, body, meta
+        # Now compress the winning layout with min(lzma, lrzip) for the actual stored body.
+        comp_method, comp_body = _pick_best_compressor(best_body, level, has_lrzip)
+        bulk_bodies.append((comp_method, comp_body))
+        method_counts["lzma" if comp_method == _COMPRESSOR_LZMA else "lrzip"] += 1
+        layout_counts[_LAYOUT_NAMES[best_layout]] += 1
+        return best_layout, len(bulk_bodies) - 1, len(comp_body), best_meta, len(cands)
+
+    for name, entry in state_dict.items():
+        if not isinstance(entry, dict):
+            metadata[name] = entry
+            continue
+        new_entry = dict(entry)  # shallow copy
+
+        if entry.get("type") == "quinary" and "packed" in entry:
+            # Reconstruct full symbol matrix from canonical packed bytes.
+            rows, cols = entry["shape"][0], entry["padded_cols"]
+            symbols_full = unpack_quinary(entry["packed"], entry["n_quins"]).numpy()
+            symbols_2d = symbols_full[:rows * cols].reshape(rows, cols)
+
+            split = _qkv_split_sizes(name, rows, cols)
+            if split is not None:
+                q_rows, kv_rows = split
+                qkv_split_count += 1
+                refs = []
+                for sub_name, sub_2d in (
+                    ("Q", symbols_2d[:q_rows]),
+                    ("K", symbols_2d[q_rows:q_rows + kv_rows]),
+                    ("V", symbols_2d[q_rows + kv_rows:]),
+                ):
+                    layout, idx, sz, meta, _ = _best_quinary_layout(sub_2d.copy())
+                    sub_rows, sub_cols = sub_2d.shape
+                    refs.append({
+                        "_v2": _BULK_SENTINEL_V2, "idx": idx, "layout": layout,
+                        "rows": sub_rows, "cols": sub_cols, "meta": meta,
+                    })
+                new_entry["packed"] = ("__QKV_SPLIT__", refs)
+                audit_lines.append(f"  {name}: qkv_split layouts={[_LAYOUT_NAMES[r['layout']] for r in refs]}")
+            else:
+                layout, idx, sz, meta, _ = _best_quinary_layout(symbols_2d.copy())
+                new_entry["packed"] = {
+                    "_v2": _BULK_SENTINEL_V2, "idx": idx, "layout": layout,
+                    "rows": rows, "cols": cols, "meta": meta,
+                }
+                audit_lines.append(f"  {name}: {_LAYOUT_NAMES[layout]} ({sz/1e6:.3f}MB)")
+
+        # Other bulk fields (fp8 data, fp16 large tensors, scale_delta tensors,
+        # etc.) — fall back to v1's torch.save+pick path.
+        for key, value in list(new_entry.items()):
+            if key == "packed":  # already handled
+                continue
+            if _is_bulk(value):
+                buf = io.BytesIO(); torch.save(value, buf)
+                method, comp = _pick_best_compressor(buf.getvalue(), level, has_lrzip)
+                bulk_bodies.append((method, comp))
+                method_counts["lzma" if method == _COMPRESSOR_LZMA else "lrzip"] += 1
+                layout_counts["raw"] += 1
+                new_entry[key] = {
+                    "_v2": _BULK_SENTINEL_V2, "idx": len(bulk_bodies) - 1,
+                    "layout": _LAYOUT_RAW,
+                }
+        metadata[name] = new_entry
+
+    # Compress metadata.
+    meta_buf = io.BytesIO(); torch.save(metadata, meta_buf)
+    meta_compressed = lzma.compress(meta_buf.getvalue(), preset=level)
+
+    # Assemble archive (header 0x03).
+    out = bytearray()
+    out.append(_COMPRESSOR_PER_STREAM_V2)
+    out.extend(len(meta_compressed).to_bytes(4, "little"))
+    out.extend(meta_compressed)
+    out.extend(len(bulk_bodies).to_bytes(4, "little"))
+    for method, body in bulk_bodies:
+        out.append(method)
+        out.extend(len(body).to_bytes(4, "little"))
+        out.extend(body)
+
+    layout_summary = ", ".join(f"{n}={c}" for n, c in layout_counts.items() if c)
+    audit = (f"per_stream_v2: meta={len(meta_compressed)/1e6:.2f}MB, "
+             f"{len(bulk_bodies)} bulk payloads "
+             f"(lzma={method_counts['lzma']}, lrzip={method_counts['lrzip']}; "
+             f"{layout_summary}), qkv_splits={qkv_split_count}, "
+             f"total={len(out)/1e6:.2f}MB")
+    return bytes(out), audit
+
+
+def _deserialize_per_stream_v2(blob: bytes) -> dict:
+    """Inverse of _serialize_per_stream_v2. Returns a state_dict whose quinary
+    entries have canonical entry["packed"] bytes — `deq_sd` is unaware of the
+    archive layer."""
+    cursor = 0
+    assert blob[cursor] == _COMPRESSOR_PER_STREAM_V2, \
+        f"expected v2 header 0x03, got {blob[0]:#x}"
+    cursor += 1
+    meta_size = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+    meta_blob = blob[cursor:cursor + meta_size]; cursor += meta_size
+    metadata = torch.load(io.BytesIO(lzma.decompress(meta_blob)),
+                          map_location="cpu", weights_only=False)
+    n_payloads = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+    decompressed: list[bytes] = []
+    for _ in range(n_payloads):
+        method = blob[cursor]; cursor += 1
+        size = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+        body = blob[cursor:cursor + size]; cursor += size
+        if method == _COMPRESSOR_LZMA:
+            decompressed.append(lzma.decompress(body))
+        elif method == _COMPRESSOR_LRZIP_ZPAQ:
+            decompressed.append(_lrzip_decompress_bytes(body))
+        else:
+            raise ValueError(f"unknown compressor method {method:#x}")
+
+    def _is_v2_ref(v):
+        return isinstance(v, dict) and v.get("_v2") == _BULK_SENTINEL_V2
+
+    for name, entry in metadata.items():
+        if not isinstance(entry, dict):
+            continue
+        for key, value in list(entry.items()):
+            if _is_v2_ref(value):
+                if value["layout"] == _LAYOUT_RAW:
+                    entry[key] = torch.load(io.BytesIO(decompressed[value["idx"]]),
+                                            map_location="cpu", weights_only=False)
+                else:
+                    entry[key] = _layout_to_canonical_bytes(
+                        value["layout"], decompressed[value["idx"]],
+                        value["rows"], value["cols"], value["meta"])
+            elif (isinstance(value, tuple) and len(value) == 2
+                  and value[0] == "__QKV_SPLIT__"):
+                # Reconstruct each part's symbol matrix, concat along row dim,
+                # repack as canonical base-5.
+                refs = value[1]
+                sub_symbols = []
+                for ref in refs:
+                    canonical_body = _layout_to_canonical_bytes(
+                        ref["layout"], decompressed[ref["idx"]],
+                        ref["rows"], ref["cols"], ref["meta"])
+                    n = ref["rows"] * ref["cols"]
+                    sub_syms = unpack_quinary(canonical_body, n).numpy()[:n]
+                    sub_symbols.append(sub_syms.reshape(ref["rows"], ref["cols"]))
+                full = np.concatenate(sub_symbols, axis=0).reshape(-1).copy()
+                entry[key], _ = pack_quinary(torch.from_numpy(full))
+    return metadata
+
+
+def _quant_scale(scale_fp16: Tensor, bits: int) -> dict:
+    """Quantize per-group scales to bits-bit log-deltas. Returns dict with
+    `scale_anchor` (fp16), `scale_step` (fp16), `scale_delta` (packed bytes
+    or int8 tensor), plus `scale_bits` to dispatch in deq_sd."""
+    scale_f = scale_fp16.float().reshape(-1)
+    n_groups = scale_f.numel()
+    log_scale = torch.log2(scale_f.clamp(min=1e-12))
+    lo, hi = float(log_scale.min().item()), float(log_scale.max().item())
+    anchor = (lo + hi) / 2.0
+    spread = hi - lo
+    levels = 1 << bits
+    half = levels // 2
+    step = max(spread / max(levels - 1, 1), 1e-9)
+    delta = ((log_scale - anchor) / step).round().clamp(-half, half - 1).to(torch.int32)
+    if bits == 4:
+        d = (delta + half).to(torch.uint8).numpy()
+        if len(d) % 2:
+            d = np.concatenate([d, np.zeros(1, dtype=np.uint8)])
+        packed = (d[::2] | (d[1::2] << 4)).astype(np.uint8)
+        delta_stored = torch.from_numpy(packed.copy())
+    elif bits == 5:
+        delta_stored = (delta + half).to(torch.uint8)
+    else:  # 8
+        delta_stored = delta.to(torch.int8)
+    return {
+        "scale_anchor": torch.tensor(anchor, dtype=torch.float16),
+        "scale_step": torch.tensor(step, dtype=torch.float16),
+        "scale_delta": delta_stored,
+        "scale_n_groups": n_groups,
+        "scale_bits": bits,
+    }
+
+def _dequant_scale(entry: dict) -> Tensor:
+    """Reconstruct fp16 per-group scales from the quantized representation."""
+    bits = int(entry["scale_bits"])
+    half = (1 << bits) // 2
+    n_groups = int(entry["scale_n_groups"])
+    if bits == 4:
+        packed = entry["scale_delta"].to(torch.uint8).numpy()
+        low = packed & 0x0F
+        high = (packed >> 4) & 0x0F
+        d = np.empty(packed.size * 2, dtype=np.uint8)
+        d[0::2] = low
+        d[1::2] = high
+        delta = torch.from_numpy(d[:n_groups].astype(np.int32) - half)
+    elif bits == 5:
+        delta = entry["scale_delta"].to(torch.int32) - half
+    else:  # 8
+        delta = entry["scale_delta"].to(torch.int32)
+    anchor = entry["scale_anchor"].float()
+    step = entry["scale_step"].float()
+    return (2.0 ** (anchor + delta.float() * step)).to(torch.float16)
+
+
+def q_sd(state_dict: dict, group_size: int = 64,
+         scale_quant_bits: int = 0) -> tuple[dict, dict]:
+    quantized = {}
+    stats = {"quinary_params": 0, "quinary_bytes": 0, "fp_params": 0, "fp_bytes": 0}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().float().contiguous()
+        t_orig_shape = list(t.shape)
+        if t.ndim == 3:
+            t = t.reshape(t.shape[0], -1)
+        is_quantized_candidate = (
+            t.ndim == 2 and t.numel() > 65_536
+            and "tok_emb" not in name and "lm_head" not in name and "embed_proj" not in name
+        )
+        if is_quantized_candidate:
+            pad = (group_size - t.shape[1] % group_size) % group_size
+            t_padded = F.pad(t, (0, pad)) if pad > 0 else t
+            t_grouped = t_padded.reshape(-1, group_size)
+            scale = t_grouped.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+            q = (t_grouped / scale).round().clamp(-2, 2).to(torch.int8)
+
+            packed_bytes, n_packed = pack_quinary(q)
+            stats["quinary_params"] += t.numel()
+
+            entry = {
+                "type": "quinary", "packed": packed_bytes,
+                "shape": list(t.shape), "padded_cols": t_padded.shape[1],
+                "group_size": group_size, "n_quins": n_packed,
+                "orig_shape": t_orig_shape,
+            }
+            if scale_quant_bits and scale_quant_bits in (4, 5, 8):
+                sq = _quant_scale(scale.half().squeeze(-1), scale_quant_bits)
+                entry.update(sq)
+                stats["quinary_bytes"] += len(packed_bytes) + sq["scale_delta"].numel() + 4  # +4 for anchor+step
+            else:
+                entry["scale"] = scale.half().squeeze(-1)
+                stats["quinary_bytes"] += len(packed_bytes) + scale.numel() * 2
+            quantized[name] = entry
+        elif t.ndim == 2:
+            quantized[name] = {"type": "fp8", "data": t.to(torch.float8_e4m3fn)}
+            stats["fp_params"] += t.numel()
+            stats["fp_bytes"] += t.numel()
+        else:
+            quantized[name] = {"type": "fp16", "data": t.half()}
+            stats["fp_params"] += t.numel()
+            stats["fp_bytes"] += t.numel() * 2
+    return quantized, stats
+
+def deq_sd(quantized: dict, target_dtype=torch.bfloat16):
+    out = {}
+    for name, entry in quantized.items():
+        if entry["type"] == "quinary":
+            q = unpack_quinary(entry["packed"], entry["n_quins"])
+            q = q.float().reshape(-1, entry["group_size"])
+            if "scale_bits" in entry:
+                scale = _dequant_scale(entry).float().unsqueeze(-1)
+            else:
+                scale = entry["scale"].float().unsqueeze(-1)
+            q_absmean = q.abs().mean(-1, keepdim=True).clamp(min=1e-8)
+            t = (q * (scale / q_absmean)).reshape(-1, entry["padded_cols"])
+            shape = entry["shape"]
+            result = t[:shape[0], :shape[1]].to(target_dtype)
+            orig = entry.get("orig_shape")
+            out[name] = result.reshape(orig).contiguous() if orig and orig != shape else result.contiguous()
+        elif entry["type"] == "fp8":
+            out[name] = entry["data"].to(torch.float32).to(target_dtype).contiguous()
+        else:
+            out[name] = entry["data"].to(target_dtype).contiguous()
+    return out
+
+def quin_stats(model: nn.Module, group_size: int = 64):
+    total = zeros = 0
+    with torch.no_grad():
+        for name, p in model.named_parameters():
+            if p.ndim == 2 and ("weight" in name or "prototypes" in name) and p.shape[0] > 1 and p.numel() % group_size == 0:
+                w = p.detach().float().reshape(-1, group_size)
+                scale = w.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+                q = (w / scale).round().clamp(-2, 2)
+                zeros += int((q == 0).sum().item())
+                total += int(q.numel())
+    return {"zero_frac": zeros / max(total, 1), "total_weights": total}
+
+_prev_committed: dict = {}
+
+def churn_fn(model: nn.Module, group_size: int = 64):
+    global _prev_committed
+    total = flipped = 0
+    with torch.no_grad():
+        for name, p in model.named_parameters():
+            if p.ndim == 2 and ("weight" in name or "prototypes" in name) and p.shape[0] > 1 and p.numel() % group_size == 0:
+                w = p.detach().float().reshape(-1, group_size)
+                scale = w.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+                q = (w / scale).round().clamp(-2, 2).cpu().numpy()
+                if name in _prev_committed:
+                    flipped += int(np.sum(q != _prev_committed[name]))
+                    total += q.size
+                _prev_committed[name] = q
+    return flipped / max(total, 1)
+
+def ns_orth(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, wd: float = 0.0):
+        super().__init__(params, dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, wd=wd))
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr, momentum = group["lr"], group["momentum"]
+            backend_steps, nesterov = group["backend_steps"], group["nesterov"]
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = F.rms_norm(g.float(), (g.size(-1),)).bfloat16()
+                    g = ns_orth(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr:curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+            wd = group.get("wd", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.mul_(1 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+def ld_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = ld_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self):
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = ld_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos:self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank, self.world_size, self.device = rank, world_size, device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start:start + per_rank_span].pin_memory().to(self.device, non_blocking=True).to(torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x, y
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+def apply_fp8_ste(w: Tensor) -> Tensor:
+    w_sim = w.to(torch.float8_e4m3fn).to(w.dtype)
+    return (w_sim - w).detach() + w
+
+class QATLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w_qat = apply_fp8_ste(self.weight)
+        return F.linear(x, w_qat.to(x.dtype), self.bias.to(x.dtype) if self.bias is not None else None)
+
+class QATEmbedding(nn.Embedding):
+    def forward(self, input: Tensor) -> Tensor:
+        w_qat = apply_fp8_ste(self.weight)
+        return F.embedding(input, w_qat, self.padding_idx, self.max_norm,
+                           self.norm_type, self.scale_grad_by_freq, self.sparse)
+
+class QuinaryLinear(nn.Linear):
+    def __init__(self, in_features, out_features, bias=False, group_size=64):
+        super().__init__(in_features, out_features, bias=bias)
+        self.group_size = group_size
+        num_groups = (in_features * out_features) // group_size
+        # Inert by design: the STE detach below blocks gradients to
+        # `scale_correction`. Kept as a fp32 buffer at value 1.0 for
+        # backwards-compatibility with the ternary-base state-dict layout.
+        # An attempt to fix the STE so this parameter receives gradients
+        # was tested 2026-05-01 (commit b9c…) and showed a small
+        # training-time regression with no TTT benefit, so reverted.
+        self.scale_correction = nn.Parameter(torch.ones(num_groups, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.bfloat16()
+        g = self.group_size
+        w_g = w.reshape(-1, g)
+        scale = w_g.abs().mean(-1, keepdim=True).clamp(min=1e-8) * self.scale_correction.to(w.dtype).unsqueeze(-1)
+        q = (w_g / scale).round().clamp(-2, 2)
+        w_quantized = w + ((q * scale).reshape(w.shape) - w).detach()
+        return F.linear(x, w_quantized,
+                        self.bias.to(x.dtype) if self.bias is not None else None)
+
+
+class NormedQuinaryLinear(QuinaryLinear):
+    def forward(self, x: Tensor) -> Tensor:
+        return super().forward(F.rms_norm(x, (x.size(-1),)))
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(p in name for p in CTP)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0, no_cache: bool = False,
+                 rope_type: str = "rope", yarn_max_len: int = 4096, train_seq_len: int = 1024):
+        super().__init__()
+        self.no_cache = no_cache
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        if rope_type == "yarn":
+            scale = train_seq_len / yarn_max_len
+            freq_idx = torch.arange(0, dim, 2, dtype=torch.float32)
+            ramp = torch.clamp((freq_idx / dim - 0.25) / 0.75, 0.0, 1.0)
+            inv_freq = inv_freq / (ramp * (1.0 / scale - 1.0) + 1.0)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len, device, dtype):
+        if self.no_cache:
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            return freqs.cos()[None, :, None, :].to(dtype=dtype), freqs.sin()[None, :, None, :].to(dtype=dtype)
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, :, None, :]
+            self._sin_cached = freqs.sin()[None, :, None, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init,
+                 group_size=64, no_cache=False, rope_type="rope",
+                 yarn_max_len=4096, train_seq_len=1024):
+        super().__init__()
+        self.num_heads, self.num_kv_heads = num_heads, num_kv_heads
+        self.head_dim = dim // num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        self.c_qkv = QuinaryLinear(dim, self.q_size + 2 * self.kv_size, bias=False, group_size=group_size)
+        self.proj = NormedQuinaryLinear(dim, dim, bias=False, group_size=group_size)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base, no_cache=no_cache,
+                             rope_type=rope_type, yarn_max_len=yarn_max_len,
+                             train_seq_len=train_seq_len)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        qkv_out = self.c_qkv(x)
+        q_out, k_out, v_out = qkv_out.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q = q_out.reshape(bsz, seqlen, self.num_heads, self.head_dim)
+        k = k_out.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        v = v_out.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        q, k = F.rms_norm(q, (q.size(-1),)), F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None]
+        y = flash_attn_func(q.contiguous(), k.contiguous(), v.contiguous(), causal=True)
+        y = y.reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+class MLP(nn.Module):
+    def __init__(self, dim, mlp_mult, group_size=64, activation="swiglu"):
+        super().__init__()
+        hidden = mlp_mult * dim
+        self.activation = activation
+        if activation == "swiglu":
+            self.gate_up = QuinaryLinear(dim, hidden * 2, bias=False, group_size=group_size)
+        else:
+            self.fc = QuinaryLinear(dim, hidden, bias=False, group_size=group_size)
+        self.proj = NormedQuinaryLinear(hidden, dim, bias=False, group_size=group_size)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        if self.activation == "swiglu":
+            gu = self.gate_up(x)
+            gate, up = gu.chunk(2, dim=-1)
+            return self.proj(F.silu(gate) * up)
+        elif self.activation == "relu":
+            return self.proj(torch.relu(self.fc(x)))
+        elif self.activation == "leaky_relu":
+            return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.01))
+        elif self.activation == "leaky_relu2":
+            return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square())
+        else:  # relu2
+            return self.proj(torch.relu(self.fc(x)).square())
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int,
+                 rope_base: float, qk_gain_init: float, group_size: int=64,
+                 activation: str="swiglu", no_cache: bool=False,
+                 rope_type: str="rope", yarn_max_len: int=4096,
+                 train_seq_len: int=1024):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init,
+                                        group_size, no_cache, rope_type, yarn_max_len, train_seq_len)
+        self.mlp = MLP(dim, mlp_mult, group_size, activation)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x_in = mix[0] * x + mix[1] * x0
+        n = self.attn_norm(x_in)
+        x = x_in + self.attn_scale.to(dtype=x_in.dtype) * self.attn(n)
+        x = x + self.mlp_scale.to(dtype=x.dtype) * self.mlp(self.mlp_norm(x))
+        return x
+
+class GPT(nn.Module):
+    def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, mlp_mult,
+                 tie_embeddings, tied_embed_init_std, logit_softcap, rope_base, qk_gain_init,
+                 group_size: int = 64, activation: str = "swiglu",
+                 embed_dim: int = 0, softcap_type: str="poly", no_cache: bool=False,
+                 rope_type: str="rope", yarn_max_len: int=4096, train_seq_len: int=1024):
+        super().__init__()
+        self.tie_embeddings = tie_embeddings
+        self.logit_softcap = logit_softcap
+        self.softcap_type = softcap_type
+        self.embed_dim = embed_dim if embed_dim > 0 else model_dim
+        self.tok_emb = QATEmbedding(vocab_size, self.embed_dim)
+        self.embed_proj = QATLinear(self.embed_dim, model_dim, bias=False) if self.embed_dim != model_dim else None
+        self.embed_proj_rev = QATLinear(model_dim, self.embed_dim, bias=False) if self.embed_dim != model_dim else None
+
+        self.blocks = nn.ModuleList([
+            Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init,
+                  group_size, activation, no_cache, rope_type, yarn_max_len, train_seq_len)
+            for _ in range(num_layers)
+        ])
+
+        # U-Net split: first half encoder, second half decoder, decoder layers
+        # add a learned-weighted skip from the symmetric encoder layer.
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+
+        self.final_norm = RMSNorm()
+        self.lm_head = QATLinear(model_dim, vocab_size, bias=False)
+        self.lm_head._zero_init = True
+        if tie_embeddings:
+            self.lm_head.weight.requires_grad_(False)
+
+        self.vocab_bias = nn.Parameter(torch.zeros(vocab_size, dtype=torch.float32))
+        self._init_weights(tied_embed_init_std)
+
+    def _init_weights(self, tied_embed_init_std: float) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=tied_embed_init_std)
+        for module in self.modules():
+            if isinstance(module, QuinaryLinear) and not getattr(module, "_zero_init", False):
+                nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            elif isinstance(module, nn.Linear) and getattr(module, "_zero_init", False):
+                nn.init.zeros_(module.weight)
+
+    def _compute_logits(self, x: Tensor) -> Tensor:
+        if self.tie_embeddings:
+            proj = self.embed_proj_rev(x) if self.embed_proj_rev is not None else x
+            logits_raw = F.linear(proj, self.tok_emb.weight.to(x.dtype))
+        else:
+            logits_raw = self.lm_head(x)
+        return logits_raw + self.vocab_bias.to(x.dtype)
+
+    def _softcap(self, logits: Tensor) -> Tensor:
+        s = self.logit_softcap
+        if self.softcap_type == "tanh":
+            return s * torch.tanh(logits / s)
+        x_sc = torch.clamp(logits / s, -2.0, 2.0)
+        x2 = x_sc * x_sc
+        return s * torch.clamp(x_sc * (1.0 - x2 / 3.0 + x2 * x2 / 15.0), -1.0, 1.0)
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor, reduction: str = "mean") -> Tensor:
+        x = self.tok_emb(input_ids).float()
+        if self.embed_proj is not None:
+            x = self.embed_proj(x)
+        x = F.rms_norm(x, (x.size(-1),))
+        x0 = x
+
+        # U-Net encoder/decoder with skip connections.
+        skips = []
+        for vi in range(self.num_encoder_layers):
+            x = self.blocks[vi](x, x0)
+            skips.append(x)
+        for vi in range(self.num_decoder_layers):
+            if vi < self.num_skip_weights:
+                x = x + self.skip_weights[vi].to(dtype=x.dtype) * skips.pop()
+            x = self.blocks[self.num_encoder_layers + vi](x, x0)
+
+        x_normed = self.final_norm(x)
+        x_flat = x_normed.reshape(-1, x_normed.size(-1))
+        targets = target_ids.reshape(-1)
+        logits = self._softcap(self._compute_logits(x_flat))
+
+        if reduction == "none":
+            return F.cross_entropy(logits.float(), targets, reduction="none").reshape(input_ids.shape)
+
+        # Fused CE: single logsumexp; z-loss only during training (not eval BPB).
+        logits_f = logits.float()
+        lse = torch.logsumexp(logits_f, dim=-1)
+        target_logits = logits_f.gather(1, targets.unsqueeze(1)).squeeze(1)
+        main_loss = (lse - target_logits).mean()
+        if self.training:
+            main_loss = main_loss + 1e-4 * (lse ** 2).mean()
+        return main_loss
+
+
+def build_luts(sp, vocab_size: int, device: torch.device):
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+def ld_val(pattern, seq_len, max_tok=int(os.environ.get("VAL_MAX_TOKENS", 0))):
+    files = sorted(glob.glob(pattern))
+    assert files, f"No files: {pattern}"
+    tok = torch.cat([ld_shard(Path(p)) for p in files]).contiguous()
+    if max_tok > 0: tok = tok[:max_tok + 1]
+    u = ((tok.numel() - 1) // seq_len) * seq_len
+    return tok[:u + 1]
+
+
+def eval_val(args, model, rank, world_size, device, grad_accum_steps, val_tokens,
+             base_bytes_lut, has_leading_space_lut, is_boundary_token_lut):
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    local_batch_seqs = max(1, local_batch_tokens // args.train_seq_len)
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.no_grad():
+        for batch_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_end = min(batch_start + local_batch_seqs, seq_end)
+            raw_start = batch_start * args.train_seq_len
+            raw_end = batch_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64)
+            x, y = local[:-1].reshape(-1, args.train_seq_len), local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                batch_loss = model(x, y).detach()
+            n = float(y.numel())
+            loss_sum += batch_loss.to(torch.float64) * n
+            token_count += n
+            prev_ids, tgt_ids = x.reshape(-1), y.reshape(-1)
+            tok_bytes = base_bytes_lut[tgt_ids].to(torch.int16)
+            tok_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(torch.int16)
+            byte_count += tok_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        for t in (loss_sum, token_count, byte_count):
+            dist.all_reduce(t, op=dist.ReduceOp.SUM)
+    val_loss = loss_sum / token_count
+    bpb = (val_loss.item() / math.log(2.0)) * (token_count.item() / byte_count.item())
+    model.train()
+    return float(val_loss.item()), float(bpb), int(token_count.item()), int(byte_count.item())
+
+def main() -> None:
+    args = Hyperparameters()
+    code = Path(__file__).read_text(encoding="utf-8")
+
+    if args.matrix_optimizer != "adamw":
+        global ns_orth
+        ns_orth = torch.compile(ns_orth)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    grad_accum_steps = max(1, 8 // world_size)
+    grad_scale = 1.0 / grad_accum_steps
+
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+    os.makedirs("logs/cuda/", exist_ok=True)
+    logfile = f"logs/cuda/{args.run_id}.txt" if master_process else None
+    if master_process:
+        print(logfile)
+        # Truncate any pre-existing logfile from a previous run with the same
+        # RUN_ID so the per-RUN_ID log file isn't append-stacked across reruns.
+        if logfile:
+            open(logfile, "w", encoding="utf-8").close()
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+
+    log0(f"Python {sys.version}", console=False)
+    log0(f"PyTorch {torch.__version__}", console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    # Guard against an obvious tokenizer/vocab-size mismatch that would
+    # silently produce a wrong byte-count LUT (and therefore a wrong BPB
+    # denominator) without crashing.
+    assert sp.vocab_size() == args.vocab_size, (
+        f"tokenizer vocab_size ({sp.vocab_size()}) != args.vocab_size "
+        f"({args.vocab_size}); check TOKENIZER_PATH / VOCAB_SIZE")
+    val_tokens = ld_val(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_luts(
+        sp, args.vocab_size, device)
+
+    # --- Model ---
+    base_model = GPT(
+        vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim,
+        num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init,
+        group_size=args.bitnet_group_size, activation=args.activation_type,
+        embed_dim=args.embed_dim,
+        softcap_type=args.softcap_type, no_cache=(args.compile_mode == "reduce-overhead"),
+        rope_type=args.rope_type, yarn_max_len=args.yarn_max_len, train_seq_len=args.train_seq_len,
+    ).to(device).bfloat16()
+
+    for module in base_model.modules():
+        if isinstance(module, nn.Linear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+    if args.tie_embeddings:
+        base_model.lm_head.weight.requires_grad_(False)
+
+    torch._dynamo.config.optimize_ddp = False
+
+    compiled_model = torch.compile(base_model, mode=args.compile_mode if args.compile_mode != "default" else None)
+    use_find_unused = not args.tie_embeddings
+    model = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False,
+                find_unused_parameters=use_find_unused,
+                static_graph=not use_find_unused,
+                gradient_as_bucket_view=True) if distributed else compiled_model
+
+    # --- Optimizers ---
+    _excl = {"tok_emb.weight", "lm_head.weight"}
+    all_other_params = [(n, p) for n, p in base_model.named_parameters()
+                        if not any(eh in n for eh in _excl)]
+    matrix_params = [p for n, p in all_other_params
+                     if p.ndim == 2 and not any(pat in n for pat in CTP)]
+    scalar_params = [p for n, p in all_other_params
+                     if p.ndim < 2 or any(pat in n for pat in CTP)]
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    opt_tok = torch.optim.Adam(
+        [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+    if args.matrix_optimizer == "adamw":
+        opt_muon = torch.optim.AdamW(
+            [{"params": matrix_params, "lr": args.adam_lr, "base_lr": args.adam_lr}],
+            betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True)
+    else:
+        opt_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum,
+                        backend_steps=args.muon_backend_steps, wd=args.muon_wd)
+    for g in opt_muon.param_groups:
+        g["base_lr"] = args.matrix_lr
+    opt_scalar = torch.optim.Adam(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+    opt_head = torch.optim.Adam(
+        [{"params": [base_model.lm_head.weight], "lr": 0.0, "base_lr": 0.0}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+
+    optimizers = [opt_tok, opt_muon, opt_scalar, opt_head]
+
+    # --- Log all hyperparameters ---
+    log0("--- Hyperparameters ---", console=False)
+    log0(" ".join(f"{a}={getattr(args,a)}" for a in sorted(dir(args)) if not a.startswith("_") and a not in ("train_files","val_files") and not callable(getattr(args,a))), console=False)
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"params:{n_params} L:{args.num_layers} d:{args.model_dim} h:{args.num_heads} kv:{args.num_kv_heads} ws:{world_size} ga:{grad_accum_steps} s:{args.seed}")
+
+    # --- Data loader & helpers ---
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all():
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float):
+        if args.warmdown_fraction <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = int(args.iterations * (1.0 - args.warmdown_fraction))
+            ratio = max((args.iterations - step) / max(args.iterations * args.warmdown_fraction, 1), 0.0) if step >= warmdown_start else 1.0
+        else:
+            warmdown_ms = max_wallclock_ms * args.warmdown_fraction
+            remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+            ratio = remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+        return max(ratio, args.min_lr)
+
+    _seq_switched = False
+    _batch_switched = False
+    active_seq_len = args.seq_len_start if args.seq_len_start > 0 else args.train_seq_len
+    active_batch_tokens = args.batch_tokens_start if args.batch_tokens_start > 0 else args.train_batch_tokens
+
+    if args.skip_training:
+        log0("skip_training=1, using existing artifact final_model.quinary.ptz")
+
+    # --- Compiler warmup ---
+    if args.warmup_steps > 0 and not args.skip_training:
+        _ms = {n: t.detach().cpu().clone() for n, t in base_model.state_dict().items()}
+        _os = [copy.deepcopy(o.state_dict()) for o in optimizers]
+        model.train()
+        for ws in range(args.warmup_steps):
+            zero_grad_all()
+            for mi in range(grad_accum_steps):
+                if distributed: model.require_backward_grad_sync = mi == grad_accum_steps - 1
+                x, y = train_loader.next_batch(active_batch_tokens, active_seq_len, grad_accum_steps)
+                torch.compiler.cudagraph_mark_step_begin()
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16): loss = model(x, y)
+                (loss * grad_scale).backward()
+            for o in optimizers: o.step()
+            zero_grad_all()
+            log0(f"warmup:{ws+1}/{args.warmup_steps}")
+        base_model.load_state_dict(_ms, strict=True)
+        for o, s in zip(optimizers, _os): o.load_state_dict(s)
+        zero_grad_all()
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # --- Main training loop ---
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    train_loss = torch.zeros((), device=device)
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    step = 0
+
+    while not args.skip_training:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        if last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0):
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb, val_tok_count, val_byte_count = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut)
+            tstats = quin_stats(base_model, group_size=args.bitnet_group_size)
+            log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                 f"train_time:{training_time_ms:.0f}ms zero_frac:{tstats['zero_frac']:.3f} "
+                 f"eval_tokens:{val_tok_count} eval_bytes:{val_byte_count}")
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}")
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+
+        # Sequence length schedule
+        if args.seq_len_start > 0 and not _seq_switched:
+            if max_wallclock_ms is not None:
+                should_switch_seq = elapsed_ms >= args.seq_schedule_fraction * max_wallclock_ms
+            else:
+                should_switch_seq = step >= int(args.iterations * args.seq_schedule_fraction)
+            if should_switch_seq:
+                active_seq_len = args.train_seq_len
+                _seq_switched = True
+                torch._dynamo.reset()
+                train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+                log0(f"step:{step} seq_len_switch:{args.seq_len_start}->{active_seq_len}")
+
+        # Batch size schedule
+        if args.batch_tokens_start > 0 and not _batch_switched:
+            if max_wallclock_ms is not None:
+                should_switch_batch = elapsed_ms >= args.batch_schedule_fraction * max_wallclock_ms
+            else:
+                should_switch_batch = step >= int(args.iterations * args.batch_schedule_fraction)
+            if should_switch_batch:
+                active_batch_tokens = args.train_batch_tokens
+                _batch_switched = True
+                log0(f"step:{step} batch_switch:{args.batch_tokens_start}->{active_batch_tokens}")
+
+        zero_grad_all()
+        train_loss.zero_()
+
+        for micro in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro == grad_accum_steps - 1
+            x, y = train_loader.next_batch(active_batch_tokens, active_seq_len, grad_accum_steps)
+            torch.compiler.cudagraph_mark_step_begin()
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                loss = model(x, y)
+            train_loss.add_(loss.detach())
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        # Muon momentum warmup (skip when AdamW is the matrix optimizer)
+        if args.matrix_optimizer != "adamw":
+            frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0
+            for g in opt_muon.param_groups:
+                g["momentum"] = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+
+        # LR scheduling
+        for opt in optimizers:
+            for g in opt.param_groups:
+                g["lr"] = g["base_lr"] * scale
+            opt.step()
+        zero_grad_all()
+        step += 1
+        approx_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        
+        if args.train_log_every > 0 and step % args.train_log_every == 0:
+            log0(f"step:{step}/{args.iterations} loss:{train_loss.item():.4f} t:{approx_ms:.0f}ms avg:{approx_ms/step:.1f}ms")
+        if args.churn_log_every > 0 and step % args.churn_log_every == 0:
+            log0(f"step:{step} churn:{churn_fn(base_model, args.bitnet_group_size):.4f} zero:{quin_stats(base_model, args.bitnet_group_size)['zero_frac']:.3f}")
+
+        # Wallclock cap sync
+        if stop_after_step is None and max_wallclock_ms is not None and step % 10 == 0:
+            reached_cap = approx_ms >= max_wallclock_ms
+            if distributed:
+                cap_t = torch.tensor(int(reached_cap), device=device)
+                dist.all_reduce(cap_t, op=dist.ReduceOp.MAX)
+                reached_cap = bool(cap_t.item())
+            if reached_cap:
+                stop_after_step = step
+
+    # --- Serialization ---
+    if master_process and not args.skip_training:
+        sd = base_model.state_dict()
+        if base_model.tie_embeddings:
+            sd.pop("lm_head.weight", None)
+
+        log0("serialize: start")
+        t_qsd = time.perf_counter()
+        q_obj, q_stats = q_sd(sd, group_size=args.bitnet_group_size,
+                              scale_quant_bits=args.scale_quant_bits)
+        qsd_time = time.perf_counter() - t_qsd
+        log0(f"serialize: q_sd in {qsd_time:.1f}s "
+             f"(quinary={q_stats['quinary_params']/1e6:.2f}M params/{q_stats['quinary_bytes']/1e6:.2f}MB, "
+             f"fp={q_stats['fp_params']/1e6:.2f}M params/{q_stats['fp_bytes']/1e6:.2f}MB)")
+
+        t_save = time.perf_counter()
+        buf = io.BytesIO()
+        torch.save(q_obj, buf)
+        raw_bytes = len(buf.getvalue())
+        log0(f"serialize: torch.save raw={raw_bytes/1e6:.2f}MB in {time.perf_counter()-t_save:.1f}s")
+
+        t_compress = time.perf_counter()
+        final_blob, per_stream_audit = _serialize_per_stream_v2(q_obj, level=9)
+        compress_time = time.perf_counter() - t_compress
+        artifact_bytes = len(final_blob)
+        log0(per_stream_audit)
+        log0(f"serialize: per_stream_v2 {raw_bytes/1e6:.2f}MB -> {artifact_bytes/1e6:.2f}MB "
+             f"(ratio {artifact_bytes/raw_bytes:.1%}, saved {(raw_bytes-artifact_bytes)/1e6:.2f}MB) "
+             f"in {compress_time:.1f}s")
+
+        with open("final_model.quinary.ptz", "wb") as f:
+            f.write(final_blob)
+
+        code_bytes = len(code.encode("utf-8"))
+
+        total = artifact_bytes + code_bytes
+        log0(f"artifact:{artifact_bytes/1e6:.2f}MB quinary:{q_stats['quinary_params']}({q_stats['quinary_bytes']}B) fp:{q_stats['fp_params']}({q_stats['fp_bytes']}B) code:{code_bytes}")
+        log0(f"budget:{total}/{16000000} ({total/1e6:.2f}/{16.00:.2f}MB) {'FITS' if total <= 16000000 else 'OVER'}")
+
+    # --- All ranks load roundtrip weights and evaluate ---
+    if distributed:
+        dist.barrier()
+
+    with open("final_model.quinary.ptz", "rb") as f:
+        loaded = _load_artifact(f.read())
+    missing, unexpected = base_model.load_state_dict(deq_sd(loaded), strict=False)
+    # The only "missing" key we expect is `lm_head.weight` when tied, since
+    # q_sd drops it from the saved state-dict. Any other missing/unexpected
+    # key is a serialization-roundtrip bug and should fail loudly.
+    expected_missing = {"lm_head.weight"} if base_model.tie_embeddings else set()
+    assert set(missing) <= expected_missing, f"unexpected missing keys after artifact load: {set(missing) - expected_missing}"
+    assert not unexpected, f"unexpected extra keys after artifact load: {unexpected}"
+    torch._dynamo.reset()
+
+    q_val_loss, q_val_bpb, q_tok_count, q_byte_count = eval_val(
+        args, model, rank, world_size, device, grad_accum_steps,
+        val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut)
+    log0(f"final_quinary_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+         f"eval_tokens:{q_tok_count} eval_bytes:{q_byte_count}")
+
+    # --- Score-first chunk-based CTP TTT ---
+    if args.ttt_steps > 0:
+        torch.cuda.synchronize()
+        t_ttt = time.perf_counter()
+        seq_len = args.train_seq_len
+        # TTT_STRIDE: sliding-window step used to slice the val stream into TTT
+        # examples. Hardcoded at 16 (canonical sp16384 quinary submission); a
+        # smaller stride yields more overlapping windows but more compute.
+        stride = 16
+        ttt_chunk_tokens = args.ttt_tokens if args.ttt_tokens > 0 else 32768
+        ttt_epochs = args.ttt_steps
+        batch_seqs = 32
+        total_tokens = val_tokens.numel() - 1
+
+        # Select TTT params (CTP only — quinary weights are frozen)
+        for p in base_model.parameters():
+            p.requires_grad_(False)
+        ttt_params = []
+        for name, p in base_model.named_parameters():
+            if any(pat in name for pat in CTP):
+                p.requires_grad_(True)
+                ttt_params.append(p)
+        n_ttt = sum(p.numel() for p in ttt_params)
+        ttt_opt = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=0.9)
+        for pg in ttt_opt.param_groups:
+            pg["initial_lr"] = pg["lr"]
+        log0(f"ttt: {n_ttt} CTP params, lr={args.ttt_lr}")
+
+        # Assign sliding windows to chunks
+        context_size = seq_len - stride
+        window_starts = [ws for ws in range(0, total_tokens, stride)
+                         if ws + context_size < total_tokens]
+        num_chunks = (total_tokens + ttt_chunk_tokens - 1) // ttt_chunk_tokens
+        chunk_windows = [[] for _ in range(num_chunks)]
+        for ws in window_starts:
+            s = 0 if ws == 0 else context_size
+            ci = min((ws + s) // ttt_chunk_tokens, num_chunks - 1)
+            chunk_windows[ci].append(ws)
+        log0(f"ttt: {n_ttt} CTP params, {num_chunks} chunks, {ttt_epochs} epochs, lr={args.ttt_lr}")
+
+        loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+        token_count = torch.zeros((), device=device, dtype=torch.float64)
+        byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+        # Recompile for TTT (find_unused_parameters needed with frozen params)
+        torch._dynamo.reset()
+        compiled_ttt = torch.compile(base_model, mode=args.compile_mode if args.compile_mode != "default" else None)
+
+        for ci in range(num_chunks):
+            windows = chunk_windows[ci]
+            if not windows:
+                continue
+            chunk_start = ci * ttt_chunk_tokens
+            chunk_end = min((ci + 1) * ttt_chunk_tokens, total_tokens)
+            my_s = len(windows) * rank // world_size
+            my_e = len(windows) * (rank + 1) // world_size
+            my_windows = windows[my_s:my_e]
+
+            # Phase 1: SCORE (no_grad, compiled)
+            base_model.eval()
+            with torch.no_grad():
+                for bi in range(0, len(my_windows), batch_seqs):
+                    batch_ws = my_windows[bi:bi + batch_seqs]
+                    bsz = len(batch_ws)
+                    x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                    y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                    wlens = []
+                    for i, ws in enumerate(batch_ws):
+                        we = min(ws + seq_len, total_tokens)
+                        wlen = we - ws
+                        wlens.append(wlen)
+                        chunk_tok = val_tokens[ws:we + 1].to(dtype=torch.int64, device=device)
+                        x_batch[i, :wlen] = chunk_tok[:-1]
+                        y_batch[i, :wlen] = chunk_tok[1:]
+                    with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                        nll = compiled_ttt(x_batch, y_batch, reduction="none").detach()
+                    for i, ws in enumerate(batch_ws):
+                        wlen = wlens[i]
+                        s = 0 if ws == 0 else context_size
+                        scored_nll = nll[i, s:wlen].to(torch.float64)
+                        loss_sum += scored_nll.sum()
+                        token_count += float(wlen - s)
+                        tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen]
+                        tb = base_bytes_lut[tgt].to(torch.float64)
+                        tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                        byte_count += tb.sum()
+
+            # Phase 2: TRAIN on chunk (score-first: already scored)
+            if ci < num_chunks - 1 and ttt_epochs > 0:
+                base_model.train()
+                chunk_seqs = (chunk_end - chunk_start) // seq_len
+                if chunk_seqs > 0:
+                    cos_mul = 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1)))
+                    for pg in ttt_opt.param_groups:
+                        pg["lr"] = pg.get("initial_lr", pg["lr"]) * cos_mul
+                    my_seq_s = chunk_seqs * rank // world_size
+                    my_seq_e = chunk_seqs * (rank + 1) // world_size
+                    for _ep in range(ttt_epochs):
+                        for bs in range(0, my_seq_e - my_seq_s, batch_seqs):
+                            be = min(bs + batch_seqs, my_seq_e - my_seq_s)
+                            start_tok = chunk_start + (my_seq_s + bs) * seq_len
+                            end_tok = chunk_start + (my_seq_s + be) * seq_len + 1
+                            if end_tok > val_tokens.numel():
+                                continue
+                            local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64)
+                            x = local[:-1].reshape(-1, seq_len)
+                            y = local[1:].reshape(-1, seq_len)
+                            ttt_opt.zero_grad(set_to_none=True)
+                            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                                loss = compiled_ttt(x, y)
+                            loss.backward()
+                            if distributed:
+                                for p in ttt_params:
+                                    if p.grad is not None:
+                                        dist.all_reduce(p.grad, op=dist.ReduceOp.AVG)
+                            torch.nn.utils.clip_grad_norm_(ttt_params, 1.0)
+                            ttt_opt.step()
+
+            if master_process and ci % max(1, num_chunks // 5) == 0:
+                log0(f"ttt chunk:{ci+1}/{num_chunks}")
+
+        if distributed:
+            for t in (loss_sum, token_count, byte_count):
+                dist.all_reduce(t, op=dist.ReduceOp.SUM)
+        ttt_val_loss = (loss_sum / token_count).item()
+        ttt_bpb = (ttt_val_loss / math.log(2.0)) * (token_count.item() / byte_count.item())
+        for p in base_model.parameters():
+            p.requires_grad_(True)
+        torch._dynamo.reset()
+        torch.cuda.synchronize()
+        ttt_time_ms = 1000.0 * (time.perf_counter() - t_ttt)
+        log0(f"ttt_eval val_loss:{ttt_val_loss:.4f} val_bpb:{ttt_bpb:.4f} "
+             f"time:{ttt_time_ms:.0f}ms "
+             f"eval_tokens:{int(token_count.item())} eval_bytes:{int(byte_count.item())}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+if __name__ == "__main__":
+    main()
+====================================================================================================
+Python 3.13.13 | packaged by Anaconda, Inc. | (main, Apr 14 2026, 06:19:41) [GCC 14.3.0]
+PyTorch 2.10.0+cu128
+--- Hyperparameters ---
+activation_type=relu2 adam_eps=1e-08 adam_lr=0.05 adam_wd=0.05 batch_schedule_fraction=0.33 batch_tokens_start=0 beta1=0.9 beta2=0.95 bitnet_group_size=192 churn_log_every=0 compile_mode=default data_path=./data/canonical/datasets/fineweb10B_sp16384 embed_dim=380 embed_lr=0.6 fp_storage=True grad_clip_norm=0.0 head_lr=0.02 iterations=10000 logit_softcap=10.0 matrix_lr=0.035 matrix_optimizer=muon max_wallclock_seconds=599.0 min_lr=0.0 mlp_mult=4 model_dim=576 muon_backend_steps=3 muon_momentum=0.95 muon_momentum_warmup_start=0.85 muon_momentum_warmup_steps=500 muon_wd=0.0 num_heads=6 num_kv_heads=3 num_layers=10 qk_gain_init=5.0 rope_base=5000.0 rope_type=yarn run_id=quinary_seed1337 scalar_lr=0.02 scale_quant_bits=5 seed=1337 seq_len_start=0 seq_schedule_fraction=0.0 skip_training=False softcap_type=poly tie_embeddings=1 tied_embed_init_std=0.005 tied_embed_lr=0.02 tokenizer_path=./data/canonical/tokenizers/fineweb_16384_bpe.model train_batch_tokens=524288 train_log_every=1000 train_seq_len=1024 ttt_lr=0.005 ttt_steps=3 ttt_tokens=32768 val_batch_size=524288 val_loss_every=0 vocab_size=16384 warmdown_fraction=0.2 warmup_steps=5 yarn_max_len=2048
+params:52828668 L:10 d:576 h:6 kv:3 ws:8 ga:1 s:1337
+warmup:1/5
+warmup:2/5
+warmup:3/5
+warmup:4/5
+warmup:5/5
+step:1000/10000 loss:3.8318 t:77272ms avg:77.3ms
+step:2000/10000 loss:3.5168 t:154418ms avg:77.2ms
+step:3000/10000 loss:3.4458 t:231363ms avg:77.1ms
+step:4000/10000 loss:3.2941 t:308098ms avg:77.0ms
+step:5000/10000 loss:3.5063 t:384842ms avg:77.0ms
+step:6000/10000 loss:3.5410 t:461611ms avg:76.9ms
+step:7000/10000 loss:3.4814 t:538278ms avg:76.9ms
+step:7800/10000 val_loss:3.2719 val_bpb:1.1606 train_time:599689ms zero_frac:0.261 eval_tokens:37146624 eval_bytes:151078879
+stopping_early: wallclock_cap train_time:599689ms step:7800/10000
+serialize: start
+serialize: q_sd in 0.1s (quinary=36.50M params/12.36MB, fp=6.90M params/7.11MB)
+serialize: torch.save raw=19.53MB in 0.0s
+per_stream_v2: meta=0.14MB, 63 bulk payloads (lzma=62, lrzip=1; raw=3, base5=1, base5_T=37, bitmask_T=22), qkv_splits=10, total=15.64MB
+serialize: per_stream_v2 19.53MB -> 15.64MB (ratio 80.1%, saved 3.89MB) in 35.7s
+artifact:15.64MB quinary:36495360(12355360B) fp:6896124(7114168B) code:79272
+budget:15721124/16000000 (15.72/16.00MB) FITS
+final_quinary_roundtrip val_loss:3.2795 val_bpb:1.1633 eval_tokens:37146624 eval_bytes:151078879
+ttt: 42364 CTP params, lr=0.005
+ttt: 42364 CTP params, 1134 chunks, 3 epochs, lr=0.005
+ttt chunk:1/1134
+ttt chunk:227/1134
+ttt chunk:453/1134
+ttt chunk:679/1134
+ttt chunk:905/1134
+ttt chunk:1131/1134
+ttt_eval val_loss:3.2120 val_bpb:1.1394 time:216087ms eval_tokens:37146624 eval_bytes:151078879
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/quinary_seed42.txt b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/quinary_seed42.txt
new file mode 100644
index 0000000000..18bc16852a
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/quinary_seed42.txt
@@ -0,0 +1,1689 @@
+"Quinary training script for OpenAI's Parameter Golf Challenge. Based on ternary submission by Ciprian-Florin Ifrim (24 March 2026). Quinary: {-2,-1,0,+1,+2} with base-5 packing (3 quins/byte = 2.667 bpw vs ternary 1.6 bpw)."
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import tempfile
+import time
+import lzma
+from pathlib import Path
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+from flash_attn_interface import flash_attn_func
+
+def _e(k, d, t=str):
+    v = os.environ.get(k, str(d))
+    if t == bool: return bool(int(v))
+    return t(v)
+
+class Hyperparameters:
+    # Defaults below match the canonical SP16384 quinary submission config —
+    # i.e. exactly what `run.sh` passes through to torchrun. A bare
+    # `torchrun --standalone --nproc_per_node=8 train_gpt.py` (no env vars)
+    # therefore reproduces the submission. `run.sh` is still the
+    # documentation-of-record for the canonical config; these defaults are
+    # mirrored from it so the two never silently disagree.
+    data_path = _e("DATA_PATH", "./data/canonical/datasets/fineweb10B_sp16384")
+    # [0-9] prefix matches only NNNNNN-suffixed shard files, ignoring any
+    # sibling parallel-array files that might share the prefix.
+    train_files = os.path.join(data_path, "fineweb_train_[0-9]*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_[0-9]*.bin")
+    tokenizer_path = _e("TOKENIZER_PATH", "./data/canonical/tokenizers/fineweb_16384_bpe.model")
+    run_id = os.environ.get("RUN_ID", f"run_{int(time.time())}")
+    seed = _e("SEED", 42, int)
+    compile_mode = _e("COMPILE_MODE", "default")
+    val_batch_size = _e("VAL_BATCH_SIZE", 524288, int)
+    val_loss_every = _e("VAL_LOSS_EVERY", 0, int)  # 0 = no in-training val
+    train_log_every = _e("TRAIN_LOG_EVERY", 1000, int)
+    iterations = _e("ITERATIONS", 10000, int)
+    warmdown_fraction = _e("WARMDOWN_FRACTION", 0.2, float)
+    min_lr = _e("MIN_LR", 0.0, float)  # floor on the LR multiplier (fraction of base LR)
+    warmup_steps = _e("WARMUP_STEPS", 5, int)
+    train_batch_tokens = _e("TRAIN_BATCH_TOKENS", 524288, int)
+    train_seq_len = _e("TRAIN_SEQ_LEN", 1024, int)
+    max_wallclock_seconds = _e("MAX_WALLCLOCK_SECONDS", 599.0, float)
+    vocab_size = _e("VOCAB_SIZE", 16384, int)
+    num_layers = _e("NUM_LAYERS", 10, int)
+    num_kv_heads = _e("NUM_KV_HEADS", 3, int)
+    model_dim = _e("MODEL_DIM", 576, int)
+    num_heads = _e("NUM_HEADS", 6, int)
+    mlp_mult = _e("MLP_MULT", 4, int)
+    tie_embeddings = _e("TIE_EMBEDDINGS", 1, int)
+    rope_base = _e("ROPE_BASE", 5000.0, float)
+    rope_type = _e("ROPE_TYPE", "yarn")
+    yarn_max_len = _e("YARN_MAX_LEN", 2048, int)
+    logit_softcap = _e("LOGIT_SOFTCAP", 10.0, float)
+    softcap_type = _e("SOFTCAP_TYPE", "poly")
+    tied_embed_init_std = _e("TIED_EMBED_INIT_STD", 0.005, float)
+    qk_gain_init = _e("QK_GAIN_INIT", 5.0, float)
+    activation_type = _e("ACTIVATION", "relu2")
+    embed_dim = _e("EMBED_DIM", 380, int)
+    embed_lr = _e("EMBED_LR", 0.6, float)
+    head_lr = _e("HEAD_LR", 0.02, float)
+    adam_lr = _e("ADAM_LR", 0.05, float)
+    adam_wd = _e("ADAM_WD", 0.05, float)
+    tied_embed_lr = _e("TIED_EMBED_LR", 0.02, float)
+    seq_len_start = _e("SEQ_LEN_START", 0, int)
+    seq_schedule_fraction = _e("SEQ_SCHEDULE_FRACTION", 0.0, float)
+    batch_tokens_start = _e("BATCH_TOKENS_START", 0, int)
+    batch_schedule_fraction = _e("BATCH_SCHEDULE_FRACTION", 0.33, float)
+    churn_log_every = _e("CHURN_LOG_EVERY", 0, int)
+    matrix_lr = _e("MATRIX_LR", 0.035, float)
+    scalar_lr = _e("SCALAR_LR", 0.02, float)
+    muon_momentum = _e("MUON_MOMENTUM", 0.95, float)
+    muon_backend_steps = _e("MUON_BACKEND_STEPS", 3, int)
+    muon_wd = _e("MUON_WD", 0.0, float)
+    matrix_optimizer = _e("MATRIX_OPTIMIZER", "muon")
+    muon_momentum_warmup_start = _e("MUON_MOMENTUM_WARMUP_START", 0.85, float)
+    muon_momentum_warmup_steps = _e("MUON_MOMENTUM_WARMUP_STEPS", 500, int)
+    beta1 = _e("BETA1", 0.9, float)
+    beta2 = _e("BETA2", 0.95, float)
+    adam_eps = _e("ADAM_EPS", 1e-8, float)
+    grad_clip_norm = _e("GRAD_CLIP_NORM", 0.0, float)
+    bitnet_group_size = _e("BITNET_GROUP_SIZE", 192, int)
+    ttt_steps = _e("TTT_STEPS", 3, int)
+    ttt_lr = _e("TTT_LR", 0.005, float)
+    ttt_tokens = _e("TTT_TOKENS", 32768, int)
+    skip_training = _e("SKIP_TRAINING", 0, bool)
+    scale_quant_bits = _e("SCALE_QUANT_BITS", 5, int)  # 5-bit log-delta scale quantization
+    fp_storage = True  # FP8 storage for non-quinary (small) tensors
+
+# Calibration / fp16-stored scalar+vector params adapted by score-first TTT.
+# `scale_correction` was previously listed here but its gradient is blocked by
+# the STE detach in QuinaryLinear.forward, so including it as a TTT target was
+# a no-op. Excluded from the CTP tuple so the TTT optimizer reflects what
+# actually receives gradients (~42k params, not 232k). The parameter still
+# exists in the state-dict and is stored fp16 in the artifact (via the ndim<2
+# bucket); it is just not selected for TTT adaptation.
+CTP = ("attn_scale","mlp_scale","resid_mix","q_gain","skip_weights","vocab_bias")
+
+def pack_quinary(q: Tensor):
+    f = (q.reshape(-1).to(torch.int8) + 2).numpy()  # {-2..2} -> {0..4}
+    n = len(f)
+    p = (3 - n % 3) % 3
+    if p: f = np.concatenate([f, np.zeros(p, dtype=np.int8)])
+    g = f.reshape(-1, 3).astype(np.uint8)
+    return (g[:,0] + g[:,1]*5 + g[:,2]*25).tobytes(), n  # max = 4+20+100 = 124
+
+def unpack_quinary(data: bytes, n: int) -> Tensor:
+    v = np.frombuffer(data, dtype=np.uint8).astype(np.int16)
+    t = np.zeros((len(v), 3), dtype=np.int8)
+    for i in range(3): t[:,i] = v % 5; v //= 5
+    return torch.from_numpy(t.reshape(-1)[:n].astype(np.int8) - 2)
+
+
+def pack_quinary_bitmask(q) -> tuple[bytes, int, int]:
+    """Pack quinary symbols ∈ {-2..2} as three concatenated bit-planes.
+
+    Layout (big-endian within bytes via numpy.packbits):
+      [zero_mask:    ceil(n_total / 8) bytes — 1 bit/symbol, 1=zero]
+      [sign_bits:    ceil(n_nonzero / 8) bytes — 1 bit/nonzero, 1=negative]
+      [mag2_bits:    ceil(n_nonzero / 8) bytes — 1 bit/nonzero, 1=|symbol|==2]
+
+    Returns: (concat_bytes, n_total, n_nonzero).
+    Each plane has homogeneous bit statistics so the downstream compressor
+    can model them independently rather than fighting a multimodal mixture.
+    """
+    if isinstance(q, torch.Tensor):
+        a = q.reshape(-1).to(torch.int8).numpy()
+    else:
+        a = np.asarray(q, dtype=np.int8).reshape(-1)
+    n_total = a.size
+    nz_mask = (a != 0)                            # bool, len n_total
+    n_nonzero = int(nz_mask.sum())
+    nz_vals = a[nz_mask]                          # int8, len n_nonzero
+    sign_bits = (nz_vals < 0).astype(np.uint8)    # 1 = negative
+    mag2_bits = (np.abs(nz_vals) == 2).astype(np.uint8)
+    zero_packed = np.packbits((~nz_mask).astype(np.uint8))  # 1 bit means "this symbol is zero"
+    sign_packed = np.packbits(sign_bits)
+    mag2_packed = np.packbits(mag2_bits)
+    return (zero_packed.tobytes() + sign_packed.tobytes() + mag2_packed.tobytes(),
+            n_total, n_nonzero)
+
+
+def unpack_quinary_bitmask(data: bytes, n_total: int, n_nonzero: int) -> Tensor:
+    """Inverse of pack_quinary_bitmask. Returns int8 tensor of length n_total."""
+    z_len = (n_total + 7) // 8
+    s_len = (n_nonzero + 7) // 8
+    m_len = (n_nonzero + 7) // 8
+    expected = z_len + s_len + m_len
+    if len(data) != expected:
+        raise ValueError(f"bitmask data size {len(data)} != expected {expected} "
+                         f"(n_total={n_total}, n_nonzero={n_nonzero})")
+    z_bytes = np.frombuffer(data[:z_len], dtype=np.uint8)
+    s_bytes = np.frombuffer(data[z_len:z_len + s_len], dtype=np.uint8)
+    m_bytes = np.frombuffer(data[z_len + s_len:], dtype=np.uint8)
+    is_zero = np.unpackbits(z_bytes)[:n_total].astype(bool)
+    sign = np.unpackbits(s_bytes)[:n_nonzero].astype(bool)
+    mag2 = np.unpackbits(m_bytes)[:n_nonzero].astype(bool)
+    nz_vals = np.where(mag2, 2, 1).astype(np.int8)
+    nz_vals = np.where(sign, -nz_vals, nz_vals)
+    out = np.zeros(n_total, dtype=np.int8)
+    out[~is_zero] = nz_vals
+    return torch.from_numpy(out)
+
+
+# Artifact archive: layout-aware per-stream v2 (header byte 0x03).
+#
+#   - For each quinary tensor: screen the 4 layouts {base5, base5_T,
+#     bitmask, bitmask_T} by LZMA9-compressed size, then run LZMA9 vs
+#     lrzip-zpaq -L9 only on the winning layout. Bounded heuristic
+#     with an LZMA floor — *not* an exhaustive 4×2 search.
+#   - For c_qkv.weight: split rows into Q / K / V sub-payloads each chosen
+#     independently (Q, K, V have different trained distributions).
+#   - For other bulk fields (FP8 embeddings, large fp16 tensors): torch.save
+#     the value and compress with min(lzma, lrzip).
+#   - Robust to the seed-dependent lrzip cliff observed on full-blob
+#     compression: even if lrzip ZPAQ underperforms on one tensor's bytes,
+#     lzma takes over for that tensor specifically.
+_COMPRESSOR_LZMA = 0
+_COMPRESSOR_LRZIP_ZPAQ = 1
+_COMPRESSOR_PER_STREAM_V2 = 3
+
+# Threshold for treating a value as "bulk" (compressed independently).
+_PER_STREAM_BULK_BYTES = 64 * 1024
+
+# v2 layout IDs — what kind of body is stored in a bulk payload.
+_LAYOUT_RAW         = 0  # opaque bytes (torch.save output for non-quinary fields)
+_LAYOUT_Q_BASE5     = 1  # base-5 packed quinary symbols, canonical row-major order
+_LAYOUT_Q_BASE5_T   = 2  # base-5 packed quinary symbols, transposed (column-major)
+_LAYOUT_Q_BITMASK   = 3  # bitmask packed (zero|sign|mag2), canonical
+_LAYOUT_Q_BITMASK_T = 4  # bitmask packed, transposed
+_LAYOUT_NAMES = {
+    _LAYOUT_RAW: "raw",
+    _LAYOUT_Q_BASE5: "base5",
+    _LAYOUT_Q_BASE5_T: "base5_T",
+    _LAYOUT_Q_BITMASK: "bitmask",
+    _LAYOUT_Q_BITMASK_T: "bitmask_T",
+}
+
+# v2 sentinel for metadata refs (distinct string from v1 to avoid version confusion).
+_BULK_SENTINEL_V2 = "__BULK_REF_V2__"
+
+
+def _lrzip_compress_bytes(data: bytes, level: int = 9) -> bytes:
+    """Compress raw bytes via `lrzip -z` (ZPAQ). Returns body only (no header)."""
+    with tempfile.NamedTemporaryFile(delete=False) as f:
+        f.write(data); in_path = f.name
+    out_path = in_path + ".lrz"
+    try:
+        try:
+            subprocess.run(["lrzip", "-z", "-L", str(level), "-q", "-f", in_path],
+                           check=True, capture_output=True)
+        except FileNotFoundError as e:
+            raise RuntimeError("lrzip binary not found; run `apt-get install lrzip`") from e
+        with open(out_path, "rb") as f:
+            return f.read()
+    finally:
+        for p in (in_path, out_path):
+            try: os.unlink(p)
+            except FileNotFoundError: pass
+
+
+def _lrzip_decompress_bytes(body: bytes) -> bytes:
+    """Decompress an lrzip ZPAQ body (no header)."""
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".lrz") as f:
+        f.write(body); in_path = f.name
+    out_path = in_path[:-4]
+    try:
+        try:
+            subprocess.run(["lrzip", "-d", "-q", "-f", in_path],
+                           check=True, capture_output=True)
+        except FileNotFoundError as e:
+            raise RuntimeError("lrzip binary not found; required to load this artifact") from e
+        with open(out_path, "rb") as f:
+            return f.read()
+    finally:
+        for p in (in_path, out_path):
+            try: os.unlink(p)
+            except FileNotFoundError: pass
+
+
+def _pick_best_compressor(data: bytes, level: int = 9, has_lrzip: bool = True) -> tuple[int, bytes]:
+    """Try lzma + (optionally) lrzip; return (method_id, body) for the smaller.
+
+    This is the per-stream robustness: even if one compressor cliffs on a
+    given byte distribution (the seed=7 issue), the other usually doesn't.
+    Any failure (missing binary, runtime error, broken lrzip backend) on
+    lrzip is silently skipped — lzma is the always-available floor.
+    """
+    candidates: list[tuple[int, bytes]] = [(_COMPRESSOR_LZMA, lzma.compress(data, preset=level))]
+    if has_lrzip:
+        try:
+            candidates.append((_COMPRESSOR_LRZIP_ZPAQ, _lrzip_compress_bytes(data, level)))
+        except (RuntimeError, subprocess.CalledProcessError, OSError):
+            pass
+    return min(candidates, key=lambda c: len(c[1]))
+
+
+def _is_bulk(value) -> bool:
+    """Decide whether a state_dict field should be peeled off for per-stream compression."""
+    if isinstance(value, (bytes, bytearray)):
+        return len(value) >= _PER_STREAM_BULK_BYTES
+    if isinstance(value, torch.Tensor):
+        return value.numel() * value.element_size() >= _PER_STREAM_BULK_BYTES
+    return False
+
+
+def _load_artifact(blob: bytes) -> dict:
+    """Load a per-stream v2 archive (header byte 0x03) into a state_dict."""
+    return _deserialize_per_stream_v2(blob)
+
+
+# ---------------------------------------------------------------------------
+# v2 archive: layout-aware per-stream compression
+# ---------------------------------------------------------------------------
+
+def _gen_quinary_layout_candidates(symbols_2d: np.ndarray) -> list[tuple[int, bytes, dict]]:
+    """For a 2D int8 symbol matrix in {-2..2}, return a list of
+    (layout_id, body_bytes, layout_meta) tuples — every supported layout.
+
+    layout_meta carries the per-layout fields needed at deserialize time
+    (n_quins, n_nonzero, etc.).
+    """
+    rows, cols = symbols_2d.shape
+    flat = symbols_2d.reshape(-1)
+    transposed = symbols_2d.T.reshape(-1).copy()  # contiguous transpose
+
+    # base-5 canonical
+    body_b5, n_b5 = pack_quinary(torch.from_numpy(flat))
+    # base-5 transposed
+    body_b5_t, n_b5_t = pack_quinary(torch.from_numpy(transposed))
+    # bitmask canonical
+    body_bm, nt_bm, nz_bm = pack_quinary_bitmask(torch.from_numpy(flat))
+    # bitmask transposed
+    body_bm_t, nt_bm_t, nz_bm_t = pack_quinary_bitmask(torch.from_numpy(transposed))
+
+    return [
+        (_LAYOUT_Q_BASE5,     body_b5,   {"n_quins": n_b5}),
+        (_LAYOUT_Q_BASE5_T,   body_b5_t, {"n_quins": n_b5_t}),
+        (_LAYOUT_Q_BITMASK,   body_bm,   {"n_total": nt_bm, "n_nonzero": nz_bm}),
+        (_LAYOUT_Q_BITMASK_T, body_bm_t, {"n_total": nt_bm_t, "n_nonzero": nz_bm_t}),
+    ]
+
+
+def _layout_to_canonical_bytes(layout: int, body: bytes, rows: int, cols: int,
+                               layout_meta: dict) -> bytes:
+    """Inverse of _gen_quinary_layout_candidates: take a body in one of the
+    layout encodings and return the canonical base-5-packed bytes that
+    `deq_sd` expects in entry["packed"]."""
+    if layout == _LAYOUT_Q_BASE5:
+        # Already canonical.
+        return body
+    if layout == _LAYOUT_Q_BASE5_T:
+        symbols_t = unpack_quinary(body, layout_meta["n_quins"]).numpy()
+        symbols = symbols_t.reshape(cols, rows).T.reshape(-1).copy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    if layout == _LAYOUT_Q_BITMASK:
+        symbols = unpack_quinary_bitmask(body, layout_meta["n_total"], layout_meta["n_nonzero"]).numpy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    if layout == _LAYOUT_Q_BITMASK_T:
+        symbols_t = unpack_quinary_bitmask(body, layout_meta["n_total"], layout_meta["n_nonzero"]).numpy()
+        symbols = symbols_t.reshape(cols, rows).T.reshape(-1).copy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    raise ValueError(f"unknown layout id {layout}")
+
+
+def _qkv_split_sizes(name: str, rows: int, cols: int) -> tuple[int, int] | None:
+    """For a c_qkv weight, derive (q_rows, kv_rows) row-counts.
+
+    Standard transformer convention: q_size = num_heads * head_dim = model_dim
+    (the square attention assumption). So q_rows == cols. The remaining rows
+    are split equally between K and V: kv_rows = (rows - cols) // 2.
+
+    Returns None if name doesn't look like c_qkv or the ratio doesn't decompose
+    cleanly (in which case we don't split, just compress as one tensor).
+    """
+    if not name.endswith("c_qkv.weight"):
+        return None
+    if rows <= cols:
+        return None  # not a GQA shape; or single-head case where Q==K==V
+    extra = rows - cols  # 2 * kv_rows
+    if extra % 2 != 0:
+        return None
+    kv_rows = extra // 2
+    q_rows = cols
+    if q_rows + 2 * kv_rows != rows:
+        return None
+    return q_rows, kv_rows
+
+
+def _serialize_per_stream_v2(state_dict: dict, level: int = 9) -> tuple[bytes, str]:
+    """v2 archive: layout-aware per-stream compression.
+
+    For each quinary entry, generate up to 4 layout candidates
+    (base5, base5_T, bitmask, bitmask_T), screen them by LZMA9-compressed
+    size, then compress the winning layout with min(LZMA9, lrzip-zpaq).
+    For c_qkv weights, the row-block is split into Q/K/V sub-payloads
+    handled independently. All other bulk fields use the v1 raw-bytes path.
+
+    This is a bounded-cost heuristic with an LZMA floor — *not* an
+    exhaustive 4×2 search. It can in principle miss a (layout, compressor)
+    pair where the LZMA-screen-loser would have won under lrzip; in
+    practice this is rare on this stack and the LZMA floor caps the
+    worst case at the canonical base5+LZMA encoding.
+
+    Returns (archive_bytes, audit_string) so the caller can route the
+    audit line through the run's logger.
+    """
+    from shutil import which
+    has_lrzip = which("lrzip") is not None
+
+    bulk_bodies: list[tuple[int, bytes]] = []  # (compressor_method, compressed_body)
+    metadata: dict = {}
+    audit_lines: list[str] = []
+    layout_counts = {n: 0 for n in _LAYOUT_NAMES.values()}
+    method_counts = {"lzma": 0, "lrzip": 0}
+    qkv_split_count = 0
+
+    def _store(layout: int, body: bytes) -> tuple[int, int, int]:
+        """Compress and store; return (payload_idx, method_id, compressed_size)."""
+        method, comp = _pick_best_compressor(body, level, has_lrzip)
+        bulk_bodies.append((method, comp))
+        method_counts["lzma" if method == _COMPRESSOR_LZMA else "lrzip"] += 1
+        layout_counts[_LAYOUT_NAMES[layout]] += 1
+        return len(bulk_bodies) - 1, method, len(comp)
+
+    def _best_quinary_layout(symbols_2d: np.ndarray) -> tuple[int, int, int, dict, int]:
+        """Generate all layout candidates, screen with lzma to pick best layout,
+        then compress winner with min(lzma, lrzip) for the final body.
+
+        Cheap-screen + refine cuts serialize compute from O(layouts*compressors)
+        to O(layouts) lzma + 1 lrzip, keeping serialize time bounded even when
+        lrzip is slow on bad streams.
+
+        Returns (chosen_layout, payload_idx, compressed_size, layout_meta, n_candidates_evaluated).
+        """
+        cands = _gen_quinary_layout_candidates(symbols_2d)
+        best_layout, best_body, best_meta = None, None, None
+        best_lzma_size = None
+        for layout, body, meta in cands:
+            lzma_size = len(lzma.compress(body, preset=level))
+            if best_lzma_size is None or lzma_size < best_lzma_size:
+                best_lzma_size = lzma_size
+                best_layout, best_body, best_meta = layout, body, meta
+        # Now compress the winning layout with min(lzma, lrzip) for the actual stored body.
+        comp_method, comp_body = _pick_best_compressor(best_body, level, has_lrzip)
+        bulk_bodies.append((comp_method, comp_body))
+        method_counts["lzma" if comp_method == _COMPRESSOR_LZMA else "lrzip"] += 1
+        layout_counts[_LAYOUT_NAMES[best_layout]] += 1
+        return best_layout, len(bulk_bodies) - 1, len(comp_body), best_meta, len(cands)
+
+    for name, entry in state_dict.items():
+        if not isinstance(entry, dict):
+            metadata[name] = entry
+            continue
+        new_entry = dict(entry)  # shallow copy
+
+        if entry.get("type") == "quinary" and "packed" in entry:
+            # Reconstruct full symbol matrix from canonical packed bytes.
+            rows, cols = entry["shape"][0], entry["padded_cols"]
+            symbols_full = unpack_quinary(entry["packed"], entry["n_quins"]).numpy()
+            symbols_2d = symbols_full[:rows * cols].reshape(rows, cols)
+
+            split = _qkv_split_sizes(name, rows, cols)
+            if split is not None:
+                q_rows, kv_rows = split
+                qkv_split_count += 1
+                refs = []
+                for sub_name, sub_2d in (
+                    ("Q", symbols_2d[:q_rows]),
+                    ("K", symbols_2d[q_rows:q_rows + kv_rows]),
+                    ("V", symbols_2d[q_rows + kv_rows:]),
+                ):
+                    layout, idx, sz, meta, _ = _best_quinary_layout(sub_2d.copy())
+                    sub_rows, sub_cols = sub_2d.shape
+                    refs.append({
+                        "_v2": _BULK_SENTINEL_V2, "idx": idx, "layout": layout,
+                        "rows": sub_rows, "cols": sub_cols, "meta": meta,
+                    })
+                new_entry["packed"] = ("__QKV_SPLIT__", refs)
+                audit_lines.append(f"  {name}: qkv_split layouts={[_LAYOUT_NAMES[r['layout']] for r in refs]}")
+            else:
+                layout, idx, sz, meta, _ = _best_quinary_layout(symbols_2d.copy())
+                new_entry["packed"] = {
+                    "_v2": _BULK_SENTINEL_V2, "idx": idx, "layout": layout,
+                    "rows": rows, "cols": cols, "meta": meta,
+                }
+                audit_lines.append(f"  {name}: {_LAYOUT_NAMES[layout]} ({sz/1e6:.3f}MB)")
+
+        # Other bulk fields (fp8 data, fp16 large tensors, scale_delta tensors,
+        # etc.) — fall back to v1's torch.save+pick path.
+        for key, value in list(new_entry.items()):
+            if key == "packed":  # already handled
+                continue
+            if _is_bulk(value):
+                buf = io.BytesIO(); torch.save(value, buf)
+                method, comp = _pick_best_compressor(buf.getvalue(), level, has_lrzip)
+                bulk_bodies.append((method, comp))
+                method_counts["lzma" if method == _COMPRESSOR_LZMA else "lrzip"] += 1
+                layout_counts["raw"] += 1
+                new_entry[key] = {
+                    "_v2": _BULK_SENTINEL_V2, "idx": len(bulk_bodies) - 1,
+                    "layout": _LAYOUT_RAW,
+                }
+        metadata[name] = new_entry
+
+    # Compress metadata.
+    meta_buf = io.BytesIO(); torch.save(metadata, meta_buf)
+    meta_compressed = lzma.compress(meta_buf.getvalue(), preset=level)
+
+    # Assemble archive (header 0x03).
+    out = bytearray()
+    out.append(_COMPRESSOR_PER_STREAM_V2)
+    out.extend(len(meta_compressed).to_bytes(4, "little"))
+    out.extend(meta_compressed)
+    out.extend(len(bulk_bodies).to_bytes(4, "little"))
+    for method, body in bulk_bodies:
+        out.append(method)
+        out.extend(len(body).to_bytes(4, "little"))
+        out.extend(body)
+
+    layout_summary = ", ".join(f"{n}={c}" for n, c in layout_counts.items() if c)
+    audit = (f"per_stream_v2: meta={len(meta_compressed)/1e6:.2f}MB, "
+             f"{len(bulk_bodies)} bulk payloads "
+             f"(lzma={method_counts['lzma']}, lrzip={method_counts['lrzip']}; "
+             f"{layout_summary}), qkv_splits={qkv_split_count}, "
+             f"total={len(out)/1e6:.2f}MB")
+    return bytes(out), audit
+
+
+def _deserialize_per_stream_v2(blob: bytes) -> dict:
+    """Inverse of _serialize_per_stream_v2. Returns a state_dict whose quinary
+    entries have canonical entry["packed"] bytes — `deq_sd` is unaware of the
+    archive layer."""
+    cursor = 0
+    assert blob[cursor] == _COMPRESSOR_PER_STREAM_V2, \
+        f"expected v2 header 0x03, got {blob[0]:#x}"
+    cursor += 1
+    meta_size = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+    meta_blob = blob[cursor:cursor + meta_size]; cursor += meta_size
+    metadata = torch.load(io.BytesIO(lzma.decompress(meta_blob)),
+                          map_location="cpu", weights_only=False)
+    n_payloads = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+    decompressed: list[bytes] = []
+    for _ in range(n_payloads):
+        method = blob[cursor]; cursor += 1
+        size = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+        body = blob[cursor:cursor + size]; cursor += size
+        if method == _COMPRESSOR_LZMA:
+            decompressed.append(lzma.decompress(body))
+        elif method == _COMPRESSOR_LRZIP_ZPAQ:
+            decompressed.append(_lrzip_decompress_bytes(body))
+        else:
+            raise ValueError(f"unknown compressor method {method:#x}")
+
+    def _is_v2_ref(v):
+        return isinstance(v, dict) and v.get("_v2") == _BULK_SENTINEL_V2
+
+    for name, entry in metadata.items():
+        if not isinstance(entry, dict):
+            continue
+        for key, value in list(entry.items()):
+            if _is_v2_ref(value):
+                if value["layout"] == _LAYOUT_RAW:
+                    entry[key] = torch.load(io.BytesIO(decompressed[value["idx"]]),
+                                            map_location="cpu", weights_only=False)
+                else:
+                    entry[key] = _layout_to_canonical_bytes(
+                        value["layout"], decompressed[value["idx"]],
+                        value["rows"], value["cols"], value["meta"])
+            elif (isinstance(value, tuple) and len(value) == 2
+                  and value[0] == "__QKV_SPLIT__"):
+                # Reconstruct each part's symbol matrix, concat along row dim,
+                # repack as canonical base-5.
+                refs = value[1]
+                sub_symbols = []
+                for ref in refs:
+                    canonical_body = _layout_to_canonical_bytes(
+                        ref["layout"], decompressed[ref["idx"]],
+                        ref["rows"], ref["cols"], ref["meta"])
+                    n = ref["rows"] * ref["cols"]
+                    sub_syms = unpack_quinary(canonical_body, n).numpy()[:n]
+                    sub_symbols.append(sub_syms.reshape(ref["rows"], ref["cols"]))
+                full = np.concatenate(sub_symbols, axis=0).reshape(-1).copy()
+                entry[key], _ = pack_quinary(torch.from_numpy(full))
+    return metadata
+
+
+def _quant_scale(scale_fp16: Tensor, bits: int) -> dict:
+    """Quantize per-group scales to bits-bit log-deltas. Returns dict with
+    `scale_anchor` (fp16), `scale_step` (fp16), `scale_delta` (packed bytes
+    or int8 tensor), plus `scale_bits` to dispatch in deq_sd."""
+    scale_f = scale_fp16.float().reshape(-1)
+    n_groups = scale_f.numel()
+    log_scale = torch.log2(scale_f.clamp(min=1e-12))
+    lo, hi = float(log_scale.min().item()), float(log_scale.max().item())
+    anchor = (lo + hi) / 2.0
+    spread = hi - lo
+    levels = 1 << bits
+    half = levels // 2
+    step = max(spread / max(levels - 1, 1), 1e-9)
+    delta = ((log_scale - anchor) / step).round().clamp(-half, half - 1).to(torch.int32)
+    if bits == 4:
+        d = (delta + half).to(torch.uint8).numpy()
+        if len(d) % 2:
+            d = np.concatenate([d, np.zeros(1, dtype=np.uint8)])
+        packed = (d[::2] | (d[1::2] << 4)).astype(np.uint8)
+        delta_stored = torch.from_numpy(packed.copy())
+    elif bits == 5:
+        delta_stored = (delta + half).to(torch.uint8)
+    else:  # 8
+        delta_stored = delta.to(torch.int8)
+    return {
+        "scale_anchor": torch.tensor(anchor, dtype=torch.float16),
+        "scale_step": torch.tensor(step, dtype=torch.float16),
+        "scale_delta": delta_stored,
+        "scale_n_groups": n_groups,
+        "scale_bits": bits,
+    }
+
+def _dequant_scale(entry: dict) -> Tensor:
+    """Reconstruct fp16 per-group scales from the quantized representation."""
+    bits = int(entry["scale_bits"])
+    half = (1 << bits) // 2
+    n_groups = int(entry["scale_n_groups"])
+    if bits == 4:
+        packed = entry["scale_delta"].to(torch.uint8).numpy()
+        low = packed & 0x0F
+        high = (packed >> 4) & 0x0F
+        d = np.empty(packed.size * 2, dtype=np.uint8)
+        d[0::2] = low
+        d[1::2] = high
+        delta = torch.from_numpy(d[:n_groups].astype(np.int32) - half)
+    elif bits == 5:
+        delta = entry["scale_delta"].to(torch.int32) - half
+    else:  # 8
+        delta = entry["scale_delta"].to(torch.int32)
+    anchor = entry["scale_anchor"].float()
+    step = entry["scale_step"].float()
+    return (2.0 ** (anchor + delta.float() * step)).to(torch.float16)
+
+
+def q_sd(state_dict: dict, group_size: int = 64,
+         scale_quant_bits: int = 0) -> tuple[dict, dict]:
+    quantized = {}
+    stats = {"quinary_params": 0, "quinary_bytes": 0, "fp_params": 0, "fp_bytes": 0}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().float().contiguous()
+        t_orig_shape = list(t.shape)
+        if t.ndim == 3:
+            t = t.reshape(t.shape[0], -1)
+        is_quantized_candidate = (
+            t.ndim == 2 and t.numel() > 65_536
+            and "tok_emb" not in name and "lm_head" not in name and "embed_proj" not in name
+        )
+        if is_quantized_candidate:
+            pad = (group_size - t.shape[1] % group_size) % group_size
+            t_padded = F.pad(t, (0, pad)) if pad > 0 else t
+            t_grouped = t_padded.reshape(-1, group_size)
+            scale = t_grouped.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+            q = (t_grouped / scale).round().clamp(-2, 2).to(torch.int8)
+
+            packed_bytes, n_packed = pack_quinary(q)
+            stats["quinary_params"] += t.numel()
+
+            entry = {
+                "type": "quinary", "packed": packed_bytes,
+                "shape": list(t.shape), "padded_cols": t_padded.shape[1],
+                "group_size": group_size, "n_quins": n_packed,
+                "orig_shape": t_orig_shape,
+            }
+            if scale_quant_bits and scale_quant_bits in (4, 5, 8):
+                sq = _quant_scale(scale.half().squeeze(-1), scale_quant_bits)
+                entry.update(sq)
+                stats["quinary_bytes"] += len(packed_bytes) + sq["scale_delta"].numel() + 4  # +4 for anchor+step
+            else:
+                entry["scale"] = scale.half().squeeze(-1)
+                stats["quinary_bytes"] += len(packed_bytes) + scale.numel() * 2
+            quantized[name] = entry
+        elif t.ndim == 2:
+            quantized[name] = {"type": "fp8", "data": t.to(torch.float8_e4m3fn)}
+            stats["fp_params"] += t.numel()
+            stats["fp_bytes"] += t.numel()
+        else:
+            quantized[name] = {"type": "fp16", "data": t.half()}
+            stats["fp_params"] += t.numel()
+            stats["fp_bytes"] += t.numel() * 2
+    return quantized, stats
+
+def deq_sd(quantized: dict, target_dtype=torch.bfloat16):
+    out = {}
+    for name, entry in quantized.items():
+        if entry["type"] == "quinary":
+            q = unpack_quinary(entry["packed"], entry["n_quins"])
+            q = q.float().reshape(-1, entry["group_size"])
+            if "scale_bits" in entry:
+                scale = _dequant_scale(entry).float().unsqueeze(-1)
+            else:
+                scale = entry["scale"].float().unsqueeze(-1)
+            q_absmean = q.abs().mean(-1, keepdim=True).clamp(min=1e-8)
+            t = (q * (scale / q_absmean)).reshape(-1, entry["padded_cols"])
+            shape = entry["shape"]
+            result = t[:shape[0], :shape[1]].to(target_dtype)
+            orig = entry.get("orig_shape")
+            out[name] = result.reshape(orig).contiguous() if orig and orig != shape else result.contiguous()
+        elif entry["type"] == "fp8":
+            out[name] = entry["data"].to(torch.float32).to(target_dtype).contiguous()
+        else:
+            out[name] = entry["data"].to(target_dtype).contiguous()
+    return out
+
+def quin_stats(model: nn.Module, group_size: int = 64):
+    total = zeros = 0
+    with torch.no_grad():
+        for name, p in model.named_parameters():
+            if p.ndim == 2 and ("weight" in name or "prototypes" in name) and p.shape[0] > 1 and p.numel() % group_size == 0:
+                w = p.detach().float().reshape(-1, group_size)
+                scale = w.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+                q = (w / scale).round().clamp(-2, 2)
+                zeros += int((q == 0).sum().item())
+                total += int(q.numel())
+    return {"zero_frac": zeros / max(total, 1), "total_weights": total}
+
+_prev_committed: dict = {}
+
+def churn_fn(model: nn.Module, group_size: int = 64):
+    global _prev_committed
+    total = flipped = 0
+    with torch.no_grad():
+        for name, p in model.named_parameters():
+            if p.ndim == 2 and ("weight" in name or "prototypes" in name) and p.shape[0] > 1 and p.numel() % group_size == 0:
+                w = p.detach().float().reshape(-1, group_size)
+                scale = w.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+                q = (w / scale).round().clamp(-2, 2).cpu().numpy()
+                if name in _prev_committed:
+                    flipped += int(np.sum(q != _prev_committed[name]))
+                    total += q.size
+                _prev_committed[name] = q
+    return flipped / max(total, 1)
+
+def ns_orth(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, wd: float = 0.0):
+        super().__init__(params, dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, wd=wd))
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr, momentum = group["lr"], group["momentum"]
+            backend_steps, nesterov = group["backend_steps"], group["nesterov"]
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = F.rms_norm(g.float(), (g.size(-1),)).bfloat16()
+                    g = ns_orth(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr:curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+            wd = group.get("wd", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.mul_(1 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+def ld_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = ld_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self):
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = ld_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos:self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank, self.world_size, self.device = rank, world_size, device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start:start + per_rank_span].pin_memory().to(self.device, non_blocking=True).to(torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x, y
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+def apply_fp8_ste(w: Tensor) -> Tensor:
+    w_sim = w.to(torch.float8_e4m3fn).to(w.dtype)
+    return (w_sim - w).detach() + w
+
+class QATLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w_qat = apply_fp8_ste(self.weight)
+        return F.linear(x, w_qat.to(x.dtype), self.bias.to(x.dtype) if self.bias is not None else None)
+
+class QATEmbedding(nn.Embedding):
+    def forward(self, input: Tensor) -> Tensor:
+        w_qat = apply_fp8_ste(self.weight)
+        return F.embedding(input, w_qat, self.padding_idx, self.max_norm,
+                           self.norm_type, self.scale_grad_by_freq, self.sparse)
+
+class QuinaryLinear(nn.Linear):
+    def __init__(self, in_features, out_features, bias=False, group_size=64):
+        super().__init__(in_features, out_features, bias=bias)
+        self.group_size = group_size
+        num_groups = (in_features * out_features) // group_size
+        # Inert by design: the STE detach below blocks gradients to
+        # `scale_correction`. Kept as a fp32 buffer at value 1.0 for
+        # backwards-compatibility with the ternary-base state-dict layout.
+        # An attempt to fix the STE so this parameter receives gradients
+        # was tested 2026-05-01 (commit b9c…) and showed a small
+        # training-time regression with no TTT benefit, so reverted.
+        self.scale_correction = nn.Parameter(torch.ones(num_groups, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.bfloat16()
+        g = self.group_size
+        w_g = w.reshape(-1, g)
+        scale = w_g.abs().mean(-1, keepdim=True).clamp(min=1e-8) * self.scale_correction.to(w.dtype).unsqueeze(-1)
+        q = (w_g / scale).round().clamp(-2, 2)
+        w_quantized = w + ((q * scale).reshape(w.shape) - w).detach()
+        return F.linear(x, w_quantized,
+                        self.bias.to(x.dtype) if self.bias is not None else None)
+
+
+class NormedQuinaryLinear(QuinaryLinear):
+    def forward(self, x: Tensor) -> Tensor:
+        return super().forward(F.rms_norm(x, (x.size(-1),)))
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(p in name for p in CTP)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0, no_cache: bool = False,
+                 rope_type: str = "rope", yarn_max_len: int = 4096, train_seq_len: int = 1024):
+        super().__init__()
+        self.no_cache = no_cache
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        if rope_type == "yarn":
+            scale = train_seq_len / yarn_max_len
+            freq_idx = torch.arange(0, dim, 2, dtype=torch.float32)
+            ramp = torch.clamp((freq_idx / dim - 0.25) / 0.75, 0.0, 1.0)
+            inv_freq = inv_freq / (ramp * (1.0 / scale - 1.0) + 1.0)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len, device, dtype):
+        if self.no_cache:
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            return freqs.cos()[None, :, None, :].to(dtype=dtype), freqs.sin()[None, :, None, :].to(dtype=dtype)
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, :, None, :]
+            self._sin_cached = freqs.sin()[None, :, None, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init,
+                 group_size=64, no_cache=False, rope_type="rope",
+                 yarn_max_len=4096, train_seq_len=1024):
+        super().__init__()
+        self.num_heads, self.num_kv_heads = num_heads, num_kv_heads
+        self.head_dim = dim // num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        self.c_qkv = QuinaryLinear(dim, self.q_size + 2 * self.kv_size, bias=False, group_size=group_size)
+        self.proj = NormedQuinaryLinear(dim, dim, bias=False, group_size=group_size)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base, no_cache=no_cache,
+                             rope_type=rope_type, yarn_max_len=yarn_max_len,
+                             train_seq_len=train_seq_len)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        qkv_out = self.c_qkv(x)
+        q_out, k_out, v_out = qkv_out.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q = q_out.reshape(bsz, seqlen, self.num_heads, self.head_dim)
+        k = k_out.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        v = v_out.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        q, k = F.rms_norm(q, (q.size(-1),)), F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None]
+        y = flash_attn_func(q.contiguous(), k.contiguous(), v.contiguous(), causal=True)
+        y = y.reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+class MLP(nn.Module):
+    def __init__(self, dim, mlp_mult, group_size=64, activation="swiglu"):
+        super().__init__()
+        hidden = mlp_mult * dim
+        self.activation = activation
+        if activation == "swiglu":
+            self.gate_up = QuinaryLinear(dim, hidden * 2, bias=False, group_size=group_size)
+        else:
+            self.fc = QuinaryLinear(dim, hidden, bias=False, group_size=group_size)
+        self.proj = NormedQuinaryLinear(hidden, dim, bias=False, group_size=group_size)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        if self.activation == "swiglu":
+            gu = self.gate_up(x)
+            gate, up = gu.chunk(2, dim=-1)
+            return self.proj(F.silu(gate) * up)
+        elif self.activation == "relu":
+            return self.proj(torch.relu(self.fc(x)))
+        elif self.activation == "leaky_relu":
+            return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.01))
+        elif self.activation == "leaky_relu2":
+            return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square())
+        else:  # relu2
+            return self.proj(torch.relu(self.fc(x)).square())
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int,
+                 rope_base: float, qk_gain_init: float, group_size: int=64,
+                 activation: str="swiglu", no_cache: bool=False,
+                 rope_type: str="rope", yarn_max_len: int=4096,
+                 train_seq_len: int=1024):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init,
+                                        group_size, no_cache, rope_type, yarn_max_len, train_seq_len)
+        self.mlp = MLP(dim, mlp_mult, group_size, activation)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x_in = mix[0] * x + mix[1] * x0
+        n = self.attn_norm(x_in)
+        x = x_in + self.attn_scale.to(dtype=x_in.dtype) * self.attn(n)
+        x = x + self.mlp_scale.to(dtype=x.dtype) * self.mlp(self.mlp_norm(x))
+        return x
+
+class GPT(nn.Module):
+    def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, mlp_mult,
+                 tie_embeddings, tied_embed_init_std, logit_softcap, rope_base, qk_gain_init,
+                 group_size: int = 64, activation: str = "swiglu",
+                 embed_dim: int = 0, softcap_type: str="poly", no_cache: bool=False,
+                 rope_type: str="rope", yarn_max_len: int=4096, train_seq_len: int=1024):
+        super().__init__()
+        self.tie_embeddings = tie_embeddings
+        self.logit_softcap = logit_softcap
+        self.softcap_type = softcap_type
+        self.embed_dim = embed_dim if embed_dim > 0 else model_dim
+        self.tok_emb = QATEmbedding(vocab_size, self.embed_dim)
+        self.embed_proj = QATLinear(self.embed_dim, model_dim, bias=False) if self.embed_dim != model_dim else None
+        self.embed_proj_rev = QATLinear(model_dim, self.embed_dim, bias=False) if self.embed_dim != model_dim else None
+
+        self.blocks = nn.ModuleList([
+            Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init,
+                  group_size, activation, no_cache, rope_type, yarn_max_len, train_seq_len)
+            for _ in range(num_layers)
+        ])
+
+        # U-Net split: first half encoder, second half decoder, decoder layers
+        # add a learned-weighted skip from the symmetric encoder layer.
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+
+        self.final_norm = RMSNorm()
+        self.lm_head = QATLinear(model_dim, vocab_size, bias=False)
+        self.lm_head._zero_init = True
+        if tie_embeddings:
+            self.lm_head.weight.requires_grad_(False)
+
+        self.vocab_bias = nn.Parameter(torch.zeros(vocab_size, dtype=torch.float32))
+        self._init_weights(tied_embed_init_std)
+
+    def _init_weights(self, tied_embed_init_std: float) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=tied_embed_init_std)
+        for module in self.modules():
+            if isinstance(module, QuinaryLinear) and not getattr(module, "_zero_init", False):
+                nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            elif isinstance(module, nn.Linear) and getattr(module, "_zero_init", False):
+                nn.init.zeros_(module.weight)
+
+    def _compute_logits(self, x: Tensor) -> Tensor:
+        if self.tie_embeddings:
+            proj = self.embed_proj_rev(x) if self.embed_proj_rev is not None else x
+            logits_raw = F.linear(proj, self.tok_emb.weight.to(x.dtype))
+        else:
+            logits_raw = self.lm_head(x)
+        return logits_raw + self.vocab_bias.to(x.dtype)
+
+    def _softcap(self, logits: Tensor) -> Tensor:
+        s = self.logit_softcap
+        if self.softcap_type == "tanh":
+            return s * torch.tanh(logits / s)
+        x_sc = torch.clamp(logits / s, -2.0, 2.0)
+        x2 = x_sc * x_sc
+        return s * torch.clamp(x_sc * (1.0 - x2 / 3.0 + x2 * x2 / 15.0), -1.0, 1.0)
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor, reduction: str = "mean") -> Tensor:
+        x = self.tok_emb(input_ids).float()
+        if self.embed_proj is not None:
+            x = self.embed_proj(x)
+        x = F.rms_norm(x, (x.size(-1),))
+        x0 = x
+
+        # U-Net encoder/decoder with skip connections.
+        skips = []
+        for vi in range(self.num_encoder_layers):
+            x = self.blocks[vi](x, x0)
+            skips.append(x)
+        for vi in range(self.num_decoder_layers):
+            if vi < self.num_skip_weights:
+                x = x + self.skip_weights[vi].to(dtype=x.dtype) * skips.pop()
+            x = self.blocks[self.num_encoder_layers + vi](x, x0)
+
+        x_normed = self.final_norm(x)
+        x_flat = x_normed.reshape(-1, x_normed.size(-1))
+        targets = target_ids.reshape(-1)
+        logits = self._softcap(self._compute_logits(x_flat))
+
+        if reduction == "none":
+            return F.cross_entropy(logits.float(), targets, reduction="none").reshape(input_ids.shape)
+
+        # Fused CE: single logsumexp; z-loss only during training (not eval BPB).
+        logits_f = logits.float()
+        lse = torch.logsumexp(logits_f, dim=-1)
+        target_logits = logits_f.gather(1, targets.unsqueeze(1)).squeeze(1)
+        main_loss = (lse - target_logits).mean()
+        if self.training:
+            main_loss = main_loss + 1e-4 * (lse ** 2).mean()
+        return main_loss
+
+
+def build_luts(sp, vocab_size: int, device: torch.device):
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+def ld_val(pattern, seq_len, max_tok=int(os.environ.get("VAL_MAX_TOKENS", 0))):
+    files = sorted(glob.glob(pattern))
+    assert files, f"No files: {pattern}"
+    tok = torch.cat([ld_shard(Path(p)) for p in files]).contiguous()
+    if max_tok > 0: tok = tok[:max_tok + 1]
+    u = ((tok.numel() - 1) // seq_len) * seq_len
+    return tok[:u + 1]
+
+
+def eval_val(args, model, rank, world_size, device, grad_accum_steps, val_tokens,
+             base_bytes_lut, has_leading_space_lut, is_boundary_token_lut):
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    local_batch_seqs = max(1, local_batch_tokens // args.train_seq_len)
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.no_grad():
+        for batch_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_end = min(batch_start + local_batch_seqs, seq_end)
+            raw_start = batch_start * args.train_seq_len
+            raw_end = batch_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64)
+            x, y = local[:-1].reshape(-1, args.train_seq_len), local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                batch_loss = model(x, y).detach()
+            n = float(y.numel())
+            loss_sum += batch_loss.to(torch.float64) * n
+            token_count += n
+            prev_ids, tgt_ids = x.reshape(-1), y.reshape(-1)
+            tok_bytes = base_bytes_lut[tgt_ids].to(torch.int16)
+            tok_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(torch.int16)
+            byte_count += tok_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        for t in (loss_sum, token_count, byte_count):
+            dist.all_reduce(t, op=dist.ReduceOp.SUM)
+    val_loss = loss_sum / token_count
+    bpb = (val_loss.item() / math.log(2.0)) * (token_count.item() / byte_count.item())
+    model.train()
+    return float(val_loss.item()), float(bpb), int(token_count.item()), int(byte_count.item())
+
+def main() -> None:
+    args = Hyperparameters()
+    code = Path(__file__).read_text(encoding="utf-8")
+
+    if args.matrix_optimizer != "adamw":
+        global ns_orth
+        ns_orth = torch.compile(ns_orth)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    grad_accum_steps = max(1, 8 // world_size)
+    grad_scale = 1.0 / grad_accum_steps
+
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+    os.makedirs("logs/cuda/", exist_ok=True)
+    logfile = f"logs/cuda/{args.run_id}.txt" if master_process else None
+    if master_process:
+        print(logfile)
+        # Truncate any pre-existing logfile from a previous run with the same
+        # RUN_ID so the per-RUN_ID log file isn't append-stacked across reruns.
+        if logfile:
+            open(logfile, "w", encoding="utf-8").close()
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+
+    log0(f"Python {sys.version}", console=False)
+    log0(f"PyTorch {torch.__version__}", console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    # Guard against an obvious tokenizer/vocab-size mismatch that would
+    # silently produce a wrong byte-count LUT (and therefore a wrong BPB
+    # denominator) without crashing.
+    assert sp.vocab_size() == args.vocab_size, (
+        f"tokenizer vocab_size ({sp.vocab_size()}) != args.vocab_size "
+        f"({args.vocab_size}); check TOKENIZER_PATH / VOCAB_SIZE")
+    val_tokens = ld_val(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_luts(
+        sp, args.vocab_size, device)
+
+    # --- Model ---
+    base_model = GPT(
+        vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim,
+        num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init,
+        group_size=args.bitnet_group_size, activation=args.activation_type,
+        embed_dim=args.embed_dim,
+        softcap_type=args.softcap_type, no_cache=(args.compile_mode == "reduce-overhead"),
+        rope_type=args.rope_type, yarn_max_len=args.yarn_max_len, train_seq_len=args.train_seq_len,
+    ).to(device).bfloat16()
+
+    for module in base_model.modules():
+        if isinstance(module, nn.Linear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+    if args.tie_embeddings:
+        base_model.lm_head.weight.requires_grad_(False)
+
+    torch._dynamo.config.optimize_ddp = False
+
+    compiled_model = torch.compile(base_model, mode=args.compile_mode if args.compile_mode != "default" else None)
+    use_find_unused = not args.tie_embeddings
+    model = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False,
+                find_unused_parameters=use_find_unused,
+                static_graph=not use_find_unused,
+                gradient_as_bucket_view=True) if distributed else compiled_model
+
+    # --- Optimizers ---
+    _excl = {"tok_emb.weight", "lm_head.weight"}
+    all_other_params = [(n, p) for n, p in base_model.named_parameters()
+                        if not any(eh in n for eh in _excl)]
+    matrix_params = [p for n, p in all_other_params
+                     if p.ndim == 2 and not any(pat in n for pat in CTP)]
+    scalar_params = [p for n, p in all_other_params
+                     if p.ndim < 2 or any(pat in n for pat in CTP)]
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    opt_tok = torch.optim.Adam(
+        [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+    if args.matrix_optimizer == "adamw":
+        opt_muon = torch.optim.AdamW(
+            [{"params": matrix_params, "lr": args.adam_lr, "base_lr": args.adam_lr}],
+            betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True)
+    else:
+        opt_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum,
+                        backend_steps=args.muon_backend_steps, wd=args.muon_wd)
+    for g in opt_muon.param_groups:
+        g["base_lr"] = args.matrix_lr
+    opt_scalar = torch.optim.Adam(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+    opt_head = torch.optim.Adam(
+        [{"params": [base_model.lm_head.weight], "lr": 0.0, "base_lr": 0.0}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+
+    optimizers = [opt_tok, opt_muon, opt_scalar, opt_head]
+
+    # --- Log all hyperparameters ---
+    log0("--- Hyperparameters ---", console=False)
+    log0(" ".join(f"{a}={getattr(args,a)}" for a in sorted(dir(args)) if not a.startswith("_") and a not in ("train_files","val_files") and not callable(getattr(args,a))), console=False)
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"params:{n_params} L:{args.num_layers} d:{args.model_dim} h:{args.num_heads} kv:{args.num_kv_heads} ws:{world_size} ga:{grad_accum_steps} s:{args.seed}")
+
+    # --- Data loader & helpers ---
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all():
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float):
+        if args.warmdown_fraction <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = int(args.iterations * (1.0 - args.warmdown_fraction))
+            ratio = max((args.iterations - step) / max(args.iterations * args.warmdown_fraction, 1), 0.0) if step >= warmdown_start else 1.0
+        else:
+            warmdown_ms = max_wallclock_ms * args.warmdown_fraction
+            remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+            ratio = remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+        return max(ratio, args.min_lr)
+
+    _seq_switched = False
+    _batch_switched = False
+    active_seq_len = args.seq_len_start if args.seq_len_start > 0 else args.train_seq_len
+    active_batch_tokens = args.batch_tokens_start if args.batch_tokens_start > 0 else args.train_batch_tokens
+
+    if args.skip_training:
+        log0("skip_training=1, using existing artifact final_model.quinary.ptz")
+
+    # --- Compiler warmup ---
+    if args.warmup_steps > 0 and not args.skip_training:
+        _ms = {n: t.detach().cpu().clone() for n, t in base_model.state_dict().items()}
+        _os = [copy.deepcopy(o.state_dict()) for o in optimizers]
+        model.train()
+        for ws in range(args.warmup_steps):
+            zero_grad_all()
+            for mi in range(grad_accum_steps):
+                if distributed: model.require_backward_grad_sync = mi == grad_accum_steps - 1
+                x, y = train_loader.next_batch(active_batch_tokens, active_seq_len, grad_accum_steps)
+                torch.compiler.cudagraph_mark_step_begin()
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16): loss = model(x, y)
+                (loss * grad_scale).backward()
+            for o in optimizers: o.step()
+            zero_grad_all()
+            log0(f"warmup:{ws+1}/{args.warmup_steps}")
+        base_model.load_state_dict(_ms, strict=True)
+        for o, s in zip(optimizers, _os): o.load_state_dict(s)
+        zero_grad_all()
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # --- Main training loop ---
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    train_loss = torch.zeros((), device=device)
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    step = 0
+
+    while not args.skip_training:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        if last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0):
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb, val_tok_count, val_byte_count = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut)
+            tstats = quin_stats(base_model, group_size=args.bitnet_group_size)
+            log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                 f"train_time:{training_time_ms:.0f}ms zero_frac:{tstats['zero_frac']:.3f} "
+                 f"eval_tokens:{val_tok_count} eval_bytes:{val_byte_count}")
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}")
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+
+        # Sequence length schedule
+        if args.seq_len_start > 0 and not _seq_switched:
+            if max_wallclock_ms is not None:
+                should_switch_seq = elapsed_ms >= args.seq_schedule_fraction * max_wallclock_ms
+            else:
+                should_switch_seq = step >= int(args.iterations * args.seq_schedule_fraction)
+            if should_switch_seq:
+                active_seq_len = args.train_seq_len
+                _seq_switched = True
+                torch._dynamo.reset()
+                train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+                log0(f"step:{step} seq_len_switch:{args.seq_len_start}->{active_seq_len}")
+
+        # Batch size schedule
+        if args.batch_tokens_start > 0 and not _batch_switched:
+            if max_wallclock_ms is not None:
+                should_switch_batch = elapsed_ms >= args.batch_schedule_fraction * max_wallclock_ms
+            else:
+                should_switch_batch = step >= int(args.iterations * args.batch_schedule_fraction)
+            if should_switch_batch:
+                active_batch_tokens = args.train_batch_tokens
+                _batch_switched = True
+                log0(f"step:{step} batch_switch:{args.batch_tokens_start}->{active_batch_tokens}")
+
+        zero_grad_all()
+        train_loss.zero_()
+
+        for micro in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro == grad_accum_steps - 1
+            x, y = train_loader.next_batch(active_batch_tokens, active_seq_len, grad_accum_steps)
+            torch.compiler.cudagraph_mark_step_begin()
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                loss = model(x, y)
+            train_loss.add_(loss.detach())
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        # Muon momentum warmup (skip when AdamW is the matrix optimizer)
+        if args.matrix_optimizer != "adamw":
+            frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0
+            for g in opt_muon.param_groups:
+                g["momentum"] = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+
+        # LR scheduling
+        for opt in optimizers:
+            for g in opt.param_groups:
+                g["lr"] = g["base_lr"] * scale
+            opt.step()
+        zero_grad_all()
+        step += 1
+        approx_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        
+        if args.train_log_every > 0 and step % args.train_log_every == 0:
+            log0(f"step:{step}/{args.iterations} loss:{train_loss.item():.4f} t:{approx_ms:.0f}ms avg:{approx_ms/step:.1f}ms")
+        if args.churn_log_every > 0 and step % args.churn_log_every == 0:
+            log0(f"step:{step} churn:{churn_fn(base_model, args.bitnet_group_size):.4f} zero:{quin_stats(base_model, args.bitnet_group_size)['zero_frac']:.3f}")
+
+        # Wallclock cap sync
+        if stop_after_step is None and max_wallclock_ms is not None and step % 10 == 0:
+            reached_cap = approx_ms >= max_wallclock_ms
+            if distributed:
+                cap_t = torch.tensor(int(reached_cap), device=device)
+                dist.all_reduce(cap_t, op=dist.ReduceOp.MAX)
+                reached_cap = bool(cap_t.item())
+            if reached_cap:
+                stop_after_step = step
+
+    # --- Serialization ---
+    if master_process and not args.skip_training:
+        sd = base_model.state_dict()
+        if base_model.tie_embeddings:
+            sd.pop("lm_head.weight", None)
+
+        log0("serialize: start")
+        t_qsd = time.perf_counter()
+        q_obj, q_stats = q_sd(sd, group_size=args.bitnet_group_size,
+                              scale_quant_bits=args.scale_quant_bits)
+        qsd_time = time.perf_counter() - t_qsd
+        log0(f"serialize: q_sd in {qsd_time:.1f}s "
+             f"(quinary={q_stats['quinary_params']/1e6:.2f}M params/{q_stats['quinary_bytes']/1e6:.2f}MB, "
+             f"fp={q_stats['fp_params']/1e6:.2f}M params/{q_stats['fp_bytes']/1e6:.2f}MB)")
+
+        t_save = time.perf_counter()
+        buf = io.BytesIO()
+        torch.save(q_obj, buf)
+        raw_bytes = len(buf.getvalue())
+        log0(f"serialize: torch.save raw={raw_bytes/1e6:.2f}MB in {time.perf_counter()-t_save:.1f}s")
+
+        t_compress = time.perf_counter()
+        final_blob, per_stream_audit = _serialize_per_stream_v2(q_obj, level=9)
+        compress_time = time.perf_counter() - t_compress
+        artifact_bytes = len(final_blob)
+        log0(per_stream_audit)
+        log0(f"serialize: per_stream_v2 {raw_bytes/1e6:.2f}MB -> {artifact_bytes/1e6:.2f}MB "
+             f"(ratio {artifact_bytes/raw_bytes:.1%}, saved {(raw_bytes-artifact_bytes)/1e6:.2f}MB) "
+             f"in {compress_time:.1f}s")
+
+        with open("final_model.quinary.ptz", "wb") as f:
+            f.write(final_blob)
+
+        code_bytes = len(code.encode("utf-8"))
+
+        total = artifact_bytes + code_bytes
+        log0(f"artifact:{artifact_bytes/1e6:.2f}MB quinary:{q_stats['quinary_params']}({q_stats['quinary_bytes']}B) fp:{q_stats['fp_params']}({q_stats['fp_bytes']}B) code:{code_bytes}")
+        log0(f"budget:{total}/{16000000} ({total/1e6:.2f}/{16.00:.2f}MB) {'FITS' if total <= 16000000 else 'OVER'}")
+
+    # --- All ranks load roundtrip weights and evaluate ---
+    if distributed:
+        dist.barrier()
+
+    with open("final_model.quinary.ptz", "rb") as f:
+        loaded = _load_artifact(f.read())
+    missing, unexpected = base_model.load_state_dict(deq_sd(loaded), strict=False)
+    # The only "missing" key we expect is `lm_head.weight` when tied, since
+    # q_sd drops it from the saved state-dict. Any other missing/unexpected
+    # key is a serialization-roundtrip bug and should fail loudly.
+    expected_missing = {"lm_head.weight"} if base_model.tie_embeddings else set()
+    assert set(missing) <= expected_missing, f"unexpected missing keys after artifact load: {set(missing) - expected_missing}"
+    assert not unexpected, f"unexpected extra keys after artifact load: {unexpected}"
+    torch._dynamo.reset()
+
+    q_val_loss, q_val_bpb, q_tok_count, q_byte_count = eval_val(
+        args, model, rank, world_size, device, grad_accum_steps,
+        val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut)
+    log0(f"final_quinary_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+         f"eval_tokens:{q_tok_count} eval_bytes:{q_byte_count}")
+
+    # --- Score-first chunk-based CTP TTT ---
+    if args.ttt_steps > 0:
+        torch.cuda.synchronize()
+        t_ttt = time.perf_counter()
+        seq_len = args.train_seq_len
+        # TTT_STRIDE: sliding-window step used to slice the val stream into TTT
+        # examples. Hardcoded at 16 (canonical sp16384 quinary submission); a
+        # smaller stride yields more overlapping windows but more compute.
+        stride = 16
+        ttt_chunk_tokens = args.ttt_tokens if args.ttt_tokens > 0 else 32768
+        ttt_epochs = args.ttt_steps
+        batch_seqs = 32
+        total_tokens = val_tokens.numel() - 1
+
+        # Select TTT params (CTP only — quinary weights are frozen)
+        for p in base_model.parameters():
+            p.requires_grad_(False)
+        ttt_params = []
+        for name, p in base_model.named_parameters():
+            if any(pat in name for pat in CTP):
+                p.requires_grad_(True)
+                ttt_params.append(p)
+        n_ttt = sum(p.numel() for p in ttt_params)
+        ttt_opt = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=0.9)
+        for pg in ttt_opt.param_groups:
+            pg["initial_lr"] = pg["lr"]
+        log0(f"ttt: {n_ttt} CTP params, lr={args.ttt_lr}")
+
+        # Assign sliding windows to chunks
+        context_size = seq_len - stride
+        window_starts = [ws for ws in range(0, total_tokens, stride)
+                         if ws + context_size < total_tokens]
+        num_chunks = (total_tokens + ttt_chunk_tokens - 1) // ttt_chunk_tokens
+        chunk_windows = [[] for _ in range(num_chunks)]
+        for ws in window_starts:
+            s = 0 if ws == 0 else context_size
+            ci = min((ws + s) // ttt_chunk_tokens, num_chunks - 1)
+            chunk_windows[ci].append(ws)
+        log0(f"ttt: {n_ttt} CTP params, {num_chunks} chunks, {ttt_epochs} epochs, lr={args.ttt_lr}")
+
+        loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+        token_count = torch.zeros((), device=device, dtype=torch.float64)
+        byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+        # Recompile for TTT (find_unused_parameters needed with frozen params)
+        torch._dynamo.reset()
+        compiled_ttt = torch.compile(base_model, mode=args.compile_mode if args.compile_mode != "default" else None)
+
+        for ci in range(num_chunks):
+            windows = chunk_windows[ci]
+            if not windows:
+                continue
+            chunk_start = ci * ttt_chunk_tokens
+            chunk_end = min((ci + 1) * ttt_chunk_tokens, total_tokens)
+            my_s = len(windows) * rank // world_size
+            my_e = len(windows) * (rank + 1) // world_size
+            my_windows = windows[my_s:my_e]
+
+            # Phase 1: SCORE (no_grad, compiled)
+            base_model.eval()
+            with torch.no_grad():
+                for bi in range(0, len(my_windows), batch_seqs):
+                    batch_ws = my_windows[bi:bi + batch_seqs]
+                    bsz = len(batch_ws)
+                    x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                    y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                    wlens = []
+                    for i, ws in enumerate(batch_ws):
+                        we = min(ws + seq_len, total_tokens)
+                        wlen = we - ws
+                        wlens.append(wlen)
+                        chunk_tok = val_tokens[ws:we + 1].to(dtype=torch.int64, device=device)
+                        x_batch[i, :wlen] = chunk_tok[:-1]
+                        y_batch[i, :wlen] = chunk_tok[1:]
+                    with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                        nll = compiled_ttt(x_batch, y_batch, reduction="none").detach()
+                    for i, ws in enumerate(batch_ws):
+                        wlen = wlens[i]
+                        s = 0 if ws == 0 else context_size
+                        scored_nll = nll[i, s:wlen].to(torch.float64)
+                        loss_sum += scored_nll.sum()
+                        token_count += float(wlen - s)
+                        tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen]
+                        tb = base_bytes_lut[tgt].to(torch.float64)
+                        tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                        byte_count += tb.sum()
+
+            # Phase 2: TRAIN on chunk (score-first: already scored)
+            if ci < num_chunks - 1 and ttt_epochs > 0:
+                base_model.train()
+                chunk_seqs = (chunk_end - chunk_start) // seq_len
+                if chunk_seqs > 0:
+                    cos_mul = 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1)))
+                    for pg in ttt_opt.param_groups:
+                        pg["lr"] = pg.get("initial_lr", pg["lr"]) * cos_mul
+                    my_seq_s = chunk_seqs * rank // world_size
+                    my_seq_e = chunk_seqs * (rank + 1) // world_size
+                    for _ep in range(ttt_epochs):
+                        for bs in range(0, my_seq_e - my_seq_s, batch_seqs):
+                            be = min(bs + batch_seqs, my_seq_e - my_seq_s)
+                            start_tok = chunk_start + (my_seq_s + bs) * seq_len
+                            end_tok = chunk_start + (my_seq_s + be) * seq_len + 1
+                            if end_tok > val_tokens.numel():
+                                continue
+                            local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64)
+                            x = local[:-1].reshape(-1, seq_len)
+                            y = local[1:].reshape(-1, seq_len)
+                            ttt_opt.zero_grad(set_to_none=True)
+                            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                                loss = compiled_ttt(x, y)
+                            loss.backward()
+                            if distributed:
+                                for p in ttt_params:
+                                    if p.grad is not None:
+                                        dist.all_reduce(p.grad, op=dist.ReduceOp.AVG)
+                            torch.nn.utils.clip_grad_norm_(ttt_params, 1.0)
+                            ttt_opt.step()
+
+            if master_process and ci % max(1, num_chunks // 5) == 0:
+                log0(f"ttt chunk:{ci+1}/{num_chunks}")
+
+        if distributed:
+            for t in (loss_sum, token_count, byte_count):
+                dist.all_reduce(t, op=dist.ReduceOp.SUM)
+        ttt_val_loss = (loss_sum / token_count).item()
+        ttt_bpb = (ttt_val_loss / math.log(2.0)) * (token_count.item() / byte_count.item())
+        for p in base_model.parameters():
+            p.requires_grad_(True)
+        torch._dynamo.reset()
+        torch.cuda.synchronize()
+        ttt_time_ms = 1000.0 * (time.perf_counter() - t_ttt)
+        log0(f"ttt_eval val_loss:{ttt_val_loss:.4f} val_bpb:{ttt_bpb:.4f} "
+             f"time:{ttt_time_ms:.0f}ms "
+             f"eval_tokens:{int(token_count.item())} eval_bytes:{int(byte_count.item())}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+if __name__ == "__main__":
+    main()
+====================================================================================================
+Python 3.13.13 | packaged by Anaconda, Inc. | (main, Apr 14 2026, 06:19:41) [GCC 14.3.0]
+PyTorch 2.10.0+cu128
+--- Hyperparameters ---
+activation_type=relu2 adam_eps=1e-08 adam_lr=0.05 adam_wd=0.05 batch_schedule_fraction=0.33 batch_tokens_start=0 beta1=0.9 beta2=0.95 bitnet_group_size=192 churn_log_every=0 compile_mode=default data_path=./data/canonical/datasets/fineweb10B_sp16384 embed_dim=380 embed_lr=0.6 fp_storage=True grad_clip_norm=0.0 head_lr=0.02 iterations=10000 logit_softcap=10.0 matrix_lr=0.035 matrix_optimizer=muon max_wallclock_seconds=599.0 min_lr=0.0 mlp_mult=4 model_dim=576 muon_backend_steps=3 muon_momentum=0.95 muon_momentum_warmup_start=0.85 muon_momentum_warmup_steps=500 muon_wd=0.0 num_heads=6 num_kv_heads=3 num_layers=10 qk_gain_init=5.0 rope_base=5000.0 rope_type=yarn run_id=quinary_seed42 scalar_lr=0.02 scale_quant_bits=5 seed=42 seq_len_start=0 seq_schedule_fraction=0.0 skip_training=False softcap_type=poly tie_embeddings=1 tied_embed_init_std=0.005 tied_embed_lr=0.02 tokenizer_path=./data/canonical/tokenizers/fineweb_16384_bpe.model train_batch_tokens=524288 train_log_every=1000 train_seq_len=1024 ttt_lr=0.005 ttt_steps=3 ttt_tokens=32768 val_batch_size=524288 val_loss_every=0 vocab_size=16384 warmdown_fraction=0.2 warmup_steps=5 yarn_max_len=2048
+params:52828668 L:10 d:576 h:6 kv:3 ws:8 ga:1 s:42
+warmup:1/5
+warmup:2/5
+warmup:3/5
+warmup:4/5
+warmup:5/5
+step:1000/10000 loss:3.8235 t:76844ms avg:76.8ms
+step:2000/10000 loss:3.5118 t:153939ms avg:77.0ms
+step:3000/10000 loss:3.4336 t:230839ms avg:76.9ms
+step:4000/10000 loss:3.2842 t:307597ms avg:76.9ms
+step:5000/10000 loss:3.5044 t:384327ms avg:76.9ms
+step:6000/10000 loss:3.5311 t:461068ms avg:76.8ms
+step:7000/10000 loss:3.4751 t:537724ms avg:76.8ms
+step:7800/10000 val_loss:3.2656 val_bpb:1.1584 train_time:599124ms zero_frac:0.262 eval_tokens:37146624 eval_bytes:151078879
+stopping_early: wallclock_cap train_time:599124ms step:7800/10000
+serialize: start
+serialize: q_sd in 0.1s (quinary=36.50M params/12.36MB, fp=6.90M params/7.11MB)
+serialize: torch.save raw=19.53MB in 0.0s
+per_stream_v2: meta=0.14MB, 63 bulk payloads (lzma=61, lrzip=2; raw=3, base5=2, base5_T=35, bitmask_T=23), qkv_splits=10, total=15.64MB
+serialize: per_stream_v2 19.53MB -> 15.64MB (ratio 80.1%, saved 3.90MB) in 36.9s
+artifact:15.64MB quinary:36495360(12355360B) fp:6896124(7114168B) code:79272
+budget:15714938/16000000 (15.71/16.00MB) FITS
+final_quinary_roundtrip val_loss:3.2774 val_bpb:1.1626 eval_tokens:37146624 eval_bytes:151078879
+ttt: 42364 CTP params, lr=0.005
+ttt: 42364 CTP params, 1134 chunks, 3 epochs, lr=0.005
+ttt chunk:1/1134
+ttt chunk:227/1134
+ttt chunk:453/1134
+ttt chunk:679/1134
+ttt chunk:905/1134
+ttt chunk:1131/1134
+ttt_eval val_loss:3.2083 val_bpb:1.1381 time:213616ms eval_tokens:37146624 eval_bytes:151078879
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/quinary_seed7.txt b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/quinary_seed7.txt
new file mode 100644
index 0000000000..386fd3c195
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/quinary_seed7.txt
@@ -0,0 +1,1689 @@
+"Quinary training script for OpenAI's Parameter Golf Challenge. Based on ternary submission by Ciprian-Florin Ifrim (24 March 2026). Quinary: {-2,-1,0,+1,+2} with base-5 packing (3 quins/byte = 2.667 bpw vs ternary 1.6 bpw)."
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import tempfile
+import time
+import lzma
+from pathlib import Path
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+from flash_attn_interface import flash_attn_func
+
+def _e(k, d, t=str):
+    v = os.environ.get(k, str(d))
+    if t == bool: return bool(int(v))
+    return t(v)
+
+class Hyperparameters:
+    # Defaults below match the canonical SP16384 quinary submission config —
+    # i.e. exactly what `run.sh` passes through to torchrun. A bare
+    # `torchrun --standalone --nproc_per_node=8 train_gpt.py` (no env vars)
+    # therefore reproduces the submission. `run.sh` is still the
+    # documentation-of-record for the canonical config; these defaults are
+    # mirrored from it so the two never silently disagree.
+    data_path = _e("DATA_PATH", "./data/canonical/datasets/fineweb10B_sp16384")
+    # [0-9] prefix matches only NNNNNN-suffixed shard files, ignoring any
+    # sibling parallel-array files that might share the prefix.
+    train_files = os.path.join(data_path, "fineweb_train_[0-9]*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_[0-9]*.bin")
+    tokenizer_path = _e("TOKENIZER_PATH", "./data/canonical/tokenizers/fineweb_16384_bpe.model")
+    run_id = os.environ.get("RUN_ID", f"run_{int(time.time())}")
+    seed = _e("SEED", 42, int)
+    compile_mode = _e("COMPILE_MODE", "default")
+    val_batch_size = _e("VAL_BATCH_SIZE", 524288, int)
+    val_loss_every = _e("VAL_LOSS_EVERY", 0, int)  # 0 = no in-training val
+    train_log_every = _e("TRAIN_LOG_EVERY", 1000, int)
+    iterations = _e("ITERATIONS", 10000, int)
+    warmdown_fraction = _e("WARMDOWN_FRACTION", 0.2, float)
+    min_lr = _e("MIN_LR", 0.0, float)  # floor on the LR multiplier (fraction of base LR)
+    warmup_steps = _e("WARMUP_STEPS", 5, int)
+    train_batch_tokens = _e("TRAIN_BATCH_TOKENS", 524288, int)
+    train_seq_len = _e("TRAIN_SEQ_LEN", 1024, int)
+    max_wallclock_seconds = _e("MAX_WALLCLOCK_SECONDS", 599.0, float)
+    vocab_size = _e("VOCAB_SIZE", 16384, int)
+    num_layers = _e("NUM_LAYERS", 10, int)
+    num_kv_heads = _e("NUM_KV_HEADS", 3, int)
+    model_dim = _e("MODEL_DIM", 576, int)
+    num_heads = _e("NUM_HEADS", 6, int)
+    mlp_mult = _e("MLP_MULT", 4, int)
+    tie_embeddings = _e("TIE_EMBEDDINGS", 1, int)
+    rope_base = _e("ROPE_BASE", 5000.0, float)
+    rope_type = _e("ROPE_TYPE", "yarn")
+    yarn_max_len = _e("YARN_MAX_LEN", 2048, int)
+    logit_softcap = _e("LOGIT_SOFTCAP", 10.0, float)
+    softcap_type = _e("SOFTCAP_TYPE", "poly")
+    tied_embed_init_std = _e("TIED_EMBED_INIT_STD", 0.005, float)
+    qk_gain_init = _e("QK_GAIN_INIT", 5.0, float)
+    activation_type = _e("ACTIVATION", "relu2")
+    embed_dim = _e("EMBED_DIM", 380, int)
+    embed_lr = _e("EMBED_LR", 0.6, float)
+    head_lr = _e("HEAD_LR", 0.02, float)
+    adam_lr = _e("ADAM_LR", 0.05, float)
+    adam_wd = _e("ADAM_WD", 0.05, float)
+    tied_embed_lr = _e("TIED_EMBED_LR", 0.02, float)
+    seq_len_start = _e("SEQ_LEN_START", 0, int)
+    seq_schedule_fraction = _e("SEQ_SCHEDULE_FRACTION", 0.0, float)
+    batch_tokens_start = _e("BATCH_TOKENS_START", 0, int)
+    batch_schedule_fraction = _e("BATCH_SCHEDULE_FRACTION", 0.33, float)
+    churn_log_every = _e("CHURN_LOG_EVERY", 0, int)
+    matrix_lr = _e("MATRIX_LR", 0.035, float)
+    scalar_lr = _e("SCALAR_LR", 0.02, float)
+    muon_momentum = _e("MUON_MOMENTUM", 0.95, float)
+    muon_backend_steps = _e("MUON_BACKEND_STEPS", 3, int)
+    muon_wd = _e("MUON_WD", 0.0, float)
+    matrix_optimizer = _e("MATRIX_OPTIMIZER", "muon")
+    muon_momentum_warmup_start = _e("MUON_MOMENTUM_WARMUP_START", 0.85, float)
+    muon_momentum_warmup_steps = _e("MUON_MOMENTUM_WARMUP_STEPS", 500, int)
+    beta1 = _e("BETA1", 0.9, float)
+    beta2 = _e("BETA2", 0.95, float)
+    adam_eps = _e("ADAM_EPS", 1e-8, float)
+    grad_clip_norm = _e("GRAD_CLIP_NORM", 0.0, float)
+    bitnet_group_size = _e("BITNET_GROUP_SIZE", 192, int)
+    ttt_steps = _e("TTT_STEPS", 3, int)
+    ttt_lr = _e("TTT_LR", 0.005, float)
+    ttt_tokens = _e("TTT_TOKENS", 32768, int)
+    skip_training = _e("SKIP_TRAINING", 0, bool)
+    scale_quant_bits = _e("SCALE_QUANT_BITS", 5, int)  # 5-bit log-delta scale quantization
+    fp_storage = True  # FP8 storage for non-quinary (small) tensors
+
+# Calibration / fp16-stored scalar+vector params adapted by score-first TTT.
+# `scale_correction` was previously listed here but its gradient is blocked by
+# the STE detach in QuinaryLinear.forward, so including it as a TTT target was
+# a no-op. Excluded from the CTP tuple so the TTT optimizer reflects what
+# actually receives gradients (~42k params, not 232k). The parameter still
+# exists in the state-dict and is stored fp16 in the artifact (via the ndim<2
+# bucket); it is just not selected for TTT adaptation.
+CTP = ("attn_scale","mlp_scale","resid_mix","q_gain","skip_weights","vocab_bias")
+
+def pack_quinary(q: Tensor):
+    f = (q.reshape(-1).to(torch.int8) + 2).numpy()  # {-2..2} -> {0..4}
+    n = len(f)
+    p = (3 - n % 3) % 3
+    if p: f = np.concatenate([f, np.zeros(p, dtype=np.int8)])
+    g = f.reshape(-1, 3).astype(np.uint8)
+    return (g[:,0] + g[:,1]*5 + g[:,2]*25).tobytes(), n  # max = 4+20+100 = 124
+
+def unpack_quinary(data: bytes, n: int) -> Tensor:
+    v = np.frombuffer(data, dtype=np.uint8).astype(np.int16)
+    t = np.zeros((len(v), 3), dtype=np.int8)
+    for i in range(3): t[:,i] = v % 5; v //= 5
+    return torch.from_numpy(t.reshape(-1)[:n].astype(np.int8) - 2)
+
+
+def pack_quinary_bitmask(q) -> tuple[bytes, int, int]:
+    """Pack quinary symbols ∈ {-2..2} as three concatenated bit-planes.
+
+    Layout (big-endian within bytes via numpy.packbits):
+      [zero_mask:    ceil(n_total / 8) bytes — 1 bit/symbol, 1=zero]
+      [sign_bits:    ceil(n_nonzero / 8) bytes — 1 bit/nonzero, 1=negative]
+      [mag2_bits:    ceil(n_nonzero / 8) bytes — 1 bit/nonzero, 1=|symbol|==2]
+
+    Returns: (concat_bytes, n_total, n_nonzero).
+    Each plane has homogeneous bit statistics so the downstream compressor
+    can model them independently rather than fighting a multimodal mixture.
+    """
+    if isinstance(q, torch.Tensor):
+        a = q.reshape(-1).to(torch.int8).numpy()
+    else:
+        a = np.asarray(q, dtype=np.int8).reshape(-1)
+    n_total = a.size
+    nz_mask = (a != 0)                            # bool, len n_total
+    n_nonzero = int(nz_mask.sum())
+    nz_vals = a[nz_mask]                          # int8, len n_nonzero
+    sign_bits = (nz_vals < 0).astype(np.uint8)    # 1 = negative
+    mag2_bits = (np.abs(nz_vals) == 2).astype(np.uint8)
+    zero_packed = np.packbits((~nz_mask).astype(np.uint8))  # 1 bit means "this symbol is zero"
+    sign_packed = np.packbits(sign_bits)
+    mag2_packed = np.packbits(mag2_bits)
+    return (zero_packed.tobytes() + sign_packed.tobytes() + mag2_packed.tobytes(),
+            n_total, n_nonzero)
+
+
+def unpack_quinary_bitmask(data: bytes, n_total: int, n_nonzero: int) -> Tensor:
+    """Inverse of pack_quinary_bitmask. Returns int8 tensor of length n_total."""
+    z_len = (n_total + 7) // 8
+    s_len = (n_nonzero + 7) // 8
+    m_len = (n_nonzero + 7) // 8
+    expected = z_len + s_len + m_len
+    if len(data) != expected:
+        raise ValueError(f"bitmask data size {len(data)} != expected {expected} "
+                         f"(n_total={n_total}, n_nonzero={n_nonzero})")
+    z_bytes = np.frombuffer(data[:z_len], dtype=np.uint8)
+    s_bytes = np.frombuffer(data[z_len:z_len + s_len], dtype=np.uint8)
+    m_bytes = np.frombuffer(data[z_len + s_len:], dtype=np.uint8)
+    is_zero = np.unpackbits(z_bytes)[:n_total].astype(bool)
+    sign = np.unpackbits(s_bytes)[:n_nonzero].astype(bool)
+    mag2 = np.unpackbits(m_bytes)[:n_nonzero].astype(bool)
+    nz_vals = np.where(mag2, 2, 1).astype(np.int8)
+    nz_vals = np.where(sign, -nz_vals, nz_vals)
+    out = np.zeros(n_total, dtype=np.int8)
+    out[~is_zero] = nz_vals
+    return torch.from_numpy(out)
+
+
+# Artifact archive: layout-aware per-stream v2 (header byte 0x03).
+#
+#   - For each quinary tensor: screen the 4 layouts {base5, base5_T,
+#     bitmask, bitmask_T} by LZMA9-compressed size, then run LZMA9 vs
+#     lrzip-zpaq -L9 only on the winning layout. Bounded heuristic
+#     with an LZMA floor — *not* an exhaustive 4×2 search.
+#   - For c_qkv.weight: split rows into Q / K / V sub-payloads each chosen
+#     independently (Q, K, V have different trained distributions).
+#   - For other bulk fields (FP8 embeddings, large fp16 tensors): torch.save
+#     the value and compress with min(lzma, lrzip).
+#   - Robust to the seed-dependent lrzip cliff observed on full-blob
+#     compression: even if lrzip ZPAQ underperforms on one tensor's bytes,
+#     lzma takes over for that tensor specifically.
+_COMPRESSOR_LZMA = 0
+_COMPRESSOR_LRZIP_ZPAQ = 1
+_COMPRESSOR_PER_STREAM_V2 = 3
+
+# Threshold for treating a value as "bulk" (compressed independently).
+_PER_STREAM_BULK_BYTES = 64 * 1024
+
+# v2 layout IDs — what kind of body is stored in a bulk payload.
+_LAYOUT_RAW         = 0  # opaque bytes (torch.save output for non-quinary fields)
+_LAYOUT_Q_BASE5     = 1  # base-5 packed quinary symbols, canonical row-major order
+_LAYOUT_Q_BASE5_T   = 2  # base-5 packed quinary symbols, transposed (column-major)
+_LAYOUT_Q_BITMASK   = 3  # bitmask packed (zero|sign|mag2), canonical
+_LAYOUT_Q_BITMASK_T = 4  # bitmask packed, transposed
+_LAYOUT_NAMES = {
+    _LAYOUT_RAW: "raw",
+    _LAYOUT_Q_BASE5: "base5",
+    _LAYOUT_Q_BASE5_T: "base5_T",
+    _LAYOUT_Q_BITMASK: "bitmask",
+    _LAYOUT_Q_BITMASK_T: "bitmask_T",
+}
+
+# v2 sentinel for metadata refs (distinct string from v1 to avoid version confusion).
+_BULK_SENTINEL_V2 = "__BULK_REF_V2__"
+
+
+def _lrzip_compress_bytes(data: bytes, level: int = 9) -> bytes:
+    """Compress raw bytes via `lrzip -z` (ZPAQ). Returns body only (no header)."""
+    with tempfile.NamedTemporaryFile(delete=False) as f:
+        f.write(data); in_path = f.name
+    out_path = in_path + ".lrz"
+    try:
+        try:
+            subprocess.run(["lrzip", "-z", "-L", str(level), "-q", "-f", in_path],
+                           check=True, capture_output=True)
+        except FileNotFoundError as e:
+            raise RuntimeError("lrzip binary not found; run `apt-get install lrzip`") from e
+        with open(out_path, "rb") as f:
+            return f.read()
+    finally:
+        for p in (in_path, out_path):
+            try: os.unlink(p)
+            except FileNotFoundError: pass
+
+
+def _lrzip_decompress_bytes(body: bytes) -> bytes:
+    """Decompress an lrzip ZPAQ body (no header)."""
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".lrz") as f:
+        f.write(body); in_path = f.name
+    out_path = in_path[:-4]
+    try:
+        try:
+            subprocess.run(["lrzip", "-d", "-q", "-f", in_path],
+                           check=True, capture_output=True)
+        except FileNotFoundError as e:
+            raise RuntimeError("lrzip binary not found; required to load this artifact") from e
+        with open(out_path, "rb") as f:
+            return f.read()
+    finally:
+        for p in (in_path, out_path):
+            try: os.unlink(p)
+            except FileNotFoundError: pass
+
+
+def _pick_best_compressor(data: bytes, level: int = 9, has_lrzip: bool = True) -> tuple[int, bytes]:
+    """Try lzma + (optionally) lrzip; return (method_id, body) for the smaller.
+
+    This is the per-stream robustness: even if one compressor cliffs on a
+    given byte distribution (the seed=7 issue), the other usually doesn't.
+    Any failure (missing binary, runtime error, broken lrzip backend) on
+    lrzip is silently skipped — lzma is the always-available floor.
+    """
+    candidates: list[tuple[int, bytes]] = [(_COMPRESSOR_LZMA, lzma.compress(data, preset=level))]
+    if has_lrzip:
+        try:
+            candidates.append((_COMPRESSOR_LRZIP_ZPAQ, _lrzip_compress_bytes(data, level)))
+        except (RuntimeError, subprocess.CalledProcessError, OSError):
+            pass
+    return min(candidates, key=lambda c: len(c[1]))
+
+
+def _is_bulk(value) -> bool:
+    """Decide whether a state_dict field should be peeled off for per-stream compression."""
+    if isinstance(value, (bytes, bytearray)):
+        return len(value) >= _PER_STREAM_BULK_BYTES
+    if isinstance(value, torch.Tensor):
+        return value.numel() * value.element_size() >= _PER_STREAM_BULK_BYTES
+    return False
+
+
+def _load_artifact(blob: bytes) -> dict:
+    """Load a per-stream v2 archive (header byte 0x03) into a state_dict."""
+    return _deserialize_per_stream_v2(blob)
+
+
+# ---------------------------------------------------------------------------
+# v2 archive: layout-aware per-stream compression
+# ---------------------------------------------------------------------------
+
+def _gen_quinary_layout_candidates(symbols_2d: np.ndarray) -> list[tuple[int, bytes, dict]]:
+    """For a 2D int8 symbol matrix in {-2..2}, return a list of
+    (layout_id, body_bytes, layout_meta) tuples — every supported layout.
+
+    layout_meta carries the per-layout fields needed at deserialize time
+    (n_quins, n_nonzero, etc.).
+    """
+    rows, cols = symbols_2d.shape
+    flat = symbols_2d.reshape(-1)
+    transposed = symbols_2d.T.reshape(-1).copy()  # contiguous transpose
+
+    # base-5 canonical
+    body_b5, n_b5 = pack_quinary(torch.from_numpy(flat))
+    # base-5 transposed
+    body_b5_t, n_b5_t = pack_quinary(torch.from_numpy(transposed))
+    # bitmask canonical
+    body_bm, nt_bm, nz_bm = pack_quinary_bitmask(torch.from_numpy(flat))
+    # bitmask transposed
+    body_bm_t, nt_bm_t, nz_bm_t = pack_quinary_bitmask(torch.from_numpy(transposed))
+
+    return [
+        (_LAYOUT_Q_BASE5,     body_b5,   {"n_quins": n_b5}),
+        (_LAYOUT_Q_BASE5_T,   body_b5_t, {"n_quins": n_b5_t}),
+        (_LAYOUT_Q_BITMASK,   body_bm,   {"n_total": nt_bm, "n_nonzero": nz_bm}),
+        (_LAYOUT_Q_BITMASK_T, body_bm_t, {"n_total": nt_bm_t, "n_nonzero": nz_bm_t}),
+    ]
+
+
+def _layout_to_canonical_bytes(layout: int, body: bytes, rows: int, cols: int,
+                               layout_meta: dict) -> bytes:
+    """Inverse of _gen_quinary_layout_candidates: take a body in one of the
+    layout encodings and return the canonical base-5-packed bytes that
+    `deq_sd` expects in entry["packed"]."""
+    if layout == _LAYOUT_Q_BASE5:
+        # Already canonical.
+        return body
+    if layout == _LAYOUT_Q_BASE5_T:
+        symbols_t = unpack_quinary(body, layout_meta["n_quins"]).numpy()
+        symbols = symbols_t.reshape(cols, rows).T.reshape(-1).copy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    if layout == _LAYOUT_Q_BITMASK:
+        symbols = unpack_quinary_bitmask(body, layout_meta["n_total"], layout_meta["n_nonzero"]).numpy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    if layout == _LAYOUT_Q_BITMASK_T:
+        symbols_t = unpack_quinary_bitmask(body, layout_meta["n_total"], layout_meta["n_nonzero"]).numpy()
+        symbols = symbols_t.reshape(cols, rows).T.reshape(-1).copy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    raise ValueError(f"unknown layout id {layout}")
+
+
+def _qkv_split_sizes(name: str, rows: int, cols: int) -> tuple[int, int] | None:
+    """For a c_qkv weight, derive (q_rows, kv_rows) row-counts.
+
+    Standard transformer convention: q_size = num_heads * head_dim = model_dim
+    (the square attention assumption). So q_rows == cols. The remaining rows
+    are split equally between K and V: kv_rows = (rows - cols) // 2.
+
+    Returns None if name doesn't look like c_qkv or the ratio doesn't decompose
+    cleanly (in which case we don't split, just compress as one tensor).
+    """
+    if not name.endswith("c_qkv.weight"):
+        return None
+    if rows <= cols:
+        return None  # not a GQA shape; or single-head case where Q==K==V
+    extra = rows - cols  # 2 * kv_rows
+    if extra % 2 != 0:
+        return None
+    kv_rows = extra // 2
+    q_rows = cols
+    if q_rows + 2 * kv_rows != rows:
+        return None
+    return q_rows, kv_rows
+
+
+def _serialize_per_stream_v2(state_dict: dict, level: int = 9) -> tuple[bytes, str]:
+    """v2 archive: layout-aware per-stream compression.
+
+    For each quinary entry, generate up to 4 layout candidates
+    (base5, base5_T, bitmask, bitmask_T), screen them by LZMA9-compressed
+    size, then compress the winning layout with min(LZMA9, lrzip-zpaq).
+    For c_qkv weights, the row-block is split into Q/K/V sub-payloads
+    handled independently. All other bulk fields use the v1 raw-bytes path.
+
+    This is a bounded-cost heuristic with an LZMA floor — *not* an
+    exhaustive 4×2 search. It can in principle miss a (layout, compressor)
+    pair where the LZMA-screen-loser would have won under lrzip; in
+    practice this is rare on this stack and the LZMA floor caps the
+    worst case at the canonical base5+LZMA encoding.
+
+    Returns (archive_bytes, audit_string) so the caller can route the
+    audit line through the run's logger.
+    """
+    from shutil import which
+    has_lrzip = which("lrzip") is not None
+
+    bulk_bodies: list[tuple[int, bytes]] = []  # (compressor_method, compressed_body)
+    metadata: dict = {}
+    audit_lines: list[str] = []
+    layout_counts = {n: 0 for n in _LAYOUT_NAMES.values()}
+    method_counts = {"lzma": 0, "lrzip": 0}
+    qkv_split_count = 0
+
+    def _store(layout: int, body: bytes) -> tuple[int, int, int]:
+        """Compress and store; return (payload_idx, method_id, compressed_size)."""
+        method, comp = _pick_best_compressor(body, level, has_lrzip)
+        bulk_bodies.append((method, comp))
+        method_counts["lzma" if method == _COMPRESSOR_LZMA else "lrzip"] += 1
+        layout_counts[_LAYOUT_NAMES[layout]] += 1
+        return len(bulk_bodies) - 1, method, len(comp)
+
+    def _best_quinary_layout(symbols_2d: np.ndarray) -> tuple[int, int, int, dict, int]:
+        """Generate all layout candidates, screen with lzma to pick best layout,
+        then compress winner with min(lzma, lrzip) for the final body.
+
+        Cheap-screen + refine cuts serialize compute from O(layouts*compressors)
+        to O(layouts) lzma + 1 lrzip, keeping serialize time bounded even when
+        lrzip is slow on bad streams.
+
+        Returns (chosen_layout, payload_idx, compressed_size, layout_meta, n_candidates_evaluated).
+        """
+        cands = _gen_quinary_layout_candidates(symbols_2d)
+        best_layout, best_body, best_meta = None, None, None
+        best_lzma_size = None
+        for layout, body, meta in cands:
+            lzma_size = len(lzma.compress(body, preset=level))
+            if best_lzma_size is None or lzma_size < best_lzma_size:
+                best_lzma_size = lzma_size
+                best_layout, best_body, best_meta = layout, body, meta
+        # Now compress the winning layout with min(lzma, lrzip) for the actual stored body.
+        comp_method, comp_body = _pick_best_compressor(best_body, level, has_lrzip)
+        bulk_bodies.append((comp_method, comp_body))
+        method_counts["lzma" if comp_method == _COMPRESSOR_LZMA else "lrzip"] += 1
+        layout_counts[_LAYOUT_NAMES[best_layout]] += 1
+        return best_layout, len(bulk_bodies) - 1, len(comp_body), best_meta, len(cands)
+
+    for name, entry in state_dict.items():
+        if not isinstance(entry, dict):
+            metadata[name] = entry
+            continue
+        new_entry = dict(entry)  # shallow copy
+
+        if entry.get("type") == "quinary" and "packed" in entry:
+            # Reconstruct full symbol matrix from canonical packed bytes.
+            rows, cols = entry["shape"][0], entry["padded_cols"]
+            symbols_full = unpack_quinary(entry["packed"], entry["n_quins"]).numpy()
+            symbols_2d = symbols_full[:rows * cols].reshape(rows, cols)
+
+            split = _qkv_split_sizes(name, rows, cols)
+            if split is not None:
+                q_rows, kv_rows = split
+                qkv_split_count += 1
+                refs = []
+                for sub_name, sub_2d in (
+                    ("Q", symbols_2d[:q_rows]),
+                    ("K", symbols_2d[q_rows:q_rows + kv_rows]),
+                    ("V", symbols_2d[q_rows + kv_rows:]),
+                ):
+                    layout, idx, sz, meta, _ = _best_quinary_layout(sub_2d.copy())
+                    sub_rows, sub_cols = sub_2d.shape
+                    refs.append({
+                        "_v2": _BULK_SENTINEL_V2, "idx": idx, "layout": layout,
+                        "rows": sub_rows, "cols": sub_cols, "meta": meta,
+                    })
+                new_entry["packed"] = ("__QKV_SPLIT__", refs)
+                audit_lines.append(f"  {name}: qkv_split layouts={[_LAYOUT_NAMES[r['layout']] for r in refs]}")
+            else:
+                layout, idx, sz, meta, _ = _best_quinary_layout(symbols_2d.copy())
+                new_entry["packed"] = {
+                    "_v2": _BULK_SENTINEL_V2, "idx": idx, "layout": layout,
+                    "rows": rows, "cols": cols, "meta": meta,
+                }
+                audit_lines.append(f"  {name}: {_LAYOUT_NAMES[layout]} ({sz/1e6:.3f}MB)")
+
+        # Other bulk fields (fp8 data, fp16 large tensors, scale_delta tensors,
+        # etc.) — fall back to v1's torch.save+pick path.
+        for key, value in list(new_entry.items()):
+            if key == "packed":  # already handled
+                continue
+            if _is_bulk(value):
+                buf = io.BytesIO(); torch.save(value, buf)
+                method, comp = _pick_best_compressor(buf.getvalue(), level, has_lrzip)
+                bulk_bodies.append((method, comp))
+                method_counts["lzma" if method == _COMPRESSOR_LZMA else "lrzip"] += 1
+                layout_counts["raw"] += 1
+                new_entry[key] = {
+                    "_v2": _BULK_SENTINEL_V2, "idx": len(bulk_bodies) - 1,
+                    "layout": _LAYOUT_RAW,
+                }
+        metadata[name] = new_entry
+
+    # Compress metadata.
+    meta_buf = io.BytesIO(); torch.save(metadata, meta_buf)
+    meta_compressed = lzma.compress(meta_buf.getvalue(), preset=level)
+
+    # Assemble archive (header 0x03).
+    out = bytearray()
+    out.append(_COMPRESSOR_PER_STREAM_V2)
+    out.extend(len(meta_compressed).to_bytes(4, "little"))
+    out.extend(meta_compressed)
+    out.extend(len(bulk_bodies).to_bytes(4, "little"))
+    for method, body in bulk_bodies:
+        out.append(method)
+        out.extend(len(body).to_bytes(4, "little"))
+        out.extend(body)
+
+    layout_summary = ", ".join(f"{n}={c}" for n, c in layout_counts.items() if c)
+    audit = (f"per_stream_v2: meta={len(meta_compressed)/1e6:.2f}MB, "
+             f"{len(bulk_bodies)} bulk payloads "
+             f"(lzma={method_counts['lzma']}, lrzip={method_counts['lrzip']}; "
+             f"{layout_summary}), qkv_splits={qkv_split_count}, "
+             f"total={len(out)/1e6:.2f}MB")
+    return bytes(out), audit
+
+
+def _deserialize_per_stream_v2(blob: bytes) -> dict:
+    """Inverse of _serialize_per_stream_v2. Returns a state_dict whose quinary
+    entries have canonical entry["packed"] bytes — `deq_sd` is unaware of the
+    archive layer."""
+    cursor = 0
+    assert blob[cursor] == _COMPRESSOR_PER_STREAM_V2, \
+        f"expected v2 header 0x03, got {blob[0]:#x}"
+    cursor += 1
+    meta_size = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+    meta_blob = blob[cursor:cursor + meta_size]; cursor += meta_size
+    metadata = torch.load(io.BytesIO(lzma.decompress(meta_blob)),
+                          map_location="cpu", weights_only=False)
+    n_payloads = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+    decompressed: list[bytes] = []
+    for _ in range(n_payloads):
+        method = blob[cursor]; cursor += 1
+        size = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+        body = blob[cursor:cursor + size]; cursor += size
+        if method == _COMPRESSOR_LZMA:
+            decompressed.append(lzma.decompress(body))
+        elif method == _COMPRESSOR_LRZIP_ZPAQ:
+            decompressed.append(_lrzip_decompress_bytes(body))
+        else:
+            raise ValueError(f"unknown compressor method {method:#x}")
+
+    def _is_v2_ref(v):
+        return isinstance(v, dict) and v.get("_v2") == _BULK_SENTINEL_V2
+
+    for name, entry in metadata.items():
+        if not isinstance(entry, dict):
+            continue
+        for key, value in list(entry.items()):
+            if _is_v2_ref(value):
+                if value["layout"] == _LAYOUT_RAW:
+                    entry[key] = torch.load(io.BytesIO(decompressed[value["idx"]]),
+                                            map_location="cpu", weights_only=False)
+                else:
+                    entry[key] = _layout_to_canonical_bytes(
+                        value["layout"], decompressed[value["idx"]],
+                        value["rows"], value["cols"], value["meta"])
+            elif (isinstance(value, tuple) and len(value) == 2
+                  and value[0] == "__QKV_SPLIT__"):
+                # Reconstruct each part's symbol matrix, concat along row dim,
+                # repack as canonical base-5.
+                refs = value[1]
+                sub_symbols = []
+                for ref in refs:
+                    canonical_body = _layout_to_canonical_bytes(
+                        ref["layout"], decompressed[ref["idx"]],
+                        ref["rows"], ref["cols"], ref["meta"])
+                    n = ref["rows"] * ref["cols"]
+                    sub_syms = unpack_quinary(canonical_body, n).numpy()[:n]
+                    sub_symbols.append(sub_syms.reshape(ref["rows"], ref["cols"]))
+                full = np.concatenate(sub_symbols, axis=0).reshape(-1).copy()
+                entry[key], _ = pack_quinary(torch.from_numpy(full))
+    return metadata
+
+
+def _quant_scale(scale_fp16: Tensor, bits: int) -> dict:
+    """Quantize per-group scales to bits-bit log-deltas. Returns dict with
+    `scale_anchor` (fp16), `scale_step` (fp16), `scale_delta` (packed bytes
+    or int8 tensor), plus `scale_bits` to dispatch in deq_sd."""
+    scale_f = scale_fp16.float().reshape(-1)
+    n_groups = scale_f.numel()
+    log_scale = torch.log2(scale_f.clamp(min=1e-12))
+    lo, hi = float(log_scale.min().item()), float(log_scale.max().item())
+    anchor = (lo + hi) / 2.0
+    spread = hi - lo
+    levels = 1 << bits
+    half = levels // 2
+    step = max(spread / max(levels - 1, 1), 1e-9)
+    delta = ((log_scale - anchor) / step).round().clamp(-half, half - 1).to(torch.int32)
+    if bits == 4:
+        d = (delta + half).to(torch.uint8).numpy()
+        if len(d) % 2:
+            d = np.concatenate([d, np.zeros(1, dtype=np.uint8)])
+        packed = (d[::2] | (d[1::2] << 4)).astype(np.uint8)
+        delta_stored = torch.from_numpy(packed.copy())
+    elif bits == 5:
+        delta_stored = (delta + half).to(torch.uint8)
+    else:  # 8
+        delta_stored = delta.to(torch.int8)
+    return {
+        "scale_anchor": torch.tensor(anchor, dtype=torch.float16),
+        "scale_step": torch.tensor(step, dtype=torch.float16),
+        "scale_delta": delta_stored,
+        "scale_n_groups": n_groups,
+        "scale_bits": bits,
+    }
+
+def _dequant_scale(entry: dict) -> Tensor:
+    """Reconstruct fp16 per-group scales from the quantized representation."""
+    bits = int(entry["scale_bits"])
+    half = (1 << bits) // 2
+    n_groups = int(entry["scale_n_groups"])
+    if bits == 4:
+        packed = entry["scale_delta"].to(torch.uint8).numpy()
+        low = packed & 0x0F
+        high = (packed >> 4) & 0x0F
+        d = np.empty(packed.size * 2, dtype=np.uint8)
+        d[0::2] = low
+        d[1::2] = high
+        delta = torch.from_numpy(d[:n_groups].astype(np.int32) - half)
+    elif bits == 5:
+        delta = entry["scale_delta"].to(torch.int32) - half
+    else:  # 8
+        delta = entry["scale_delta"].to(torch.int32)
+    anchor = entry["scale_anchor"].float()
+    step = entry["scale_step"].float()
+    return (2.0 ** (anchor + delta.float() * step)).to(torch.float16)
+
+
+def q_sd(state_dict: dict, group_size: int = 64,
+         scale_quant_bits: int = 0) -> tuple[dict, dict]:
+    quantized = {}
+    stats = {"quinary_params": 0, "quinary_bytes": 0, "fp_params": 0, "fp_bytes": 0}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().float().contiguous()
+        t_orig_shape = list(t.shape)
+        if t.ndim == 3:
+            t = t.reshape(t.shape[0], -1)
+        is_quantized_candidate = (
+            t.ndim == 2 and t.numel() > 65_536
+            and "tok_emb" not in name and "lm_head" not in name and "embed_proj" not in name
+        )
+        if is_quantized_candidate:
+            pad = (group_size - t.shape[1] % group_size) % group_size
+            t_padded = F.pad(t, (0, pad)) if pad > 0 else t
+            t_grouped = t_padded.reshape(-1, group_size)
+            scale = t_grouped.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+            q = (t_grouped / scale).round().clamp(-2, 2).to(torch.int8)
+
+            packed_bytes, n_packed = pack_quinary(q)
+            stats["quinary_params"] += t.numel()
+
+            entry = {
+                "type": "quinary", "packed": packed_bytes,
+                "shape": list(t.shape), "padded_cols": t_padded.shape[1],
+                "group_size": group_size, "n_quins": n_packed,
+                "orig_shape": t_orig_shape,
+            }
+            if scale_quant_bits and scale_quant_bits in (4, 5, 8):
+                sq = _quant_scale(scale.half().squeeze(-1), scale_quant_bits)
+                entry.update(sq)
+                stats["quinary_bytes"] += len(packed_bytes) + sq["scale_delta"].numel() + 4  # +4 for anchor+step
+            else:
+                entry["scale"] = scale.half().squeeze(-1)
+                stats["quinary_bytes"] += len(packed_bytes) + scale.numel() * 2
+            quantized[name] = entry
+        elif t.ndim == 2:
+            quantized[name] = {"type": "fp8", "data": t.to(torch.float8_e4m3fn)}
+            stats["fp_params"] += t.numel()
+            stats["fp_bytes"] += t.numel()
+        else:
+            quantized[name] = {"type": "fp16", "data": t.half()}
+            stats["fp_params"] += t.numel()
+            stats["fp_bytes"] += t.numel() * 2
+    return quantized, stats
+
+def deq_sd(quantized: dict, target_dtype=torch.bfloat16):
+    out = {}
+    for name, entry in quantized.items():
+        if entry["type"] == "quinary":
+            q = unpack_quinary(entry["packed"], entry["n_quins"])
+            q = q.float().reshape(-1, entry["group_size"])
+            if "scale_bits" in entry:
+                scale = _dequant_scale(entry).float().unsqueeze(-1)
+            else:
+                scale = entry["scale"].float().unsqueeze(-1)
+            q_absmean = q.abs().mean(-1, keepdim=True).clamp(min=1e-8)
+            t = (q * (scale / q_absmean)).reshape(-1, entry["padded_cols"])
+            shape = entry["shape"]
+            result = t[:shape[0], :shape[1]].to(target_dtype)
+            orig = entry.get("orig_shape")
+            out[name] = result.reshape(orig).contiguous() if orig and orig != shape else result.contiguous()
+        elif entry["type"] == "fp8":
+            out[name] = entry["data"].to(torch.float32).to(target_dtype).contiguous()
+        else:
+            out[name] = entry["data"].to(target_dtype).contiguous()
+    return out
+
+def quin_stats(model: nn.Module, group_size: int = 64):
+    total = zeros = 0
+    with torch.no_grad():
+        for name, p in model.named_parameters():
+            if p.ndim == 2 and ("weight" in name or "prototypes" in name) and p.shape[0] > 1 and p.numel() % group_size == 0:
+                w = p.detach().float().reshape(-1, group_size)
+                scale = w.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+                q = (w / scale).round().clamp(-2, 2)
+                zeros += int((q == 0).sum().item())
+                total += int(q.numel())
+    return {"zero_frac": zeros / max(total, 1), "total_weights": total}
+
+_prev_committed: dict = {}
+
+def churn_fn(model: nn.Module, group_size: int = 64):
+    global _prev_committed
+    total = flipped = 0
+    with torch.no_grad():
+        for name, p in model.named_parameters():
+            if p.ndim == 2 and ("weight" in name or "prototypes" in name) and p.shape[0] > 1 and p.numel() % group_size == 0:
+                w = p.detach().float().reshape(-1, group_size)
+                scale = w.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+                q = (w / scale).round().clamp(-2, 2).cpu().numpy()
+                if name in _prev_committed:
+                    flipped += int(np.sum(q != _prev_committed[name]))
+                    total += q.size
+                _prev_committed[name] = q
+    return flipped / max(total, 1)
+
+def ns_orth(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, wd: float = 0.0):
+        super().__init__(params, dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, wd=wd))
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr, momentum = group["lr"], group["momentum"]
+            backend_steps, nesterov = group["backend_steps"], group["nesterov"]
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = F.rms_norm(g.float(), (g.size(-1),)).bfloat16()
+                    g = ns_orth(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr:curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+            wd = group.get("wd", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.mul_(1 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+def ld_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = ld_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self):
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = ld_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos:self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank, self.world_size, self.device = rank, world_size, device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start:start + per_rank_span].pin_memory().to(self.device, non_blocking=True).to(torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x, y
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+def apply_fp8_ste(w: Tensor) -> Tensor:
+    w_sim = w.to(torch.float8_e4m3fn).to(w.dtype)
+    return (w_sim - w).detach() + w
+
+class QATLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w_qat = apply_fp8_ste(self.weight)
+        return F.linear(x, w_qat.to(x.dtype), self.bias.to(x.dtype) if self.bias is not None else None)
+
+class QATEmbedding(nn.Embedding):
+    def forward(self, input: Tensor) -> Tensor:
+        w_qat = apply_fp8_ste(self.weight)
+        return F.embedding(input, w_qat, self.padding_idx, self.max_norm,
+                           self.norm_type, self.scale_grad_by_freq, self.sparse)
+
+class QuinaryLinear(nn.Linear):
+    def __init__(self, in_features, out_features, bias=False, group_size=64):
+        super().__init__(in_features, out_features, bias=bias)
+        self.group_size = group_size
+        num_groups = (in_features * out_features) // group_size
+        # Inert by design: the STE detach below blocks gradients to
+        # `scale_correction`. Kept as a fp32 buffer at value 1.0 for
+        # backwards-compatibility with the ternary-base state-dict layout.
+        # An attempt to fix the STE so this parameter receives gradients
+        # was tested 2026-05-01 (commit b9c…) and showed a small
+        # training-time regression with no TTT benefit, so reverted.
+        self.scale_correction = nn.Parameter(torch.ones(num_groups, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.bfloat16()
+        g = self.group_size
+        w_g = w.reshape(-1, g)
+        scale = w_g.abs().mean(-1, keepdim=True).clamp(min=1e-8) * self.scale_correction.to(w.dtype).unsqueeze(-1)
+        q = (w_g / scale).round().clamp(-2, 2)
+        w_quantized = w + ((q * scale).reshape(w.shape) - w).detach()
+        return F.linear(x, w_quantized,
+                        self.bias.to(x.dtype) if self.bias is not None else None)
+
+
+class NormedQuinaryLinear(QuinaryLinear):
+    def forward(self, x: Tensor) -> Tensor:
+        return super().forward(F.rms_norm(x, (x.size(-1),)))
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(p in name for p in CTP)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0, no_cache: bool = False,
+                 rope_type: str = "rope", yarn_max_len: int = 4096, train_seq_len: int = 1024):
+        super().__init__()
+        self.no_cache = no_cache
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        if rope_type == "yarn":
+            scale = train_seq_len / yarn_max_len
+            freq_idx = torch.arange(0, dim, 2, dtype=torch.float32)
+            ramp = torch.clamp((freq_idx / dim - 0.25) / 0.75, 0.0, 1.0)
+            inv_freq = inv_freq / (ramp * (1.0 / scale - 1.0) + 1.0)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len, device, dtype):
+        if self.no_cache:
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            return freqs.cos()[None, :, None, :].to(dtype=dtype), freqs.sin()[None, :, None, :].to(dtype=dtype)
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, :, None, :]
+            self._sin_cached = freqs.sin()[None, :, None, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init,
+                 group_size=64, no_cache=False, rope_type="rope",
+                 yarn_max_len=4096, train_seq_len=1024):
+        super().__init__()
+        self.num_heads, self.num_kv_heads = num_heads, num_kv_heads
+        self.head_dim = dim // num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        self.c_qkv = QuinaryLinear(dim, self.q_size + 2 * self.kv_size, bias=False, group_size=group_size)
+        self.proj = NormedQuinaryLinear(dim, dim, bias=False, group_size=group_size)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base, no_cache=no_cache,
+                             rope_type=rope_type, yarn_max_len=yarn_max_len,
+                             train_seq_len=train_seq_len)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        qkv_out = self.c_qkv(x)
+        q_out, k_out, v_out = qkv_out.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q = q_out.reshape(bsz, seqlen, self.num_heads, self.head_dim)
+        k = k_out.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        v = v_out.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        q, k = F.rms_norm(q, (q.size(-1),)), F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None]
+        y = flash_attn_func(q.contiguous(), k.contiguous(), v.contiguous(), causal=True)
+        y = y.reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+class MLP(nn.Module):
+    def __init__(self, dim, mlp_mult, group_size=64, activation="swiglu"):
+        super().__init__()
+        hidden = mlp_mult * dim
+        self.activation = activation
+        if activation == "swiglu":
+            self.gate_up = QuinaryLinear(dim, hidden * 2, bias=False, group_size=group_size)
+        else:
+            self.fc = QuinaryLinear(dim, hidden, bias=False, group_size=group_size)
+        self.proj = NormedQuinaryLinear(hidden, dim, bias=False, group_size=group_size)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        if self.activation == "swiglu":
+            gu = self.gate_up(x)
+            gate, up = gu.chunk(2, dim=-1)
+            return self.proj(F.silu(gate) * up)
+        elif self.activation == "relu":
+            return self.proj(torch.relu(self.fc(x)))
+        elif self.activation == "leaky_relu":
+            return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.01))
+        elif self.activation == "leaky_relu2":
+            return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square())
+        else:  # relu2
+            return self.proj(torch.relu(self.fc(x)).square())
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int,
+                 rope_base: float, qk_gain_init: float, group_size: int=64,
+                 activation: str="swiglu", no_cache: bool=False,
+                 rope_type: str="rope", yarn_max_len: int=4096,
+                 train_seq_len: int=1024):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init,
+                                        group_size, no_cache, rope_type, yarn_max_len, train_seq_len)
+        self.mlp = MLP(dim, mlp_mult, group_size, activation)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x_in = mix[0] * x + mix[1] * x0
+        n = self.attn_norm(x_in)
+        x = x_in + self.attn_scale.to(dtype=x_in.dtype) * self.attn(n)
+        x = x + self.mlp_scale.to(dtype=x.dtype) * self.mlp(self.mlp_norm(x))
+        return x
+
+class GPT(nn.Module):
+    def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, mlp_mult,
+                 tie_embeddings, tied_embed_init_std, logit_softcap, rope_base, qk_gain_init,
+                 group_size: int = 64, activation: str = "swiglu",
+                 embed_dim: int = 0, softcap_type: str="poly", no_cache: bool=False,
+                 rope_type: str="rope", yarn_max_len: int=4096, train_seq_len: int=1024):
+        super().__init__()
+        self.tie_embeddings = tie_embeddings
+        self.logit_softcap = logit_softcap
+        self.softcap_type = softcap_type
+        self.embed_dim = embed_dim if embed_dim > 0 else model_dim
+        self.tok_emb = QATEmbedding(vocab_size, self.embed_dim)
+        self.embed_proj = QATLinear(self.embed_dim, model_dim, bias=False) if self.embed_dim != model_dim else None
+        self.embed_proj_rev = QATLinear(model_dim, self.embed_dim, bias=False) if self.embed_dim != model_dim else None
+
+        self.blocks = nn.ModuleList([
+            Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init,
+                  group_size, activation, no_cache, rope_type, yarn_max_len, train_seq_len)
+            for _ in range(num_layers)
+        ])
+
+        # U-Net split: first half encoder, second half decoder, decoder layers
+        # add a learned-weighted skip from the symmetric encoder layer.
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+
+        self.final_norm = RMSNorm()
+        self.lm_head = QATLinear(model_dim, vocab_size, bias=False)
+        self.lm_head._zero_init = True
+        if tie_embeddings:
+            self.lm_head.weight.requires_grad_(False)
+
+        self.vocab_bias = nn.Parameter(torch.zeros(vocab_size, dtype=torch.float32))
+        self._init_weights(tied_embed_init_std)
+
+    def _init_weights(self, tied_embed_init_std: float) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=tied_embed_init_std)
+        for module in self.modules():
+            if isinstance(module, QuinaryLinear) and not getattr(module, "_zero_init", False):
+                nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            elif isinstance(module, nn.Linear) and getattr(module, "_zero_init", False):
+                nn.init.zeros_(module.weight)
+
+    def _compute_logits(self, x: Tensor) -> Tensor:
+        if self.tie_embeddings:
+            proj = self.embed_proj_rev(x) if self.embed_proj_rev is not None else x
+            logits_raw = F.linear(proj, self.tok_emb.weight.to(x.dtype))
+        else:
+            logits_raw = self.lm_head(x)
+        return logits_raw + self.vocab_bias.to(x.dtype)
+
+    def _softcap(self, logits: Tensor) -> Tensor:
+        s = self.logit_softcap
+        if self.softcap_type == "tanh":
+            return s * torch.tanh(logits / s)
+        x_sc = torch.clamp(logits / s, -2.0, 2.0)
+        x2 = x_sc * x_sc
+        return s * torch.clamp(x_sc * (1.0 - x2 / 3.0 + x2 * x2 / 15.0), -1.0, 1.0)
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor, reduction: str = "mean") -> Tensor:
+        x = self.tok_emb(input_ids).float()
+        if self.embed_proj is not None:
+            x = self.embed_proj(x)
+        x = F.rms_norm(x, (x.size(-1),))
+        x0 = x
+
+        # U-Net encoder/decoder with skip connections.
+        skips = []
+        for vi in range(self.num_encoder_layers):
+            x = self.blocks[vi](x, x0)
+            skips.append(x)
+        for vi in range(self.num_decoder_layers):
+            if vi < self.num_skip_weights:
+                x = x + self.skip_weights[vi].to(dtype=x.dtype) * skips.pop()
+            x = self.blocks[self.num_encoder_layers + vi](x, x0)
+
+        x_normed = self.final_norm(x)
+        x_flat = x_normed.reshape(-1, x_normed.size(-1))
+        targets = target_ids.reshape(-1)
+        logits = self._softcap(self._compute_logits(x_flat))
+
+        if reduction == "none":
+            return F.cross_entropy(logits.float(), targets, reduction="none").reshape(input_ids.shape)
+
+        # Fused CE: single logsumexp; z-loss only during training (not eval BPB).
+        logits_f = logits.float()
+        lse = torch.logsumexp(logits_f, dim=-1)
+        target_logits = logits_f.gather(1, targets.unsqueeze(1)).squeeze(1)
+        main_loss = (lse - target_logits).mean()
+        if self.training:
+            main_loss = main_loss + 1e-4 * (lse ** 2).mean()
+        return main_loss
+
+
+def build_luts(sp, vocab_size: int, device: torch.device):
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+def ld_val(pattern, seq_len, max_tok=int(os.environ.get("VAL_MAX_TOKENS", 0))):
+    files = sorted(glob.glob(pattern))
+    assert files, f"No files: {pattern}"
+    tok = torch.cat([ld_shard(Path(p)) for p in files]).contiguous()
+    if max_tok > 0: tok = tok[:max_tok + 1]
+    u = ((tok.numel() - 1) // seq_len) * seq_len
+    return tok[:u + 1]
+
+
+def eval_val(args, model, rank, world_size, device, grad_accum_steps, val_tokens,
+             base_bytes_lut, has_leading_space_lut, is_boundary_token_lut):
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    local_batch_seqs = max(1, local_batch_tokens // args.train_seq_len)
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.no_grad():
+        for batch_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_end = min(batch_start + local_batch_seqs, seq_end)
+            raw_start = batch_start * args.train_seq_len
+            raw_end = batch_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64)
+            x, y = local[:-1].reshape(-1, args.train_seq_len), local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                batch_loss = model(x, y).detach()
+            n = float(y.numel())
+            loss_sum += batch_loss.to(torch.float64) * n
+            token_count += n
+            prev_ids, tgt_ids = x.reshape(-1), y.reshape(-1)
+            tok_bytes = base_bytes_lut[tgt_ids].to(torch.int16)
+            tok_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(torch.int16)
+            byte_count += tok_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        for t in (loss_sum, token_count, byte_count):
+            dist.all_reduce(t, op=dist.ReduceOp.SUM)
+    val_loss = loss_sum / token_count
+    bpb = (val_loss.item() / math.log(2.0)) * (token_count.item() / byte_count.item())
+    model.train()
+    return float(val_loss.item()), float(bpb), int(token_count.item()), int(byte_count.item())
+
+def main() -> None:
+    args = Hyperparameters()
+    code = Path(__file__).read_text(encoding="utf-8")
+
+    if args.matrix_optimizer != "adamw":
+        global ns_orth
+        ns_orth = torch.compile(ns_orth)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    grad_accum_steps = max(1, 8 // world_size)
+    grad_scale = 1.0 / grad_accum_steps
+
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+    os.makedirs("logs/cuda/", exist_ok=True)
+    logfile = f"logs/cuda/{args.run_id}.txt" if master_process else None
+    if master_process:
+        print(logfile)
+        # Truncate any pre-existing logfile from a previous run with the same
+        # RUN_ID so the per-RUN_ID log file isn't append-stacked across reruns.
+        if logfile:
+            open(logfile, "w", encoding="utf-8").close()
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+
+    log0(f"Python {sys.version}", console=False)
+    log0(f"PyTorch {torch.__version__}", console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    # Guard against an obvious tokenizer/vocab-size mismatch that would
+    # silently produce a wrong byte-count LUT (and therefore a wrong BPB
+    # denominator) without crashing.
+    assert sp.vocab_size() == args.vocab_size, (
+        f"tokenizer vocab_size ({sp.vocab_size()}) != args.vocab_size "
+        f"({args.vocab_size}); check TOKENIZER_PATH / VOCAB_SIZE")
+    val_tokens = ld_val(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_luts(
+        sp, args.vocab_size, device)
+
+    # --- Model ---
+    base_model = GPT(
+        vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim,
+        num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init,
+        group_size=args.bitnet_group_size, activation=args.activation_type,
+        embed_dim=args.embed_dim,
+        softcap_type=args.softcap_type, no_cache=(args.compile_mode == "reduce-overhead"),
+        rope_type=args.rope_type, yarn_max_len=args.yarn_max_len, train_seq_len=args.train_seq_len,
+    ).to(device).bfloat16()
+
+    for module in base_model.modules():
+        if isinstance(module, nn.Linear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+    if args.tie_embeddings:
+        base_model.lm_head.weight.requires_grad_(False)
+
+    torch._dynamo.config.optimize_ddp = False
+
+    compiled_model = torch.compile(base_model, mode=args.compile_mode if args.compile_mode != "default" else None)
+    use_find_unused = not args.tie_embeddings
+    model = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False,
+                find_unused_parameters=use_find_unused,
+                static_graph=not use_find_unused,
+                gradient_as_bucket_view=True) if distributed else compiled_model
+
+    # --- Optimizers ---
+    _excl = {"tok_emb.weight", "lm_head.weight"}
+    all_other_params = [(n, p) for n, p in base_model.named_parameters()
+                        if not any(eh in n for eh in _excl)]
+    matrix_params = [p for n, p in all_other_params
+                     if p.ndim == 2 and not any(pat in n for pat in CTP)]
+    scalar_params = [p for n, p in all_other_params
+                     if p.ndim < 2 or any(pat in n for pat in CTP)]
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    opt_tok = torch.optim.Adam(
+        [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+    if args.matrix_optimizer == "adamw":
+        opt_muon = torch.optim.AdamW(
+            [{"params": matrix_params, "lr": args.adam_lr, "base_lr": args.adam_lr}],
+            betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True)
+    else:
+        opt_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum,
+                        backend_steps=args.muon_backend_steps, wd=args.muon_wd)
+    for g in opt_muon.param_groups:
+        g["base_lr"] = args.matrix_lr
+    opt_scalar = torch.optim.Adam(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+    opt_head = torch.optim.Adam(
+        [{"params": [base_model.lm_head.weight], "lr": 0.0, "base_lr": 0.0}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+
+    optimizers = [opt_tok, opt_muon, opt_scalar, opt_head]
+
+    # --- Log all hyperparameters ---
+    log0("--- Hyperparameters ---", console=False)
+    log0(" ".join(f"{a}={getattr(args,a)}" for a in sorted(dir(args)) if not a.startswith("_") and a not in ("train_files","val_files") and not callable(getattr(args,a))), console=False)
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"params:{n_params} L:{args.num_layers} d:{args.model_dim} h:{args.num_heads} kv:{args.num_kv_heads} ws:{world_size} ga:{grad_accum_steps} s:{args.seed}")
+
+    # --- Data loader & helpers ---
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all():
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float):
+        if args.warmdown_fraction <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = int(args.iterations * (1.0 - args.warmdown_fraction))
+            ratio = max((args.iterations - step) / max(args.iterations * args.warmdown_fraction, 1), 0.0) if step >= warmdown_start else 1.0
+        else:
+            warmdown_ms = max_wallclock_ms * args.warmdown_fraction
+            remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+            ratio = remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+        return max(ratio, args.min_lr)
+
+    _seq_switched = False
+    _batch_switched = False
+    active_seq_len = args.seq_len_start if args.seq_len_start > 0 else args.train_seq_len
+    active_batch_tokens = args.batch_tokens_start if args.batch_tokens_start > 0 else args.train_batch_tokens
+
+    if args.skip_training:
+        log0("skip_training=1, using existing artifact final_model.quinary.ptz")
+
+    # --- Compiler warmup ---
+    if args.warmup_steps > 0 and not args.skip_training:
+        _ms = {n: t.detach().cpu().clone() for n, t in base_model.state_dict().items()}
+        _os = [copy.deepcopy(o.state_dict()) for o in optimizers]
+        model.train()
+        for ws in range(args.warmup_steps):
+            zero_grad_all()
+            for mi in range(grad_accum_steps):
+                if distributed: model.require_backward_grad_sync = mi == grad_accum_steps - 1
+                x, y = train_loader.next_batch(active_batch_tokens, active_seq_len, grad_accum_steps)
+                torch.compiler.cudagraph_mark_step_begin()
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16): loss = model(x, y)
+                (loss * grad_scale).backward()
+            for o in optimizers: o.step()
+            zero_grad_all()
+            log0(f"warmup:{ws+1}/{args.warmup_steps}")
+        base_model.load_state_dict(_ms, strict=True)
+        for o, s in zip(optimizers, _os): o.load_state_dict(s)
+        zero_grad_all()
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # --- Main training loop ---
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    train_loss = torch.zeros((), device=device)
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    step = 0
+
+    while not args.skip_training:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        if last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0):
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb, val_tok_count, val_byte_count = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut)
+            tstats = quin_stats(base_model, group_size=args.bitnet_group_size)
+            log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                 f"train_time:{training_time_ms:.0f}ms zero_frac:{tstats['zero_frac']:.3f} "
+                 f"eval_tokens:{val_tok_count} eval_bytes:{val_byte_count}")
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}")
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+
+        # Sequence length schedule
+        if args.seq_len_start > 0 and not _seq_switched:
+            if max_wallclock_ms is not None:
+                should_switch_seq = elapsed_ms >= args.seq_schedule_fraction * max_wallclock_ms
+            else:
+                should_switch_seq = step >= int(args.iterations * args.seq_schedule_fraction)
+            if should_switch_seq:
+                active_seq_len = args.train_seq_len
+                _seq_switched = True
+                torch._dynamo.reset()
+                train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+                log0(f"step:{step} seq_len_switch:{args.seq_len_start}->{active_seq_len}")
+
+        # Batch size schedule
+        if args.batch_tokens_start > 0 and not _batch_switched:
+            if max_wallclock_ms is not None:
+                should_switch_batch = elapsed_ms >= args.batch_schedule_fraction * max_wallclock_ms
+            else:
+                should_switch_batch = step >= int(args.iterations * args.batch_schedule_fraction)
+            if should_switch_batch:
+                active_batch_tokens = args.train_batch_tokens
+                _batch_switched = True
+                log0(f"step:{step} batch_switch:{args.batch_tokens_start}->{active_batch_tokens}")
+
+        zero_grad_all()
+        train_loss.zero_()
+
+        for micro in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro == grad_accum_steps - 1
+            x, y = train_loader.next_batch(active_batch_tokens, active_seq_len, grad_accum_steps)
+            torch.compiler.cudagraph_mark_step_begin()
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                loss = model(x, y)
+            train_loss.add_(loss.detach())
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        # Muon momentum warmup (skip when AdamW is the matrix optimizer)
+        if args.matrix_optimizer != "adamw":
+            frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0
+            for g in opt_muon.param_groups:
+                g["momentum"] = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+
+        # LR scheduling
+        for opt in optimizers:
+            for g in opt.param_groups:
+                g["lr"] = g["base_lr"] * scale
+            opt.step()
+        zero_grad_all()
+        step += 1
+        approx_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        
+        if args.train_log_every > 0 and step % args.train_log_every == 0:
+            log0(f"step:{step}/{args.iterations} loss:{train_loss.item():.4f} t:{approx_ms:.0f}ms avg:{approx_ms/step:.1f}ms")
+        if args.churn_log_every > 0 and step % args.churn_log_every == 0:
+            log0(f"step:{step} churn:{churn_fn(base_model, args.bitnet_group_size):.4f} zero:{quin_stats(base_model, args.bitnet_group_size)['zero_frac']:.3f}")
+
+        # Wallclock cap sync
+        if stop_after_step is None and max_wallclock_ms is not None and step % 10 == 0:
+            reached_cap = approx_ms >= max_wallclock_ms
+            if distributed:
+                cap_t = torch.tensor(int(reached_cap), device=device)
+                dist.all_reduce(cap_t, op=dist.ReduceOp.MAX)
+                reached_cap = bool(cap_t.item())
+            if reached_cap:
+                stop_after_step = step
+
+    # --- Serialization ---
+    if master_process and not args.skip_training:
+        sd = base_model.state_dict()
+        if base_model.tie_embeddings:
+            sd.pop("lm_head.weight", None)
+
+        log0("serialize: start")
+        t_qsd = time.perf_counter()
+        q_obj, q_stats = q_sd(sd, group_size=args.bitnet_group_size,
+                              scale_quant_bits=args.scale_quant_bits)
+        qsd_time = time.perf_counter() - t_qsd
+        log0(f"serialize: q_sd in {qsd_time:.1f}s "
+             f"(quinary={q_stats['quinary_params']/1e6:.2f}M params/{q_stats['quinary_bytes']/1e6:.2f}MB, "
+             f"fp={q_stats['fp_params']/1e6:.2f}M params/{q_stats['fp_bytes']/1e6:.2f}MB)")
+
+        t_save = time.perf_counter()
+        buf = io.BytesIO()
+        torch.save(q_obj, buf)
+        raw_bytes = len(buf.getvalue())
+        log0(f"serialize: torch.save raw={raw_bytes/1e6:.2f}MB in {time.perf_counter()-t_save:.1f}s")
+
+        t_compress = time.perf_counter()
+        final_blob, per_stream_audit = _serialize_per_stream_v2(q_obj, level=9)
+        compress_time = time.perf_counter() - t_compress
+        artifact_bytes = len(final_blob)
+        log0(per_stream_audit)
+        log0(f"serialize: per_stream_v2 {raw_bytes/1e6:.2f}MB -> {artifact_bytes/1e6:.2f}MB "
+             f"(ratio {artifact_bytes/raw_bytes:.1%}, saved {(raw_bytes-artifact_bytes)/1e6:.2f}MB) "
+             f"in {compress_time:.1f}s")
+
+        with open("final_model.quinary.ptz", "wb") as f:
+            f.write(final_blob)
+
+        code_bytes = len(code.encode("utf-8"))
+
+        total = artifact_bytes + code_bytes
+        log0(f"artifact:{artifact_bytes/1e6:.2f}MB quinary:{q_stats['quinary_params']}({q_stats['quinary_bytes']}B) fp:{q_stats['fp_params']}({q_stats['fp_bytes']}B) code:{code_bytes}")
+        log0(f"budget:{total}/{16000000} ({total/1e6:.2f}/{16.00:.2f}MB) {'FITS' if total <= 16000000 else 'OVER'}")
+
+    # --- All ranks load roundtrip weights and evaluate ---
+    if distributed:
+        dist.barrier()
+
+    with open("final_model.quinary.ptz", "rb") as f:
+        loaded = _load_artifact(f.read())
+    missing, unexpected = base_model.load_state_dict(deq_sd(loaded), strict=False)
+    # The only "missing" key we expect is `lm_head.weight` when tied, since
+    # q_sd drops it from the saved state-dict. Any other missing/unexpected
+    # key is a serialization-roundtrip bug and should fail loudly.
+    expected_missing = {"lm_head.weight"} if base_model.tie_embeddings else set()
+    assert set(missing) <= expected_missing, f"unexpected missing keys after artifact load: {set(missing) - expected_missing}"
+    assert not unexpected, f"unexpected extra keys after artifact load: {unexpected}"
+    torch._dynamo.reset()
+
+    q_val_loss, q_val_bpb, q_tok_count, q_byte_count = eval_val(
+        args, model, rank, world_size, device, grad_accum_steps,
+        val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut)
+    log0(f"final_quinary_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+         f"eval_tokens:{q_tok_count} eval_bytes:{q_byte_count}")
+
+    # --- Score-first chunk-based CTP TTT ---
+    if args.ttt_steps > 0:
+        torch.cuda.synchronize()
+        t_ttt = time.perf_counter()
+        seq_len = args.train_seq_len
+        # TTT_STRIDE: sliding-window step used to slice the val stream into TTT
+        # examples. Hardcoded at 16 (canonical sp16384 quinary submission); a
+        # smaller stride yields more overlapping windows but more compute.
+        stride = 16
+        ttt_chunk_tokens = args.ttt_tokens if args.ttt_tokens > 0 else 32768
+        ttt_epochs = args.ttt_steps
+        batch_seqs = 32
+        total_tokens = val_tokens.numel() - 1
+
+        # Select TTT params (CTP only — quinary weights are frozen)
+        for p in base_model.parameters():
+            p.requires_grad_(False)
+        ttt_params = []
+        for name, p in base_model.named_parameters():
+            if any(pat in name for pat in CTP):
+                p.requires_grad_(True)
+                ttt_params.append(p)
+        n_ttt = sum(p.numel() for p in ttt_params)
+        ttt_opt = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=0.9)
+        for pg in ttt_opt.param_groups:
+            pg["initial_lr"] = pg["lr"]
+        log0(f"ttt: {n_ttt} CTP params, lr={args.ttt_lr}")
+
+        # Assign sliding windows to chunks
+        context_size = seq_len - stride
+        window_starts = [ws for ws in range(0, total_tokens, stride)
+                         if ws + context_size < total_tokens]
+        num_chunks = (total_tokens + ttt_chunk_tokens - 1) // ttt_chunk_tokens
+        chunk_windows = [[] for _ in range(num_chunks)]
+        for ws in window_starts:
+            s = 0 if ws == 0 else context_size
+            ci = min((ws + s) // ttt_chunk_tokens, num_chunks - 1)
+            chunk_windows[ci].append(ws)
+        log0(f"ttt: {n_ttt} CTP params, {num_chunks} chunks, {ttt_epochs} epochs, lr={args.ttt_lr}")
+
+        loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+        token_count = torch.zeros((), device=device, dtype=torch.float64)
+        byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+        # Recompile for TTT (find_unused_parameters needed with frozen params)
+        torch._dynamo.reset()
+        compiled_ttt = torch.compile(base_model, mode=args.compile_mode if args.compile_mode != "default" else None)
+
+        for ci in range(num_chunks):
+            windows = chunk_windows[ci]
+            if not windows:
+                continue
+            chunk_start = ci * ttt_chunk_tokens
+            chunk_end = min((ci + 1) * ttt_chunk_tokens, total_tokens)
+            my_s = len(windows) * rank // world_size
+            my_e = len(windows) * (rank + 1) // world_size
+            my_windows = windows[my_s:my_e]
+
+            # Phase 1: SCORE (no_grad, compiled)
+            base_model.eval()
+            with torch.no_grad():
+                for bi in range(0, len(my_windows), batch_seqs):
+                    batch_ws = my_windows[bi:bi + batch_seqs]
+                    bsz = len(batch_ws)
+                    x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                    y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                    wlens = []
+                    for i, ws in enumerate(batch_ws):
+                        we = min(ws + seq_len, total_tokens)
+                        wlen = we - ws
+                        wlens.append(wlen)
+                        chunk_tok = val_tokens[ws:we + 1].to(dtype=torch.int64, device=device)
+                        x_batch[i, :wlen] = chunk_tok[:-1]
+                        y_batch[i, :wlen] = chunk_tok[1:]
+                    with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                        nll = compiled_ttt(x_batch, y_batch, reduction="none").detach()
+                    for i, ws in enumerate(batch_ws):
+                        wlen = wlens[i]
+                        s = 0 if ws == 0 else context_size
+                        scored_nll = nll[i, s:wlen].to(torch.float64)
+                        loss_sum += scored_nll.sum()
+                        token_count += float(wlen - s)
+                        tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen]
+                        tb = base_bytes_lut[tgt].to(torch.float64)
+                        tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                        byte_count += tb.sum()
+
+            # Phase 2: TRAIN on chunk (score-first: already scored)
+            if ci < num_chunks - 1 and ttt_epochs > 0:
+                base_model.train()
+                chunk_seqs = (chunk_end - chunk_start) // seq_len
+                if chunk_seqs > 0:
+                    cos_mul = 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1)))
+                    for pg in ttt_opt.param_groups:
+                        pg["lr"] = pg.get("initial_lr", pg["lr"]) * cos_mul
+                    my_seq_s = chunk_seqs * rank // world_size
+                    my_seq_e = chunk_seqs * (rank + 1) // world_size
+                    for _ep in range(ttt_epochs):
+                        for bs in range(0, my_seq_e - my_seq_s, batch_seqs):
+                            be = min(bs + batch_seqs, my_seq_e - my_seq_s)
+                            start_tok = chunk_start + (my_seq_s + bs) * seq_len
+                            end_tok = chunk_start + (my_seq_s + be) * seq_len + 1
+                            if end_tok > val_tokens.numel():
+                                continue
+                            local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64)
+                            x = local[:-1].reshape(-1, seq_len)
+                            y = local[1:].reshape(-1, seq_len)
+                            ttt_opt.zero_grad(set_to_none=True)
+                            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                                loss = compiled_ttt(x, y)
+                            loss.backward()
+                            if distributed:
+                                for p in ttt_params:
+                                    if p.grad is not None:
+                                        dist.all_reduce(p.grad, op=dist.ReduceOp.AVG)
+                            torch.nn.utils.clip_grad_norm_(ttt_params, 1.0)
+                            ttt_opt.step()
+
+            if master_process and ci % max(1, num_chunks // 5) == 0:
+                log0(f"ttt chunk:{ci+1}/{num_chunks}")
+
+        if distributed:
+            for t in (loss_sum, token_count, byte_count):
+                dist.all_reduce(t, op=dist.ReduceOp.SUM)
+        ttt_val_loss = (loss_sum / token_count).item()
+        ttt_bpb = (ttt_val_loss / math.log(2.0)) * (token_count.item() / byte_count.item())
+        for p in base_model.parameters():
+            p.requires_grad_(True)
+        torch._dynamo.reset()
+        torch.cuda.synchronize()
+        ttt_time_ms = 1000.0 * (time.perf_counter() - t_ttt)
+        log0(f"ttt_eval val_loss:{ttt_val_loss:.4f} val_bpb:{ttt_bpb:.4f} "
+             f"time:{ttt_time_ms:.0f}ms "
+             f"eval_tokens:{int(token_count.item())} eval_bytes:{int(byte_count.item())}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+if __name__ == "__main__":
+    main()
+====================================================================================================
+Python 3.13.13 | packaged by Anaconda, Inc. | (main, Apr 14 2026, 06:19:41) [GCC 14.3.0]
+PyTorch 2.10.0+cu128
+--- Hyperparameters ---
+activation_type=relu2 adam_eps=1e-08 adam_lr=0.05 adam_wd=0.05 batch_schedule_fraction=0.33 batch_tokens_start=0 beta1=0.9 beta2=0.95 bitnet_group_size=192 churn_log_every=0 compile_mode=default data_path=./data/canonical/datasets/fineweb10B_sp16384 embed_dim=380 embed_lr=0.6 fp_storage=True grad_clip_norm=0.0 head_lr=0.02 iterations=10000 logit_softcap=10.0 matrix_lr=0.035 matrix_optimizer=muon max_wallclock_seconds=599.0 min_lr=0.0 mlp_mult=4 model_dim=576 muon_backend_steps=3 muon_momentum=0.95 muon_momentum_warmup_start=0.85 muon_momentum_warmup_steps=500 muon_wd=0.0 num_heads=6 num_kv_heads=3 num_layers=10 qk_gain_init=5.0 rope_base=5000.0 rope_type=yarn run_id=quinary_seed7 scalar_lr=0.02 scale_quant_bits=5 seed=7 seq_len_start=0 seq_schedule_fraction=0.0 skip_training=False softcap_type=poly tie_embeddings=1 tied_embed_init_std=0.005 tied_embed_lr=0.02 tokenizer_path=./data/canonical/tokenizers/fineweb_16384_bpe.model train_batch_tokens=524288 train_log_every=1000 train_seq_len=1024 ttt_lr=0.005 ttt_steps=3 ttt_tokens=32768 val_batch_size=524288 val_loss_every=0 vocab_size=16384 warmdown_fraction=0.2 warmup_steps=5 yarn_max_len=2048
+params:52828668 L:10 d:576 h:6 kv:3 ws:8 ga:1 s:7
+warmup:1/5
+warmup:2/5
+warmup:3/5
+warmup:4/5
+warmup:5/5
+step:1000/10000 loss:3.8264 t:77231ms avg:77.2ms
+step:2000/10000 loss:3.5207 t:154350ms avg:77.2ms
+step:3000/10000 loss:3.4328 t:231252ms avg:77.1ms
+step:4000/10000 loss:3.2871 t:307962ms avg:77.0ms
+step:5000/10000 loss:3.5032 t:384654ms avg:76.9ms
+step:6000/10000 loss:3.5300 t:461365ms avg:76.9ms
+step:7000/10000 loss:3.4704 t:538006ms avg:76.9ms
+step:7800/10000 val_loss:3.2666 val_bpb:1.1587 train_time:599456ms zero_frac:0.261 eval_tokens:37146624 eval_bytes:151078879
+stopping_early: wallclock_cap train_time:599456ms step:7800/10000
+serialize: start
+serialize: q_sd in 0.1s (quinary=36.50M params/12.36MB, fp=6.90M params/7.11MB)
+serialize: torch.save raw=19.53MB in 0.0s
+per_stream_v2: meta=0.14MB, 63 bulk payloads (lzma=61, lrzip=2; raw=3, base5=2, base5_T=37, bitmask_T=21), qkv_splits=10, total=15.65MB
+serialize: per_stream_v2 19.53MB -> 15.65MB (ratio 80.1%, saved 3.89MB) in 36.2s
+artifact:15.65MB quinary:36495360(12355360B) fp:6896124(7114168B) code:79272
+budget:15724839/16000000 (15.72/16.00MB) FITS
+final_quinary_roundtrip val_loss:3.2762 val_bpb:1.1622 eval_tokens:37146624 eval_bytes:151078879
+ttt: 42364 CTP params, lr=0.005
+ttt: 42364 CTP params, 1134 chunks, 3 epochs, lr=0.005
+ttt chunk:1/1134
+ttt chunk:227/1134
+ttt chunk:453/1134
+ttt chunk:679/1134
+ttt chunk:905/1134
+ttt chunk:1131/1134
+ttt_eval val_loss:3.2076 val_bpb:1.1378 time:214181ms eval_tokens:37146624 eval_bytes:151078879
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/requirements.txt b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/requirements.txt
new file mode 100644
index 0000000000..0c5eedce7b
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/requirements.txt
@@ -0,0 +1,10 @@
+numpy
+tqdm
+torch==2.10
+huggingface-hub
+kernels
+setuptools
+typing-extensions==4.15.0
+datasets
+tiktoken
+sentencepiece
\ No newline at end of file
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/run.sh b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/run.sh
new file mode 100755
index 0000000000..067425a7a4
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/run.sh
@@ -0,0 +1,74 @@
+# Quinary (5-level) Parameter Golf submission.
+#
+# Defaults below match the canonical 53M-param model + per-stream v2
+# layout-aware compression that produced the artifact in this folder:
+#   - sp16384 vocab + tokenizer
+#   - EMBED_DIM=380, MODEL_DIM=576, NUM_LAYERS=10, NUM_HEADS=6, NUM_KV_HEADS=3
+#   - QK_GAIN_INIT=5.0, MATRIX_LR=0.035
+#   - TTT_STEPS=3, TTT_LR=0.005, TTT_TOKENS=32768
+#   - per-stream v2 archive (header byte 0x03):
+#       * splits each bulk tensor into its own compressed payload
+#       * for each quinary tensor, screens 4 layouts {base5, base5_T,
+#         bitmask, bitmask_T} by LZMA9 size, then runs LZMA9 vs lrzip-zpaq
+#         only on the winning layout (bounded heuristic, not exhaustive 4×2)
+#       * for c_qkv.weight, splits rows into Q/K/V sub-payloads independently
+#       * robust to the seed-dependent lrzip cliff (full-blob lrzip can OVER
+#         on ~33% of seeds; per-stream v2 consistently FITS at ~15.64 MB)
+#   - SCALE_QUANT_BITS=5 (per-group scale log-delta quant, saves ~141 KB
+#     at +2.1 mBPB TTT cost; net Pareto-positive)
+#
+# To run with a different seed (e.g., for the 3-seed mean):
+#   SEED=1337 bash run.sh
+
+RUN_ID=${RUN_ID:-quinary_seed42} \
+DATA_PATH=${DATA_PATH:-./data/canonical/datasets/fineweb10B_sp16384} \
+TOKENIZER_PATH=${TOKENIZER_PATH:-./data/canonical/tokenizers/fineweb_16384_bpe.model} \
+VOCAB_SIZE=${VOCAB_SIZE:-16384} \
+BITNET_GROUP_SIZE=${BITNET_GROUP_SIZE:-192} \
+EMBED_DIM=${EMBED_DIM:-380} \
+NUM_LAYERS=${NUM_LAYERS:-10} \
+MODEL_DIM=${MODEL_DIM:-576} \
+NUM_KV_HEADS=${NUM_KV_HEADS:-3} \
+NUM_HEADS=${NUM_HEADS:-6} \
+MLP_MULT=${MLP_MULT:-4} \
+MATRIX_OPTIMIZER=${MATRIX_OPTIMIZER:-muon} \
+ADAM_LR=${ADAM_LR:-0.05} \
+ADAM_WD=${ADAM_WD:-0.05} \
+MUON_BACKEND_STEPS=${MUON_BACKEND_STEPS:-3} \
+MUON_MOMENTUM=${MUON_MOMENTUM:-0.95} \
+MUON_MOMENTUM_WARMUP_START=${MUON_MOMENTUM_WARMUP_START:-0.85} \
+MUON_MOMENTUM_WARMUP_STEPS=${MUON_MOMENTUM_WARMUP_STEPS:-500} \
+MUON_WD=${MUON_WD:-0.0} \
+MATRIX_LR=${MATRIX_LR:-0.035} \
+SCALAR_LR=${SCALAR_LR:-0.02} \
+TIED_EMBED_LR=${TIED_EMBED_LR:-0.02} \
+WARMDOWN_FRACTION=${WARMDOWN_FRACTION:-0.2} \
+LOGIT_SOFTCAP=${LOGIT_SOFTCAP:-10} \
+QK_GAIN_INIT=${QK_GAIN_INIT:-5.0} \
+ROPE_TYPE=${ROPE_TYPE:-yarn} \
+YARN_MAX_LEN=${YARN_MAX_LEN:-2048} \
+ROPE_BASE=${ROPE_BASE:-5000} \
+BATCH_TOKENS_START=${BATCH_TOKENS_START:-0} \
+BATCH_SCHEDULE_FRACTION=${BATCH_SCHEDULE_FRACTION:-0.33} \
+TRAIN_BATCH_TOKENS=${TRAIN_BATCH_TOKENS:-524288} \
+SEQ_LEN_START=${SEQ_LEN_START:-0} \
+SEQ_SCHEDULE_FRACTION=${SEQ_SCHEDULE_FRACTION:-0.0} \
+TRAIN_SEQ_LEN=${TRAIN_SEQ_LEN:-1024} \
+ITERATIONS=${ITERATIONS:-10000} \
+WARMUP_STEPS=${WARMUP_STEPS:-5} \
+MAX_WALLCLOCK_SECONDS=${MAX_WALLCLOCK_SECONDS:-599} \
+VAL_LOSS_EVERY=${VAL_LOSS_EVERY:-0} \
+TRAIN_LOG_EVERY=${TRAIN_LOG_EVERY:-1000} \
+CHURN_LOG_EVERY=${CHURN_LOG_EVERY:-0} \
+VAL_MAX_TOKENS=${VAL_MAX_TOKENS:-0} \
+TIE_EMBEDDINGS=${TIE_EMBEDDINGS:-1} \
+HEAD_LR=${HEAD_LR:-0.02} \
+ACTIVATION=${ACTIVATION:-relu2} \
+SOFTCAP_TYPE=${SOFTCAP_TYPE:-poly} \
+TTT_STEPS=${TTT_STEPS:-3} \
+TTT_LR=${TTT_LR:-0.005} \
+TTT_TOKENS=${TTT_TOKENS:-32768} \
+SCALE_QUANT_BITS=${SCALE_QUANT_BITS:-5} \
+SEED=${SEED:-42} \
+COMPILE_MODE=${COMPILE_MODE:-default} \
+OMP_NUM_THREADS=${OMP_NUM_THREADS:-1} torchrun --standalone --nproc_per_node=8 train_gpt.py
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/setup.sh b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/setup.sh
new file mode 100755
index 0000000000..27920ae3c7
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/setup.sh
@@ -0,0 +1,130 @@
+#!/bin/bash
+# -------------------------------------------------------------------------------
+# Parameter Golf -- Quinary submission environment setup
+# Run from the submission/ directory on a fresh 8xH100 pod.
+#
+# After this finishes:
+#   - lrzip is installed (used by per-stream compression)
+#   - Python deps from requirements.txt are installed
+#   - FlashAttention-3 wheel is installed (Hopper-only)
+#   - sp16384 tokenizer + tokenized FineWeb shards are at ./data/
+#
+# Total time on a fresh pod: ~10-25 min (mostly the ~23 GB HF download).
+# -------------------------------------------------------------------------------
+
+set -e
+
+echo "=============================================="
+echo " Parameter Golf -- Quinary submission setup"
+echo "=============================================="
+
+# --------------------------------------------------------------------
+# 1. System packages (lrzip; needed by per-stream artifact compression)
+# --------------------------------------------------------------------
+echo ""
+echo "[1/4] System packages (lrzip)..."
+
+if command -v lrzip >/dev/null 2>&1; then
+    echo "    lrzip already installed -- skipping."
+else
+    apt-get update -qq
+    apt-get install -y -qq lrzip
+    echo "    Installed."
+fi
+
+# --------------------------------------------------------------------
+# 2. Python requirements
+# --------------------------------------------------------------------
+echo ""
+echo "[2/4] Python requirements..."
+
+if python3 -c "import torch, sentencepiece, numpy, huggingface_hub" 2>/dev/null; then
+    echo "    Core packages already installed -- skipping."
+else
+    pip install --upgrade pip -q
+    pip install -r requirements.txt -q
+    echo "    Installed."
+fi
+
+# --------------------------------------------------------------------
+# 3. FlashAttention-3 (Hopper-specific wheel)
+# --------------------------------------------------------------------
+echo ""
+echo "[3/4] FlashAttention-3..."
+
+if python3 -c "import flash_attn_interface" 2>/dev/null; then
+    echo "    Already installed -- skipping."
+else
+    pip install --no-cache-dir \
+        "https://download.pytorch.org/whl/cu128/flash_attn_3-3.0.0-cp39-abi3-manylinux_2_28_x86_64.whl"
+    echo "    Installed."
+fi
+
+# --------------------------------------------------------------------
+# 4. FineWeb dataset + sp16384 tokenizer (canonical/ subset only)
+# --------------------------------------------------------------------
+echo ""
+echo "[4/4] FineWeb sp16384 dataset + tokenizer..."
+
+if ls ./data/canonical/datasets/fineweb10B_sp16384/fineweb_val_*.bin 1>/dev/null 2>&1; then
+    echo "    Already present at ./data/canonical/ -- skipping."
+else
+    echo "    Downloading from deniskurlov/parameter-golf-fineweb-sp16384 (canonical/ only, ~23 GB)..."
+    hf download deniskurlov/parameter-golf-fineweb-sp16384 \
+        --include "canonical/**" \
+        --local-dir ./data \
+        --repo-type dataset
+    echo "    Downloaded."
+fi
+
+# --------------------------------------------------------------------
+# Verification
+# --------------------------------------------------------------------
+echo ""
+echo "=============================================="
+echo " Verification"
+echo "=============================================="
+
+python3 - << 'EOF'
+import sys, glob
+import torch, numpy as np
+
+print(f"Python       : {sys.version.split()[0]}")
+print(f"PyTorch      : {torch.__version__}")
+print(f"CUDA         : {torch.cuda.is_available()}")
+print(f"GPUs         : {torch.cuda.device_count()}")
+if torch.cuda.is_available():
+    for i in range(torch.cuda.device_count()):
+        props = torch.cuda.get_device_properties(i)
+        print(f"  GPU {i}      : {props.name} ({props.total_memory // 1024**3} GB)")
+
+try:
+    import flash_attn_interface  # noqa
+    print("FlashAttn3   : installed")
+except ImportError:
+    print("FlashAttn3   : NOT found (required for training)")
+
+import sentencepiece as spm
+sp_path = "./data/canonical/tokenizers/fineweb_16384_bpe.model"
+sp = spm.SentencePieceProcessor(model_file=sp_path)
+print(f"Tokenizer    : {sp.vocab_size()}-vocab SentencePiece BPE @ {sp_path}")
+
+train = sorted(glob.glob("./data/canonical/datasets/fineweb10B_sp16384/fineweb_train_*.bin"))
+val   = sorted(glob.glob("./data/canonical/datasets/fineweb10B_sp16384/fineweb_val_*.bin"))
+total_val = sum(int(np.fromfile(f, dtype="<i4", count=3)[2]) for f in val) if val else 0
+print(f"Dataset      : {len(train)} train shards, {len(val)} val shards, {total_val:,} val tokens")
+
+import shutil
+print(f"lrzip binary : {shutil.which('lrzip') or 'NOT FOUND (required for per-stream compression)'}")
+EOF
+
+echo ""
+echo "=============================================="
+echo " Done. To train + evaluate:"
+echo ""
+echo "   bash run.sh"
+echo ""
+echo " Or with overrides (e.g., a different seed):"
+echo ""
+echo "   SEED=1337 bash run.sh"
+echo "=============================================="
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/submission.json b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/submission.json
new file mode 100644
index 0000000000..8473438a1d
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/submission.json
@@ -0,0 +1,34 @@
+{
+  "author": "Denis Kurlov",
+  "github_id": "deniskurlov",
+  "name": "52.8M Quinary U-Net + Per-Stream-v2 Layout-Aware Compression + Score-First TTT",
+  "blurb": "Direct quinary fork of the 2026-03-24 ternary record submission by Ciprian-Florin Ifrim (PR #640, 1.1570 sliding BPB). Hypothesis: quinary {-2,-1,0,+1,+2} (5 levels per parameter; raw 8/3 ≈ 2.667 bits/param via base-5 packing 3 quins per byte, log₂5 ≈ 2.322 bits/param entropy floor) buys per-parameter expressivity that outweighs the ~1 bit/param cost vs ternary's 3 levels. Architecture inherited unchanged: 10L (5 enc + 5 dec) U-Net, 4x relu² MLP, factored tied embedding, polynomial-5 softcap, YaRN 2048, Muon, fused QKV, FP8 QAT for non-quantized linears, FlashAttention-3. Adapted: 768d->576d, GQA 8:4->6:3, embed bottleneck 254->380, group_size 128->192, tokenizer SP8192->SP16384, single-blob LZMA->layout-aware per-stream v2 archive (header 0x03; per-quinary-tensor LZMA-screened layout selection over {base5, base5_T, bitmask, bitmask_T} then LZMA-vs-lrzip on the winner — bounded heuristic, not exhaustive 4×2; c_qkv split into Q/K/V sub-payloads; structurally based on parameter-golf PR #1855), and stride-16 sliding eval -> score-first TTT (3 epochs, lr=0.005, adapting only the 42,364 fp16 calibration parameters — per-layer scales, residual mix, Q-gain, skip weights, vocab bias). 52.8M params in 15.72MB max-seed total. Trained 7,800 steps in ~599s on 8xH100 SXM. 3-seed validation (42, 1337, 7): TTT BPB 1.1384 ± 0.0009 std (-22 mBPB from quinary architectural change vs ternary RT 1.1842, -24 mBPB additional from TTT), all FITS with margin ~275 KB under the 16 MB cap. BPB denominator audited end-to-end: verify_bpb.py exact-eval-slice lut_bytes=151,078,879 matches the runtime eval_bytes:151,078,879 printed by train_gpt.py for every seed (delta=+0). Per-stream v2 also solves the seed-dependent lrzip cliff that forced earlier full-blob lrzip artifacts to OVER on ~33% of seeds (with the v2 archive seed=7 is now actually the best-fitting seed at 1.1378 TTT BPB).",
+  "date": "2026-05-01T22:00:00Z",
+  "val_loss": 3.2093,
+  "val_bpb": 1.1384,
+  "bytes_total": 15724839,
+  "bytes_total_note": "Max across the 3 verified seeds (seed=7); per-seed values in seed_results. Range across seeds: 15,714,938 - 15,724,839. Margin under 16 MB cap = 275,161 bytes.",
+  "bytes_code": 79272,
+  "seed_results": {
+    "42":   { "val_loss": 3.2083, "val_bpb": 1.1381, "val_bpb_roundtrip": 1.1626, "bytes_total": 15714938, "steps_reached": 7800, "eval_tokens": 37146624, "eval_bytes": 151078879, "verified": true },
+    "1337": { "val_loss": 3.2120, "val_bpb": 1.1394, "val_bpb_roundtrip": 1.1633, "bytes_total": 15721124, "steps_reached": 7800, "eval_tokens": 37146624, "eval_bytes": 151078879, "verified": true },
+    "7":    { "val_loss": 3.2076, "val_bpb": 1.1378, "val_bpb_roundtrip": 1.1622, "bytes_total": 15724839, "steps_reached": 7800, "eval_tokens": 37146624, "eval_bytes": 151078879, "verified": true }
+  },
+  "seed_mean_val_bpb": 1.1384,
+  "seed_std_val_bpb": 0.00085,
+  "seed_stderr_val_bpb": 0.00049,
+  "seed_mean_val_bpb_roundtrip": 1.1627,
+  "seed_std_val_bpb_roundtrip": 0.00056,
+  "seed_stderr_val_bpb_roundtrip": 0.00032,
+  "n_params": 52828668,
+  "n_quinary_params": 36495360,
+  "n_fp_params": 6896124,
+  "training_seconds": 599,
+  "eval_seconds": 300,
+  "eval_seconds_breakdown": {
+    "load_artifact_and_decompress": 5,
+    "roundtrip_eval": 80,
+    "ttt_eval": 215
+  },
+  "track": "track_non_record_16mb"
+}
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/train_gpt.py b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/train_gpt.py
new file mode 100644
index 0000000000..446bde7cd8
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/train_gpt.py
@@ -0,0 +1,1652 @@
+"Quinary training script for OpenAI's Parameter Golf Challenge. Based on ternary submission by Ciprian-Florin Ifrim (24 March 2026). Quinary: {-2,-1,0,+1,+2} with base-5 packing (3 quins/byte = 2.667 bpw vs ternary 1.6 bpw)."
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import tempfile
+import time
+import lzma
+from pathlib import Path
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+from flash_attn_interface import flash_attn_func
+
+def _e(k, d, t=str):
+    v = os.environ.get(k, str(d))
+    if t == bool: return bool(int(v))
+    return t(v)
+
+class Hyperparameters:
+    # Defaults below match the canonical SP16384 quinary submission config —
+    # i.e. exactly what `run.sh` passes through to torchrun. A bare
+    # `torchrun --standalone --nproc_per_node=8 train_gpt.py` (no env vars)
+    # therefore reproduces the submission. `run.sh` is still the
+    # documentation-of-record for the canonical config; these defaults are
+    # mirrored from it so the two never silently disagree.
+    data_path = _e("DATA_PATH", "./data/canonical/datasets/fineweb10B_sp16384")
+    # [0-9] prefix matches only NNNNNN-suffixed shard files, ignoring any
+    # sibling parallel-array files that might share the prefix.
+    train_files = os.path.join(data_path, "fineweb_train_[0-9]*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_[0-9]*.bin")
+    tokenizer_path = _e("TOKENIZER_PATH", "./data/canonical/tokenizers/fineweb_16384_bpe.model")
+    run_id = os.environ.get("RUN_ID", f"run_{int(time.time())}")
+    seed = _e("SEED", 42, int)
+    compile_mode = _e("COMPILE_MODE", "default")
+    val_batch_size = _e("VAL_BATCH_SIZE", 524288, int)
+    val_loss_every = _e("VAL_LOSS_EVERY", 0, int)  # 0 = no in-training val
+    train_log_every = _e("TRAIN_LOG_EVERY", 1000, int)
+    iterations = _e("ITERATIONS", 10000, int)
+    warmdown_fraction = _e("WARMDOWN_FRACTION", 0.2, float)
+    min_lr = _e("MIN_LR", 0.0, float)  # floor on the LR multiplier (fraction of base LR)
+    warmup_steps = _e("WARMUP_STEPS", 5, int)
+    train_batch_tokens = _e("TRAIN_BATCH_TOKENS", 524288, int)
+    train_seq_len = _e("TRAIN_SEQ_LEN", 1024, int)
+    max_wallclock_seconds = _e("MAX_WALLCLOCK_SECONDS", 599.0, float)
+    vocab_size = _e("VOCAB_SIZE", 16384, int)
+    num_layers = _e("NUM_LAYERS", 10, int)
+    num_kv_heads = _e("NUM_KV_HEADS", 3, int)
+    model_dim = _e("MODEL_DIM", 576, int)
+    num_heads = _e("NUM_HEADS", 6, int)
+    mlp_mult = _e("MLP_MULT", 4, int)
+    tie_embeddings = _e("TIE_EMBEDDINGS", 1, int)
+    rope_base = _e("ROPE_BASE", 5000.0, float)
+    rope_type = _e("ROPE_TYPE", "yarn")
+    yarn_max_len = _e("YARN_MAX_LEN", 2048, int)
+    logit_softcap = _e("LOGIT_SOFTCAP", 10.0, float)
+    softcap_type = _e("SOFTCAP_TYPE", "poly")
+    tied_embed_init_std = _e("TIED_EMBED_INIT_STD", 0.005, float)
+    qk_gain_init = _e("QK_GAIN_INIT", 5.0, float)
+    activation_type = _e("ACTIVATION", "relu2")
+    embed_dim = _e("EMBED_DIM", 380, int)
+    embed_lr = _e("EMBED_LR", 0.6, float)
+    head_lr = _e("HEAD_LR", 0.02, float)
+    adam_lr = _e("ADAM_LR", 0.05, float)
+    adam_wd = _e("ADAM_WD", 0.05, float)
+    tied_embed_lr = _e("TIED_EMBED_LR", 0.02, float)
+    seq_len_start = _e("SEQ_LEN_START", 0, int)
+    seq_schedule_fraction = _e("SEQ_SCHEDULE_FRACTION", 0.0, float)
+    batch_tokens_start = _e("BATCH_TOKENS_START", 0, int)
+    batch_schedule_fraction = _e("BATCH_SCHEDULE_FRACTION", 0.33, float)
+    churn_log_every = _e("CHURN_LOG_EVERY", 0, int)
+    matrix_lr = _e("MATRIX_LR", 0.035, float)
+    scalar_lr = _e("SCALAR_LR", 0.02, float)
+    muon_momentum = _e("MUON_MOMENTUM", 0.95, float)
+    muon_backend_steps = _e("MUON_BACKEND_STEPS", 3, int)
+    muon_wd = _e("MUON_WD", 0.0, float)
+    matrix_optimizer = _e("MATRIX_OPTIMIZER", "muon")
+    muon_momentum_warmup_start = _e("MUON_MOMENTUM_WARMUP_START", 0.85, float)
+    muon_momentum_warmup_steps = _e("MUON_MOMENTUM_WARMUP_STEPS", 500, int)
+    beta1 = _e("BETA1", 0.9, float)
+    beta2 = _e("BETA2", 0.95, float)
+    adam_eps = _e("ADAM_EPS", 1e-8, float)
+    grad_clip_norm = _e("GRAD_CLIP_NORM", 0.0, float)
+    bitnet_group_size = _e("BITNET_GROUP_SIZE", 192, int)
+    ttt_steps = _e("TTT_STEPS", 3, int)
+    ttt_lr = _e("TTT_LR", 0.005, float)
+    ttt_tokens = _e("TTT_TOKENS", 32768, int)
+    skip_training = _e("SKIP_TRAINING", 0, bool)
+    scale_quant_bits = _e("SCALE_QUANT_BITS", 5, int)  # 5-bit log-delta scale quantization
+    fp_storage = True  # FP8 storage for non-quinary (small) tensors
+
+# Calibration / fp16-stored scalar+vector params adapted by score-first TTT.
+# `scale_correction` was previously listed here but its gradient is blocked by
+# the STE detach in QuinaryLinear.forward, so including it as a TTT target was
+# a no-op. Excluded from the CTP tuple so the TTT optimizer reflects what
+# actually receives gradients (~42k params, not 232k). The parameter still
+# exists in the state-dict and is stored fp16 in the artifact (via the ndim<2
+# bucket); it is just not selected for TTT adaptation.
+CTP = ("attn_scale","mlp_scale","resid_mix","q_gain","skip_weights","vocab_bias")
+
+def pack_quinary(q: Tensor):
+    f = (q.reshape(-1).to(torch.int8) + 2).numpy()  # {-2..2} -> {0..4}
+    n = len(f)
+    p = (3 - n % 3) % 3
+    if p: f = np.concatenate([f, np.zeros(p, dtype=np.int8)])
+    g = f.reshape(-1, 3).astype(np.uint8)
+    return (g[:,0] + g[:,1]*5 + g[:,2]*25).tobytes(), n  # max = 4+20+100 = 124
+
+def unpack_quinary(data: bytes, n: int) -> Tensor:
+    v = np.frombuffer(data, dtype=np.uint8).astype(np.int16)
+    t = np.zeros((len(v), 3), dtype=np.int8)
+    for i in range(3): t[:,i] = v % 5; v //= 5
+    return torch.from_numpy(t.reshape(-1)[:n].astype(np.int8) - 2)
+
+
+def pack_quinary_bitmask(q) -> tuple[bytes, int, int]:
+    """Pack quinary symbols ∈ {-2..2} as three concatenated bit-planes.
+
+    Layout (big-endian within bytes via numpy.packbits):
+      [zero_mask:    ceil(n_total / 8) bytes — 1 bit/symbol, 1=zero]
+      [sign_bits:    ceil(n_nonzero / 8) bytes — 1 bit/nonzero, 1=negative]
+      [mag2_bits:    ceil(n_nonzero / 8) bytes — 1 bit/nonzero, 1=|symbol|==2]
+
+    Returns: (concat_bytes, n_total, n_nonzero).
+    Each plane has homogeneous bit statistics so the downstream compressor
+    can model them independently rather than fighting a multimodal mixture.
+    """
+    if isinstance(q, torch.Tensor):
+        a = q.reshape(-1).to(torch.int8).numpy()
+    else:
+        a = np.asarray(q, dtype=np.int8).reshape(-1)
+    n_total = a.size
+    nz_mask = (a != 0)                            # bool, len n_total
+    n_nonzero = int(nz_mask.sum())
+    nz_vals = a[nz_mask]                          # int8, len n_nonzero
+    sign_bits = (nz_vals < 0).astype(np.uint8)    # 1 = negative
+    mag2_bits = (np.abs(nz_vals) == 2).astype(np.uint8)
+    zero_packed = np.packbits((~nz_mask).astype(np.uint8))  # 1 bit means "this symbol is zero"
+    sign_packed = np.packbits(sign_bits)
+    mag2_packed = np.packbits(mag2_bits)
+    return (zero_packed.tobytes() + sign_packed.tobytes() + mag2_packed.tobytes(),
+            n_total, n_nonzero)
+
+
+def unpack_quinary_bitmask(data: bytes, n_total: int, n_nonzero: int) -> Tensor:
+    """Inverse of pack_quinary_bitmask. Returns int8 tensor of length n_total."""
+    z_len = (n_total + 7) // 8
+    s_len = (n_nonzero + 7) // 8
+    m_len = (n_nonzero + 7) // 8
+    expected = z_len + s_len + m_len
+    if len(data) != expected:
+        raise ValueError(f"bitmask data size {len(data)} != expected {expected} "
+                         f"(n_total={n_total}, n_nonzero={n_nonzero})")
+    z_bytes = np.frombuffer(data[:z_len], dtype=np.uint8)
+    s_bytes = np.frombuffer(data[z_len:z_len + s_len], dtype=np.uint8)
+    m_bytes = np.frombuffer(data[z_len + s_len:], dtype=np.uint8)
+    is_zero = np.unpackbits(z_bytes)[:n_total].astype(bool)
+    sign = np.unpackbits(s_bytes)[:n_nonzero].astype(bool)
+    mag2 = np.unpackbits(m_bytes)[:n_nonzero].astype(bool)
+    nz_vals = np.where(mag2, 2, 1).astype(np.int8)
+    nz_vals = np.where(sign, -nz_vals, nz_vals)
+    out = np.zeros(n_total, dtype=np.int8)
+    out[~is_zero] = nz_vals
+    return torch.from_numpy(out)
+
+
+# Artifact archive: layout-aware per-stream v2 (header byte 0x03).
+#
+#   - For each quinary tensor: screen the 4 layouts {base5, base5_T,
+#     bitmask, bitmask_T} by LZMA9-compressed size, then run LZMA9 vs
+#     lrzip-zpaq -L9 only on the winning layout. Bounded heuristic
+#     with an LZMA floor — *not* an exhaustive 4×2 search.
+#   - For c_qkv.weight: split rows into Q / K / V sub-payloads each chosen
+#     independently (Q, K, V have different trained distributions).
+#   - For other bulk fields (FP8 embeddings, large fp16 tensors): torch.save
+#     the value and compress with min(lzma, lrzip).
+#   - Robust to the seed-dependent lrzip cliff observed on full-blob
+#     compression: even if lrzip ZPAQ underperforms on one tensor's bytes,
+#     lzma takes over for that tensor specifically.
+_COMPRESSOR_LZMA = 0
+_COMPRESSOR_LRZIP_ZPAQ = 1
+_COMPRESSOR_PER_STREAM_V2 = 3
+
+# Threshold for treating a value as "bulk" (compressed independently).
+_PER_STREAM_BULK_BYTES = 64 * 1024
+
+# v2 layout IDs — what kind of body is stored in a bulk payload.
+_LAYOUT_RAW         = 0  # opaque bytes (torch.save output for non-quinary fields)
+_LAYOUT_Q_BASE5     = 1  # base-5 packed quinary symbols, canonical row-major order
+_LAYOUT_Q_BASE5_T   = 2  # base-5 packed quinary symbols, transposed (column-major)
+_LAYOUT_Q_BITMASK   = 3  # bitmask packed (zero|sign|mag2), canonical
+_LAYOUT_Q_BITMASK_T = 4  # bitmask packed, transposed
+_LAYOUT_NAMES = {
+    _LAYOUT_RAW: "raw",
+    _LAYOUT_Q_BASE5: "base5",
+    _LAYOUT_Q_BASE5_T: "base5_T",
+    _LAYOUT_Q_BITMASK: "bitmask",
+    _LAYOUT_Q_BITMASK_T: "bitmask_T",
+}
+
+# v2 sentinel for metadata refs (distinct string from v1 to avoid version confusion).
+_BULK_SENTINEL_V2 = "__BULK_REF_V2__"
+
+
+def _lrzip_compress_bytes(data: bytes, level: int = 9) -> bytes:
+    """Compress raw bytes via `lrzip -z` (ZPAQ). Returns body only (no header)."""
+    with tempfile.NamedTemporaryFile(delete=False) as f:
+        f.write(data); in_path = f.name
+    out_path = in_path + ".lrz"
+    try:
+        try:
+            subprocess.run(["lrzip", "-z", "-L", str(level), "-q", "-f", in_path],
+                           check=True, capture_output=True)
+        except FileNotFoundError as e:
+            raise RuntimeError("lrzip binary not found; run `apt-get install lrzip`") from e
+        with open(out_path, "rb") as f:
+            return f.read()
+    finally:
+        for p in (in_path, out_path):
+            try: os.unlink(p)
+            except FileNotFoundError: pass
+
+
+def _lrzip_decompress_bytes(body: bytes) -> bytes:
+    """Decompress an lrzip ZPAQ body (no header)."""
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".lrz") as f:
+        f.write(body); in_path = f.name
+    out_path = in_path[:-4]
+    try:
+        try:
+            subprocess.run(["lrzip", "-d", "-q", "-f", in_path],
+                           check=True, capture_output=True)
+        except FileNotFoundError as e:
+            raise RuntimeError("lrzip binary not found; required to load this artifact") from e
+        with open(out_path, "rb") as f:
+            return f.read()
+    finally:
+        for p in (in_path, out_path):
+            try: os.unlink(p)
+            except FileNotFoundError: pass
+
+
+def _pick_best_compressor(data: bytes, level: int = 9, has_lrzip: bool = True) -> tuple[int, bytes]:
+    """Try lzma + (optionally) lrzip; return (method_id, body) for the smaller.
+
+    This is the per-stream robustness: even if one compressor cliffs on a
+    given byte distribution (the seed=7 issue), the other usually doesn't.
+    Any failure (missing binary, runtime error, broken lrzip backend) on
+    lrzip is silently skipped — lzma is the always-available floor.
+    """
+    candidates: list[tuple[int, bytes]] = [(_COMPRESSOR_LZMA, lzma.compress(data, preset=level))]
+    if has_lrzip:
+        try:
+            candidates.append((_COMPRESSOR_LRZIP_ZPAQ, _lrzip_compress_bytes(data, level)))
+        except (RuntimeError, subprocess.CalledProcessError, OSError):
+            pass
+    return min(candidates, key=lambda c: len(c[1]))
+
+
+def _is_bulk(value) -> bool:
+    """Decide whether a state_dict field should be peeled off for per-stream compression."""
+    if isinstance(value, (bytes, bytearray)):
+        return len(value) >= _PER_STREAM_BULK_BYTES
+    if isinstance(value, torch.Tensor):
+        return value.numel() * value.element_size() >= _PER_STREAM_BULK_BYTES
+    return False
+
+
+def _load_artifact(blob: bytes) -> dict:
+    """Load a per-stream v2 archive (header byte 0x03) into a state_dict."""
+    return _deserialize_per_stream_v2(blob)
+
+
+# ---------------------------------------------------------------------------
+# v2 archive: layout-aware per-stream compression
+# ---------------------------------------------------------------------------
+
+def _gen_quinary_layout_candidates(symbols_2d: np.ndarray) -> list[tuple[int, bytes, dict]]:
+    """For a 2D int8 symbol matrix in {-2..2}, return a list of
+    (layout_id, body_bytes, layout_meta) tuples — every supported layout.
+
+    layout_meta carries the per-layout fields needed at deserialize time
+    (n_quins, n_nonzero, etc.).
+    """
+    rows, cols = symbols_2d.shape
+    flat = symbols_2d.reshape(-1)
+    transposed = symbols_2d.T.reshape(-1).copy()  # contiguous transpose
+
+    # base-5 canonical
+    body_b5, n_b5 = pack_quinary(torch.from_numpy(flat))
+    # base-5 transposed
+    body_b5_t, n_b5_t = pack_quinary(torch.from_numpy(transposed))
+    # bitmask canonical
+    body_bm, nt_bm, nz_bm = pack_quinary_bitmask(torch.from_numpy(flat))
+    # bitmask transposed
+    body_bm_t, nt_bm_t, nz_bm_t = pack_quinary_bitmask(torch.from_numpy(transposed))
+
+    return [
+        (_LAYOUT_Q_BASE5,     body_b5,   {"n_quins": n_b5}),
+        (_LAYOUT_Q_BASE5_T,   body_b5_t, {"n_quins": n_b5_t}),
+        (_LAYOUT_Q_BITMASK,   body_bm,   {"n_total": nt_bm, "n_nonzero": nz_bm}),
+        (_LAYOUT_Q_BITMASK_T, body_bm_t, {"n_total": nt_bm_t, "n_nonzero": nz_bm_t}),
+    ]
+
+
+def _layout_to_canonical_bytes(layout: int, body: bytes, rows: int, cols: int,
+                               layout_meta: dict) -> bytes:
+    """Inverse of _gen_quinary_layout_candidates: take a body in one of the
+    layout encodings and return the canonical base-5-packed bytes that
+    `deq_sd` expects in entry["packed"]."""
+    if layout == _LAYOUT_Q_BASE5:
+        # Already canonical.
+        return body
+    if layout == _LAYOUT_Q_BASE5_T:
+        symbols_t = unpack_quinary(body, layout_meta["n_quins"]).numpy()
+        symbols = symbols_t.reshape(cols, rows).T.reshape(-1).copy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    if layout == _LAYOUT_Q_BITMASK:
+        symbols = unpack_quinary_bitmask(body, layout_meta["n_total"], layout_meta["n_nonzero"]).numpy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    if layout == _LAYOUT_Q_BITMASK_T:
+        symbols_t = unpack_quinary_bitmask(body, layout_meta["n_total"], layout_meta["n_nonzero"]).numpy()
+        symbols = symbols_t.reshape(cols, rows).T.reshape(-1).copy()
+        out, _ = pack_quinary(torch.from_numpy(symbols))
+        return out
+    raise ValueError(f"unknown layout id {layout}")
+
+
+def _qkv_split_sizes(name: str, rows: int, cols: int) -> tuple[int, int] | None:
+    """For a c_qkv weight, derive (q_rows, kv_rows) row-counts.
+
+    Standard transformer convention: q_size = num_heads * head_dim = model_dim
+    (the square attention assumption). So q_rows == cols. The remaining rows
+    are split equally between K and V: kv_rows = (rows - cols) // 2.
+
+    Returns None if name doesn't look like c_qkv or the ratio doesn't decompose
+    cleanly (in which case we don't split, just compress as one tensor).
+    """
+    if not name.endswith("c_qkv.weight"):
+        return None
+    if rows <= cols:
+        return None  # not a GQA shape; or single-head case where Q==K==V
+    extra = rows - cols  # 2 * kv_rows
+    if extra % 2 != 0:
+        return None
+    kv_rows = extra // 2
+    q_rows = cols
+    if q_rows + 2 * kv_rows != rows:
+        return None
+    return q_rows, kv_rows
+
+
+def _serialize_per_stream_v2(state_dict: dict, level: int = 9) -> tuple[bytes, str]:
+    """v2 archive: layout-aware per-stream compression.
+
+    For each quinary entry, generate up to 4 layout candidates
+    (base5, base5_T, bitmask, bitmask_T), screen them by LZMA9-compressed
+    size, then compress the winning layout with min(LZMA9, lrzip-zpaq).
+    For c_qkv weights, the row-block is split into Q/K/V sub-payloads
+    handled independently. All other bulk fields use the v1 raw-bytes path.
+
+    This is a bounded-cost heuristic with an LZMA floor — *not* an
+    exhaustive 4×2 search. It can in principle miss a (layout, compressor)
+    pair where the LZMA-screen-loser would have won under lrzip; in
+    practice this is rare on this stack and the LZMA floor caps the
+    worst case at the canonical base5+LZMA encoding.
+
+    Returns (archive_bytes, audit_string) so the caller can route the
+    audit line through the run's logger.
+    """
+    from shutil import which
+    has_lrzip = which("lrzip") is not None
+
+    bulk_bodies: list[tuple[int, bytes]] = []  # (compressor_method, compressed_body)
+    metadata: dict = {}
+    audit_lines: list[str] = []
+    layout_counts = {n: 0 for n in _LAYOUT_NAMES.values()}
+    method_counts = {"lzma": 0, "lrzip": 0}
+    qkv_split_count = 0
+
+    def _store(layout: int, body: bytes) -> tuple[int, int, int]:
+        """Compress and store; return (payload_idx, method_id, compressed_size)."""
+        method, comp = _pick_best_compressor(body, level, has_lrzip)
+        bulk_bodies.append((method, comp))
+        method_counts["lzma" if method == _COMPRESSOR_LZMA else "lrzip"] += 1
+        layout_counts[_LAYOUT_NAMES[layout]] += 1
+        return len(bulk_bodies) - 1, method, len(comp)
+
+    def _best_quinary_layout(symbols_2d: np.ndarray) -> tuple[int, int, int, dict, int]:
+        """Generate all layout candidates, screen with lzma to pick best layout,
+        then compress winner with min(lzma, lrzip) for the final body.
+
+        Cheap-screen + refine cuts serialize compute from O(layouts*compressors)
+        to O(layouts) lzma + 1 lrzip, keeping serialize time bounded even when
+        lrzip is slow on bad streams.
+
+        Returns (chosen_layout, payload_idx, compressed_size, layout_meta, n_candidates_evaluated).
+        """
+        cands = _gen_quinary_layout_candidates(symbols_2d)
+        best_layout, best_body, best_meta = None, None, None
+        best_lzma_size = None
+        for layout, body, meta in cands:
+            lzma_size = len(lzma.compress(body, preset=level))
+            if best_lzma_size is None or lzma_size < best_lzma_size:
+                best_lzma_size = lzma_size
+                best_layout, best_body, best_meta = layout, body, meta
+        # Now compress the winning layout with min(lzma, lrzip) for the actual stored body.
+        comp_method, comp_body = _pick_best_compressor(best_body, level, has_lrzip)
+        bulk_bodies.append((comp_method, comp_body))
+        method_counts["lzma" if comp_method == _COMPRESSOR_LZMA else "lrzip"] += 1
+        layout_counts[_LAYOUT_NAMES[best_layout]] += 1
+        return best_layout, len(bulk_bodies) - 1, len(comp_body), best_meta, len(cands)
+
+    for name, entry in state_dict.items():
+        if not isinstance(entry, dict):
+            metadata[name] = entry
+            continue
+        new_entry = dict(entry)  # shallow copy
+
+        if entry.get("type") == "quinary" and "packed" in entry:
+            # Reconstruct full symbol matrix from canonical packed bytes.
+            rows, cols = entry["shape"][0], entry["padded_cols"]
+            symbols_full = unpack_quinary(entry["packed"], entry["n_quins"]).numpy()
+            symbols_2d = symbols_full[:rows * cols].reshape(rows, cols)
+
+            split = _qkv_split_sizes(name, rows, cols)
+            if split is not None:
+                q_rows, kv_rows = split
+                qkv_split_count += 1
+                refs = []
+                for sub_name, sub_2d in (
+                    ("Q", symbols_2d[:q_rows]),
+                    ("K", symbols_2d[q_rows:q_rows + kv_rows]),
+                    ("V", symbols_2d[q_rows + kv_rows:]),
+                ):
+                    layout, idx, sz, meta, _ = _best_quinary_layout(sub_2d.copy())
+                    sub_rows, sub_cols = sub_2d.shape
+                    refs.append({
+                        "_v2": _BULK_SENTINEL_V2, "idx": idx, "layout": layout,
+                        "rows": sub_rows, "cols": sub_cols, "meta": meta,
+                    })
+                new_entry["packed"] = ("__QKV_SPLIT__", refs)
+                audit_lines.append(f"  {name}: qkv_split layouts={[_LAYOUT_NAMES[r['layout']] for r in refs]}")
+            else:
+                layout, idx, sz, meta, _ = _best_quinary_layout(symbols_2d.copy())
+                new_entry["packed"] = {
+                    "_v2": _BULK_SENTINEL_V2, "idx": idx, "layout": layout,
+                    "rows": rows, "cols": cols, "meta": meta,
+                }
+                audit_lines.append(f"  {name}: {_LAYOUT_NAMES[layout]} ({sz/1e6:.3f}MB)")
+
+        # Other bulk fields (fp8 data, fp16 large tensors, scale_delta tensors,
+        # etc.) — fall back to v1's torch.save+pick path.
+        for key, value in list(new_entry.items()):
+            if key == "packed":  # already handled
+                continue
+            if _is_bulk(value):
+                buf = io.BytesIO(); torch.save(value, buf)
+                method, comp = _pick_best_compressor(buf.getvalue(), level, has_lrzip)
+                bulk_bodies.append((method, comp))
+                method_counts["lzma" if method == _COMPRESSOR_LZMA else "lrzip"] += 1
+                layout_counts["raw"] += 1
+                new_entry[key] = {
+                    "_v2": _BULK_SENTINEL_V2, "idx": len(bulk_bodies) - 1,
+                    "layout": _LAYOUT_RAW,
+                }
+        metadata[name] = new_entry
+
+    # Compress metadata.
+    meta_buf = io.BytesIO(); torch.save(metadata, meta_buf)
+    meta_compressed = lzma.compress(meta_buf.getvalue(), preset=level)
+
+    # Assemble archive (header 0x03).
+    out = bytearray()
+    out.append(_COMPRESSOR_PER_STREAM_V2)
+    out.extend(len(meta_compressed).to_bytes(4, "little"))
+    out.extend(meta_compressed)
+    out.extend(len(bulk_bodies).to_bytes(4, "little"))
+    for method, body in bulk_bodies:
+        out.append(method)
+        out.extend(len(body).to_bytes(4, "little"))
+        out.extend(body)
+
+    layout_summary = ", ".join(f"{n}={c}" for n, c in layout_counts.items() if c)
+    audit = (f"per_stream_v2: meta={len(meta_compressed)/1e6:.2f}MB, "
+             f"{len(bulk_bodies)} bulk payloads "
+             f"(lzma={method_counts['lzma']}, lrzip={method_counts['lrzip']}; "
+             f"{layout_summary}), qkv_splits={qkv_split_count}, "
+             f"total={len(out)/1e6:.2f}MB")
+    return bytes(out), audit
+
+
+def _deserialize_per_stream_v2(blob: bytes) -> dict:
+    """Inverse of _serialize_per_stream_v2. Returns a state_dict whose quinary
+    entries have canonical entry["packed"] bytes — `deq_sd` is unaware of the
+    archive layer."""
+    cursor = 0
+    assert blob[cursor] == _COMPRESSOR_PER_STREAM_V2, \
+        f"expected v2 header 0x03, got {blob[0]:#x}"
+    cursor += 1
+    meta_size = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+    meta_blob = blob[cursor:cursor + meta_size]; cursor += meta_size
+    metadata = torch.load(io.BytesIO(lzma.decompress(meta_blob)),
+                          map_location="cpu", weights_only=False)
+    n_payloads = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+    decompressed: list[bytes] = []
+    for _ in range(n_payloads):
+        method = blob[cursor]; cursor += 1
+        size = int.from_bytes(blob[cursor:cursor + 4], "little"); cursor += 4
+        body = blob[cursor:cursor + size]; cursor += size
+        if method == _COMPRESSOR_LZMA:
+            decompressed.append(lzma.decompress(body))
+        elif method == _COMPRESSOR_LRZIP_ZPAQ:
+            decompressed.append(_lrzip_decompress_bytes(body))
+        else:
+            raise ValueError(f"unknown compressor method {method:#x}")
+
+    def _is_v2_ref(v):
+        return isinstance(v, dict) and v.get("_v2") == _BULK_SENTINEL_V2
+
+    for name, entry in metadata.items():
+        if not isinstance(entry, dict):
+            continue
+        for key, value in list(entry.items()):
+            if _is_v2_ref(value):
+                if value["layout"] == _LAYOUT_RAW:
+                    entry[key] = torch.load(io.BytesIO(decompressed[value["idx"]]),
+                                            map_location="cpu", weights_only=False)
+                else:
+                    entry[key] = _layout_to_canonical_bytes(
+                        value["layout"], decompressed[value["idx"]],
+                        value["rows"], value["cols"], value["meta"])
+            elif (isinstance(value, tuple) and len(value) == 2
+                  and value[0] == "__QKV_SPLIT__"):
+                # Reconstruct each part's symbol matrix, concat along row dim,
+                # repack as canonical base-5.
+                refs = value[1]
+                sub_symbols = []
+                for ref in refs:
+                    canonical_body = _layout_to_canonical_bytes(
+                        ref["layout"], decompressed[ref["idx"]],
+                        ref["rows"], ref["cols"], ref["meta"])
+                    n = ref["rows"] * ref["cols"]
+                    sub_syms = unpack_quinary(canonical_body, n).numpy()[:n]
+                    sub_symbols.append(sub_syms.reshape(ref["rows"], ref["cols"]))
+                full = np.concatenate(sub_symbols, axis=0).reshape(-1).copy()
+                entry[key], _ = pack_quinary(torch.from_numpy(full))
+    return metadata
+
+
+def _quant_scale(scale_fp16: Tensor, bits: int) -> dict:
+    """Quantize per-group scales to bits-bit log-deltas. Returns dict with
+    `scale_anchor` (fp16), `scale_step` (fp16), `scale_delta` (packed bytes
+    or int8 tensor), plus `scale_bits` to dispatch in deq_sd."""
+    scale_f = scale_fp16.float().reshape(-1)
+    n_groups = scale_f.numel()
+    log_scale = torch.log2(scale_f.clamp(min=1e-12))
+    lo, hi = float(log_scale.min().item()), float(log_scale.max().item())
+    anchor = (lo + hi) / 2.0
+    spread = hi - lo
+    levels = 1 << bits
+    half = levels // 2
+    step = max(spread / max(levels - 1, 1), 1e-9)
+    delta = ((log_scale - anchor) / step).round().clamp(-half, half - 1).to(torch.int32)
+    if bits == 4:
+        d = (delta + half).to(torch.uint8).numpy()
+        if len(d) % 2:
+            d = np.concatenate([d, np.zeros(1, dtype=np.uint8)])
+        packed = (d[::2] | (d[1::2] << 4)).astype(np.uint8)
+        delta_stored = torch.from_numpy(packed.copy())
+    elif bits == 5:
+        delta_stored = (delta + half).to(torch.uint8)
+    else:  # 8
+        delta_stored = delta.to(torch.int8)
+    return {
+        "scale_anchor": torch.tensor(anchor, dtype=torch.float16),
+        "scale_step": torch.tensor(step, dtype=torch.float16),
+        "scale_delta": delta_stored,
+        "scale_n_groups": n_groups,
+        "scale_bits": bits,
+    }
+
+def _dequant_scale(entry: dict) -> Tensor:
+    """Reconstruct fp16 per-group scales from the quantized representation."""
+    bits = int(entry["scale_bits"])
+    half = (1 << bits) // 2
+    n_groups = int(entry["scale_n_groups"])
+    if bits == 4:
+        packed = entry["scale_delta"].to(torch.uint8).numpy()
+        low = packed & 0x0F
+        high = (packed >> 4) & 0x0F
+        d = np.empty(packed.size * 2, dtype=np.uint8)
+        d[0::2] = low
+        d[1::2] = high
+        delta = torch.from_numpy(d[:n_groups].astype(np.int32) - half)
+    elif bits == 5:
+        delta = entry["scale_delta"].to(torch.int32) - half
+    else:  # 8
+        delta = entry["scale_delta"].to(torch.int32)
+    anchor = entry["scale_anchor"].float()
+    step = entry["scale_step"].float()
+    return (2.0 ** (anchor + delta.float() * step)).to(torch.float16)
+
+
+def q_sd(state_dict: dict, group_size: int = 64,
+         scale_quant_bits: int = 0) -> tuple[dict, dict]:
+    quantized = {}
+    stats = {"quinary_params": 0, "quinary_bytes": 0, "fp_params": 0, "fp_bytes": 0}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().float().contiguous()
+        t_orig_shape = list(t.shape)
+        if t.ndim == 3:
+            t = t.reshape(t.shape[0], -1)
+        is_quantized_candidate = (
+            t.ndim == 2 and t.numel() > 65_536
+            and "tok_emb" not in name and "lm_head" not in name and "embed_proj" not in name
+        )
+        if is_quantized_candidate:
+            pad = (group_size - t.shape[1] % group_size) % group_size
+            t_padded = F.pad(t, (0, pad)) if pad > 0 else t
+            t_grouped = t_padded.reshape(-1, group_size)
+            scale = t_grouped.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+            q = (t_grouped / scale).round().clamp(-2, 2).to(torch.int8)
+
+            packed_bytes, n_packed = pack_quinary(q)
+            stats["quinary_params"] += t.numel()
+
+            entry = {
+                "type": "quinary", "packed": packed_bytes,
+                "shape": list(t.shape), "padded_cols": t_padded.shape[1],
+                "group_size": group_size, "n_quins": n_packed,
+                "orig_shape": t_orig_shape,
+            }
+            if scale_quant_bits and scale_quant_bits in (4, 5, 8):
+                sq = _quant_scale(scale.half().squeeze(-1), scale_quant_bits)
+                entry.update(sq)
+                stats["quinary_bytes"] += len(packed_bytes) + sq["scale_delta"].numel() + 4  # +4 for anchor+step
+            else:
+                entry["scale"] = scale.half().squeeze(-1)
+                stats["quinary_bytes"] += len(packed_bytes) + scale.numel() * 2
+            quantized[name] = entry
+        elif t.ndim == 2:
+            quantized[name] = {"type": "fp8", "data": t.to(torch.float8_e4m3fn)}
+            stats["fp_params"] += t.numel()
+            stats["fp_bytes"] += t.numel()
+        else:
+            quantized[name] = {"type": "fp16", "data": t.half()}
+            stats["fp_params"] += t.numel()
+            stats["fp_bytes"] += t.numel() * 2
+    return quantized, stats
+
+def deq_sd(quantized: dict, target_dtype=torch.bfloat16):
+    out = {}
+    for name, entry in quantized.items():
+        if entry["type"] == "quinary":
+            q = unpack_quinary(entry["packed"], entry["n_quins"])
+            q = q.float().reshape(-1, entry["group_size"])
+            if "scale_bits" in entry:
+                scale = _dequant_scale(entry).float().unsqueeze(-1)
+            else:
+                scale = entry["scale"].float().unsqueeze(-1)
+            q_absmean = q.abs().mean(-1, keepdim=True).clamp(min=1e-8)
+            t = (q * (scale / q_absmean)).reshape(-1, entry["padded_cols"])
+            shape = entry["shape"]
+            result = t[:shape[0], :shape[1]].to(target_dtype)
+            orig = entry.get("orig_shape")
+            out[name] = result.reshape(orig).contiguous() if orig and orig != shape else result.contiguous()
+        elif entry["type"] == "fp8":
+            out[name] = entry["data"].to(torch.float32).to(target_dtype).contiguous()
+        else:
+            out[name] = entry["data"].to(target_dtype).contiguous()
+    return out
+
+def quin_stats(model: nn.Module, group_size: int = 64):
+    total = zeros = 0
+    with torch.no_grad():
+        for name, p in model.named_parameters():
+            if p.ndim == 2 and ("weight" in name or "prototypes" in name) and p.shape[0] > 1 and p.numel() % group_size == 0:
+                w = p.detach().float().reshape(-1, group_size)
+                scale = w.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+                q = (w / scale).round().clamp(-2, 2)
+                zeros += int((q == 0).sum().item())
+                total += int(q.numel())
+    return {"zero_frac": zeros / max(total, 1), "total_weights": total}
+
+_prev_committed: dict = {}
+
+def churn_fn(model: nn.Module, group_size: int = 64):
+    global _prev_committed
+    total = flipped = 0
+    with torch.no_grad():
+        for name, p in model.named_parameters():
+            if p.ndim == 2 and ("weight" in name or "prototypes" in name) and p.shape[0] > 1 and p.numel() % group_size == 0:
+                w = p.detach().float().reshape(-1, group_size)
+                scale = w.abs().mean(-1, keepdim=True).clamp(min=1e-8).half().float()
+                q = (w / scale).round().clamp(-2, 2).cpu().numpy()
+                if name in _prev_committed:
+                    flipped += int(np.sum(q != _prev_committed[name]))
+                    total += q.size
+                _prev_committed[name] = q
+    return flipped / max(total, 1)
+
+def ns_orth(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, wd: float = 0.0):
+        super().__init__(params, dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, wd=wd))
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr, momentum = group["lr"], group["momentum"]
+            backend_steps, nesterov = group["backend_steps"], group["nesterov"]
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = F.rms_norm(g.float(), (g.size(-1),)).bfloat16()
+                    g = ns_orth(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr:curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+            wd = group.get("wd", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.mul_(1 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+def ld_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = ld_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self):
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = ld_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos:self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank, self.world_size, self.device = rank, world_size, device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start:start + per_rank_span].pin_memory().to(self.device, non_blocking=True).to(torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x, y
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+def apply_fp8_ste(w: Tensor) -> Tensor:
+    w_sim = w.to(torch.float8_e4m3fn).to(w.dtype)
+    return (w_sim - w).detach() + w
+
+class QATLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w_qat = apply_fp8_ste(self.weight)
+        return F.linear(x, w_qat.to(x.dtype), self.bias.to(x.dtype) if self.bias is not None else None)
+
+class QATEmbedding(nn.Embedding):
+    def forward(self, input: Tensor) -> Tensor:
+        w_qat = apply_fp8_ste(self.weight)
+        return F.embedding(input, w_qat, self.padding_idx, self.max_norm,
+                           self.norm_type, self.scale_grad_by_freq, self.sparse)
+
+class QuinaryLinear(nn.Linear):
+    def __init__(self, in_features, out_features, bias=False, group_size=64):
+        super().__init__(in_features, out_features, bias=bias)
+        self.group_size = group_size
+        num_groups = (in_features * out_features) // group_size
+        # Inert by design: the STE detach below blocks gradients to
+        # `scale_correction`. Kept as a fp32 buffer at value 1.0 for
+        # backwards-compatibility with the ternary-base state-dict layout.
+        # An attempt to fix the STE so this parameter receives gradients
+        # was tested 2026-05-01 (commit b9c…) and showed a small
+        # training-time regression with no TTT benefit, so reverted.
+        self.scale_correction = nn.Parameter(torch.ones(num_groups, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.bfloat16()
+        g = self.group_size
+        w_g = w.reshape(-1, g)
+        scale = w_g.abs().mean(-1, keepdim=True).clamp(min=1e-8) * self.scale_correction.to(w.dtype).unsqueeze(-1)
+        q = (w_g / scale).round().clamp(-2, 2)
+        w_quantized = w + ((q * scale).reshape(w.shape) - w).detach()
+        return F.linear(x, w_quantized,
+                        self.bias.to(x.dtype) if self.bias is not None else None)
+
+
+class NormedQuinaryLinear(QuinaryLinear):
+    def forward(self, x: Tensor) -> Tensor:
+        return super().forward(F.rms_norm(x, (x.size(-1),)))
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(p in name for p in CTP)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0, no_cache: bool = False,
+                 rope_type: str = "rope", yarn_max_len: int = 4096, train_seq_len: int = 1024):
+        super().__init__()
+        self.no_cache = no_cache
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        if rope_type == "yarn":
+            scale = train_seq_len / yarn_max_len
+            freq_idx = torch.arange(0, dim, 2, dtype=torch.float32)
+            ramp = torch.clamp((freq_idx / dim - 0.25) / 0.75, 0.0, 1.0)
+            inv_freq = inv_freq / (ramp * (1.0 / scale - 1.0) + 1.0)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len, device, dtype):
+        if self.no_cache:
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            return freqs.cos()[None, :, None, :].to(dtype=dtype), freqs.sin()[None, :, None, :].to(dtype=dtype)
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, :, None, :]
+            self._sin_cached = freqs.sin()[None, :, None, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init,
+                 group_size=64, no_cache=False, rope_type="rope",
+                 yarn_max_len=4096, train_seq_len=1024):
+        super().__init__()
+        self.num_heads, self.num_kv_heads = num_heads, num_kv_heads
+        self.head_dim = dim // num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        self.c_qkv = QuinaryLinear(dim, self.q_size + 2 * self.kv_size, bias=False, group_size=group_size)
+        self.proj = NormedQuinaryLinear(dim, dim, bias=False, group_size=group_size)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base, no_cache=no_cache,
+                             rope_type=rope_type, yarn_max_len=yarn_max_len,
+                             train_seq_len=train_seq_len)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        qkv_out = self.c_qkv(x)
+        q_out, k_out, v_out = qkv_out.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q = q_out.reshape(bsz, seqlen, self.num_heads, self.head_dim)
+        k = k_out.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        v = v_out.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        q, k = F.rms_norm(q, (q.size(-1),)), F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None]
+        y = flash_attn_func(q.contiguous(), k.contiguous(), v.contiguous(), causal=True)
+        y = y.reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+class MLP(nn.Module):
+    def __init__(self, dim, mlp_mult, group_size=64, activation="swiglu"):
+        super().__init__()
+        hidden = mlp_mult * dim
+        self.activation = activation
+        if activation == "swiglu":
+            self.gate_up = QuinaryLinear(dim, hidden * 2, bias=False, group_size=group_size)
+        else:
+            self.fc = QuinaryLinear(dim, hidden, bias=False, group_size=group_size)
+        self.proj = NormedQuinaryLinear(hidden, dim, bias=False, group_size=group_size)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        if self.activation == "swiglu":
+            gu = self.gate_up(x)
+            gate, up = gu.chunk(2, dim=-1)
+            return self.proj(F.silu(gate) * up)
+        elif self.activation == "relu":
+            return self.proj(torch.relu(self.fc(x)))
+        elif self.activation == "leaky_relu":
+            return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.01))
+        elif self.activation == "leaky_relu2":
+            return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square())
+        else:  # relu2
+            return self.proj(torch.relu(self.fc(x)).square())
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int,
+                 rope_base: float, qk_gain_init: float, group_size: int=64,
+                 activation: str="swiglu", no_cache: bool=False,
+                 rope_type: str="rope", yarn_max_len: int=4096,
+                 train_seq_len: int=1024):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init,
+                                        group_size, no_cache, rope_type, yarn_max_len, train_seq_len)
+        self.mlp = MLP(dim, mlp_mult, group_size, activation)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x_in = mix[0] * x + mix[1] * x0
+        n = self.attn_norm(x_in)
+        x = x_in + self.attn_scale.to(dtype=x_in.dtype) * self.attn(n)
+        x = x + self.mlp_scale.to(dtype=x.dtype) * self.mlp(self.mlp_norm(x))
+        return x
+
+class GPT(nn.Module):
+    def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, mlp_mult,
+                 tie_embeddings, tied_embed_init_std, logit_softcap, rope_base, qk_gain_init,
+                 group_size: int = 64, activation: str = "swiglu",
+                 embed_dim: int = 0, softcap_type: str="poly", no_cache: bool=False,
+                 rope_type: str="rope", yarn_max_len: int=4096, train_seq_len: int=1024):
+        super().__init__()
+        self.tie_embeddings = tie_embeddings
+        self.logit_softcap = logit_softcap
+        self.softcap_type = softcap_type
+        self.embed_dim = embed_dim if embed_dim > 0 else model_dim
+        self.tok_emb = QATEmbedding(vocab_size, self.embed_dim)
+        self.embed_proj = QATLinear(self.embed_dim, model_dim, bias=False) if self.embed_dim != model_dim else None
+        self.embed_proj_rev = QATLinear(model_dim, self.embed_dim, bias=False) if self.embed_dim != model_dim else None
+
+        self.blocks = nn.ModuleList([
+            Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init,
+                  group_size, activation, no_cache, rope_type, yarn_max_len, train_seq_len)
+            for _ in range(num_layers)
+        ])
+
+        # U-Net split: first half encoder, second half decoder, decoder layers
+        # add a learned-weighted skip from the symmetric encoder layer.
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+
+        self.final_norm = RMSNorm()
+        self.lm_head = QATLinear(model_dim, vocab_size, bias=False)
+        self.lm_head._zero_init = True
+        if tie_embeddings:
+            self.lm_head.weight.requires_grad_(False)
+
+        self.vocab_bias = nn.Parameter(torch.zeros(vocab_size, dtype=torch.float32))
+        self._init_weights(tied_embed_init_std)
+
+    def _init_weights(self, tied_embed_init_std: float) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=tied_embed_init_std)
+        for module in self.modules():
+            if isinstance(module, QuinaryLinear) and not getattr(module, "_zero_init", False):
+                nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            elif isinstance(module, nn.Linear) and getattr(module, "_zero_init", False):
+                nn.init.zeros_(module.weight)
+
+    def _compute_logits(self, x: Tensor) -> Tensor:
+        if self.tie_embeddings:
+            proj = self.embed_proj_rev(x) if self.embed_proj_rev is not None else x
+            logits_raw = F.linear(proj, self.tok_emb.weight.to(x.dtype))
+        else:
+            logits_raw = self.lm_head(x)
+        return logits_raw + self.vocab_bias.to(x.dtype)
+
+    def _softcap(self, logits: Tensor) -> Tensor:
+        s = self.logit_softcap
+        if self.softcap_type == "tanh":
+            return s * torch.tanh(logits / s)
+        x_sc = torch.clamp(logits / s, -2.0, 2.0)
+        x2 = x_sc * x_sc
+        return s * torch.clamp(x_sc * (1.0 - x2 / 3.0 + x2 * x2 / 15.0), -1.0, 1.0)
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor, reduction: str = "mean") -> Tensor:
+        x = self.tok_emb(input_ids).float()
+        if self.embed_proj is not None:
+            x = self.embed_proj(x)
+        x = F.rms_norm(x, (x.size(-1),))
+        x0 = x
+
+        # U-Net encoder/decoder with skip connections.
+        skips = []
+        for vi in range(self.num_encoder_layers):
+            x = self.blocks[vi](x, x0)
+            skips.append(x)
+        for vi in range(self.num_decoder_layers):
+            if vi < self.num_skip_weights:
+                x = x + self.skip_weights[vi].to(dtype=x.dtype) * skips.pop()
+            x = self.blocks[self.num_encoder_layers + vi](x, x0)
+
+        x_normed = self.final_norm(x)
+        x_flat = x_normed.reshape(-1, x_normed.size(-1))
+        targets = target_ids.reshape(-1)
+        logits = self._softcap(self._compute_logits(x_flat))
+
+        if reduction == "none":
+            return F.cross_entropy(logits.float(), targets, reduction="none").reshape(input_ids.shape)
+
+        # Fused CE: single logsumexp; z-loss only during training (not eval BPB).
+        logits_f = logits.float()
+        lse = torch.logsumexp(logits_f, dim=-1)
+        target_logits = logits_f.gather(1, targets.unsqueeze(1)).squeeze(1)
+        main_loss = (lse - target_logits).mean()
+        if self.training:
+            main_loss = main_loss + 1e-4 * (lse ** 2).mean()
+        return main_loss
+
+
+def build_luts(sp, vocab_size: int, device: torch.device):
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+def ld_val(pattern, seq_len, max_tok=int(os.environ.get("VAL_MAX_TOKENS", 0))):
+    files = sorted(glob.glob(pattern))
+    assert files, f"No files: {pattern}"
+    tok = torch.cat([ld_shard(Path(p)) for p in files]).contiguous()
+    if max_tok > 0: tok = tok[:max_tok + 1]
+    u = ((tok.numel() - 1) // seq_len) * seq_len
+    return tok[:u + 1]
+
+
+def eval_val(args, model, rank, world_size, device, grad_accum_steps, val_tokens,
+             base_bytes_lut, has_leading_space_lut, is_boundary_token_lut):
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    local_batch_seqs = max(1, local_batch_tokens // args.train_seq_len)
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.no_grad():
+        for batch_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_end = min(batch_start + local_batch_seqs, seq_end)
+            raw_start = batch_start * args.train_seq_len
+            raw_end = batch_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64)
+            x, y = local[:-1].reshape(-1, args.train_seq_len), local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                batch_loss = model(x, y).detach()
+            n = float(y.numel())
+            loss_sum += batch_loss.to(torch.float64) * n
+            token_count += n
+            prev_ids, tgt_ids = x.reshape(-1), y.reshape(-1)
+            tok_bytes = base_bytes_lut[tgt_ids].to(torch.int16)
+            tok_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(torch.int16)
+            byte_count += tok_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        for t in (loss_sum, token_count, byte_count):
+            dist.all_reduce(t, op=dist.ReduceOp.SUM)
+    val_loss = loss_sum / token_count
+    bpb = (val_loss.item() / math.log(2.0)) * (token_count.item() / byte_count.item())
+    model.train()
+    return float(val_loss.item()), float(bpb), int(token_count.item()), int(byte_count.item())
+
+def main() -> None:
+    args = Hyperparameters()
+    code = Path(__file__).read_text(encoding="utf-8")
+
+    if args.matrix_optimizer != "adamw":
+        global ns_orth
+        ns_orth = torch.compile(ns_orth)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    grad_accum_steps = max(1, 8 // world_size)
+    grad_scale = 1.0 / grad_accum_steps
+
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+    os.makedirs("logs/cuda/", exist_ok=True)
+    logfile = f"logs/cuda/{args.run_id}.txt" if master_process else None
+    if master_process:
+        print(logfile)
+        # Truncate any pre-existing logfile from a previous run with the same
+        # RUN_ID so the per-RUN_ID log file isn't append-stacked across reruns.
+        if logfile:
+            open(logfile, "w", encoding="utf-8").close()
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+
+    log0(f"Python {sys.version}", console=False)
+    log0(f"PyTorch {torch.__version__}", console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    # Guard against an obvious tokenizer/vocab-size mismatch that would
+    # silently produce a wrong byte-count LUT (and therefore a wrong BPB
+    # denominator) without crashing.
+    assert sp.vocab_size() == args.vocab_size, (
+        f"tokenizer vocab_size ({sp.vocab_size()}) != args.vocab_size "
+        f"({args.vocab_size}); check TOKENIZER_PATH / VOCAB_SIZE")
+    val_tokens = ld_val(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_luts(
+        sp, args.vocab_size, device)
+
+    # --- Model ---
+    base_model = GPT(
+        vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim,
+        num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init,
+        group_size=args.bitnet_group_size, activation=args.activation_type,
+        embed_dim=args.embed_dim,
+        softcap_type=args.softcap_type, no_cache=(args.compile_mode == "reduce-overhead"),
+        rope_type=args.rope_type, yarn_max_len=args.yarn_max_len, train_seq_len=args.train_seq_len,
+    ).to(device).bfloat16()
+
+    for module in base_model.modules():
+        if isinstance(module, nn.Linear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+    if args.tie_embeddings:
+        base_model.lm_head.weight.requires_grad_(False)
+
+    torch._dynamo.config.optimize_ddp = False
+
+    compiled_model = torch.compile(base_model, mode=args.compile_mode if args.compile_mode != "default" else None)
+    use_find_unused = not args.tie_embeddings
+    model = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False,
+                find_unused_parameters=use_find_unused,
+                static_graph=not use_find_unused,
+                gradient_as_bucket_view=True) if distributed else compiled_model
+
+    # --- Optimizers ---
+    _excl = {"tok_emb.weight", "lm_head.weight"}
+    all_other_params = [(n, p) for n, p in base_model.named_parameters()
+                        if not any(eh in n for eh in _excl)]
+    matrix_params = [p for n, p in all_other_params
+                     if p.ndim == 2 and not any(pat in n for pat in CTP)]
+    scalar_params = [p for n, p in all_other_params
+                     if p.ndim < 2 or any(pat in n for pat in CTP)]
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    opt_tok = torch.optim.Adam(
+        [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+    if args.matrix_optimizer == "adamw":
+        opt_muon = torch.optim.AdamW(
+            [{"params": matrix_params, "lr": args.adam_lr, "base_lr": args.adam_lr}],
+            betas=(args.beta1, args.beta2), eps=args.adam_eps, weight_decay=args.adam_wd, fused=True)
+    else:
+        opt_muon = Muon(matrix_params, lr=args.matrix_lr, momentum=args.muon_momentum,
+                        backend_steps=args.muon_backend_steps, wd=args.muon_wd)
+    for g in opt_muon.param_groups:
+        g["base_lr"] = args.matrix_lr
+    opt_scalar = torch.optim.Adam(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+    opt_head = torch.optim.Adam(
+        [{"params": [base_model.lm_head.weight], "lr": 0.0, "base_lr": 0.0}],
+        betas=(args.beta1, args.beta2), eps=args.adam_eps, fused=True)
+
+    optimizers = [opt_tok, opt_muon, opt_scalar, opt_head]
+
+    # --- Log all hyperparameters ---
+    log0("--- Hyperparameters ---", console=False)
+    log0(" ".join(f"{a}={getattr(args,a)}" for a in sorted(dir(args)) if not a.startswith("_") and a not in ("train_files","val_files") and not callable(getattr(args,a))), console=False)
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"params:{n_params} L:{args.num_layers} d:{args.model_dim} h:{args.num_heads} kv:{args.num_kv_heads} ws:{world_size} ga:{grad_accum_steps} s:{args.seed}")
+
+    # --- Data loader & helpers ---
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all():
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float):
+        if args.warmdown_fraction <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = int(args.iterations * (1.0 - args.warmdown_fraction))
+            ratio = max((args.iterations - step) / max(args.iterations * args.warmdown_fraction, 1), 0.0) if step >= warmdown_start else 1.0
+        else:
+            warmdown_ms = max_wallclock_ms * args.warmdown_fraction
+            remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+            ratio = remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+        return max(ratio, args.min_lr)
+
+    _seq_switched = False
+    _batch_switched = False
+    active_seq_len = args.seq_len_start if args.seq_len_start > 0 else args.train_seq_len
+    active_batch_tokens = args.batch_tokens_start if args.batch_tokens_start > 0 else args.train_batch_tokens
+
+    if args.skip_training:
+        log0("skip_training=1, using existing artifact final_model.quinary.ptz")
+
+    # --- Compiler warmup ---
+    if args.warmup_steps > 0 and not args.skip_training:
+        _ms = {n: t.detach().cpu().clone() for n, t in base_model.state_dict().items()}
+        _os = [copy.deepcopy(o.state_dict()) for o in optimizers]
+        model.train()
+        for ws in range(args.warmup_steps):
+            zero_grad_all()
+            for mi in range(grad_accum_steps):
+                if distributed: model.require_backward_grad_sync = mi == grad_accum_steps - 1
+                x, y = train_loader.next_batch(active_batch_tokens, active_seq_len, grad_accum_steps)
+                torch.compiler.cudagraph_mark_step_begin()
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16): loss = model(x, y)
+                (loss * grad_scale).backward()
+            for o in optimizers: o.step()
+            zero_grad_all()
+            log0(f"warmup:{ws+1}/{args.warmup_steps}")
+        base_model.load_state_dict(_ms, strict=True)
+        for o, s in zip(optimizers, _os): o.load_state_dict(s)
+        zero_grad_all()
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # --- Main training loop ---
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    train_loss = torch.zeros((), device=device)
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    step = 0
+
+    while not args.skip_training:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        if last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0):
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb, val_tok_count, val_byte_count = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut)
+            tstats = quin_stats(base_model, group_size=args.bitnet_group_size)
+            log0(f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                 f"train_time:{training_time_ms:.0f}ms zero_frac:{tstats['zero_frac']:.3f} "
+                 f"eval_tokens:{val_tok_count} eval_bytes:{val_byte_count}")
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}/{args.iterations}")
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+
+        # Sequence length schedule
+        if args.seq_len_start > 0 and not _seq_switched:
+            if max_wallclock_ms is not None:
+                should_switch_seq = elapsed_ms >= args.seq_schedule_fraction * max_wallclock_ms
+            else:
+                should_switch_seq = step >= int(args.iterations * args.seq_schedule_fraction)
+            if should_switch_seq:
+                active_seq_len = args.train_seq_len
+                _seq_switched = True
+                torch._dynamo.reset()
+                train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+                log0(f"step:{step} seq_len_switch:{args.seq_len_start}->{active_seq_len}")
+
+        # Batch size schedule
+        if args.batch_tokens_start > 0 and not _batch_switched:
+            if max_wallclock_ms is not None:
+                should_switch_batch = elapsed_ms >= args.batch_schedule_fraction * max_wallclock_ms
+            else:
+                should_switch_batch = step >= int(args.iterations * args.batch_schedule_fraction)
+            if should_switch_batch:
+                active_batch_tokens = args.train_batch_tokens
+                _batch_switched = True
+                log0(f"step:{step} batch_switch:{args.batch_tokens_start}->{active_batch_tokens}")
+
+        zero_grad_all()
+        train_loss.zero_()
+
+        for micro in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro == grad_accum_steps - 1
+            x, y = train_loader.next_batch(active_batch_tokens, active_seq_len, grad_accum_steps)
+            torch.compiler.cudagraph_mark_step_begin()
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                loss = model(x, y)
+            train_loss.add_(loss.detach())
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        # Muon momentum warmup (skip when AdamW is the matrix optimizer)
+        if args.matrix_optimizer != "adamw":
+            frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0
+            for g in opt_muon.param_groups:
+                g["momentum"] = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+
+        # LR scheduling
+        for opt in optimizers:
+            for g in opt.param_groups:
+                g["lr"] = g["base_lr"] * scale
+            opt.step()
+        zero_grad_all()
+        step += 1
+        approx_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        
+        if args.train_log_every > 0 and step % args.train_log_every == 0:
+            log0(f"step:{step}/{args.iterations} loss:{train_loss.item():.4f} t:{approx_ms:.0f}ms avg:{approx_ms/step:.1f}ms")
+        if args.churn_log_every > 0 and step % args.churn_log_every == 0:
+            log0(f"step:{step} churn:{churn_fn(base_model, args.bitnet_group_size):.4f} zero:{quin_stats(base_model, args.bitnet_group_size)['zero_frac']:.3f}")
+
+        # Wallclock cap sync
+        if stop_after_step is None and max_wallclock_ms is not None and step % 10 == 0:
+            reached_cap = approx_ms >= max_wallclock_ms
+            if distributed:
+                cap_t = torch.tensor(int(reached_cap), device=device)
+                dist.all_reduce(cap_t, op=dist.ReduceOp.MAX)
+                reached_cap = bool(cap_t.item())
+            if reached_cap:
+                stop_after_step = step
+
+    # --- Serialization ---
+    if master_process and not args.skip_training:
+        sd = base_model.state_dict()
+        if base_model.tie_embeddings:
+            sd.pop("lm_head.weight", None)
+
+        log0("serialize: start")
+        t_qsd = time.perf_counter()
+        q_obj, q_stats = q_sd(sd, group_size=args.bitnet_group_size,
+                              scale_quant_bits=args.scale_quant_bits)
+        qsd_time = time.perf_counter() - t_qsd
+        log0(f"serialize: q_sd in {qsd_time:.1f}s "
+             f"(quinary={q_stats['quinary_params']/1e6:.2f}M params/{q_stats['quinary_bytes']/1e6:.2f}MB, "
+             f"fp={q_stats['fp_params']/1e6:.2f}M params/{q_stats['fp_bytes']/1e6:.2f}MB)")
+
+        t_save = time.perf_counter()
+        buf = io.BytesIO()
+        torch.save(q_obj, buf)
+        raw_bytes = len(buf.getvalue())
+        log0(f"serialize: torch.save raw={raw_bytes/1e6:.2f}MB in {time.perf_counter()-t_save:.1f}s")
+
+        t_compress = time.perf_counter()
+        final_blob, per_stream_audit = _serialize_per_stream_v2(q_obj, level=9)
+        compress_time = time.perf_counter() - t_compress
+        artifact_bytes = len(final_blob)
+        log0(per_stream_audit)
+        log0(f"serialize: per_stream_v2 {raw_bytes/1e6:.2f}MB -> {artifact_bytes/1e6:.2f}MB "
+             f"(ratio {artifact_bytes/raw_bytes:.1%}, saved {(raw_bytes-artifact_bytes)/1e6:.2f}MB) "
+             f"in {compress_time:.1f}s")
+
+        with open("final_model.quinary.ptz", "wb") as f:
+            f.write(final_blob)
+
+        code_bytes = len(code.encode("utf-8"))
+
+        total = artifact_bytes + code_bytes
+        log0(f"artifact:{artifact_bytes/1e6:.2f}MB quinary:{q_stats['quinary_params']}({q_stats['quinary_bytes']}B) fp:{q_stats['fp_params']}({q_stats['fp_bytes']}B) code:{code_bytes}")
+        log0(f"budget:{total}/{16000000} ({total/1e6:.2f}/{16.00:.2f}MB) {'FITS' if total <= 16000000 else 'OVER'}")
+
+    # --- All ranks load roundtrip weights and evaluate ---
+    if distributed:
+        dist.barrier()
+
+    with open("final_model.quinary.ptz", "rb") as f:
+        loaded = _load_artifact(f.read())
+    missing, unexpected = base_model.load_state_dict(deq_sd(loaded), strict=False)
+    # The only "missing" key we expect is `lm_head.weight` when tied, since
+    # q_sd drops it from the saved state-dict. Any other missing/unexpected
+    # key is a serialization-roundtrip bug and should fail loudly.
+    expected_missing = {"lm_head.weight"} if base_model.tie_embeddings else set()
+    assert set(missing) <= expected_missing, f"unexpected missing keys after artifact load: {set(missing) - expected_missing}"
+    assert not unexpected, f"unexpected extra keys after artifact load: {unexpected}"
+    torch._dynamo.reset()
+
+    q_val_loss, q_val_bpb, q_tok_count, q_byte_count = eval_val(
+        args, model, rank, world_size, device, grad_accum_steps,
+        val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut)
+    log0(f"final_quinary_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+         f"eval_tokens:{q_tok_count} eval_bytes:{q_byte_count}")
+
+    # --- Score-first chunk-based CTP TTT ---
+    if args.ttt_steps > 0:
+        torch.cuda.synchronize()
+        t_ttt = time.perf_counter()
+        seq_len = args.train_seq_len
+        # TTT_STRIDE: sliding-window step used to slice the val stream into TTT
+        # examples. Hardcoded at 16 (canonical sp16384 quinary submission); a
+        # smaller stride yields more overlapping windows but more compute.
+        stride = 16
+        ttt_chunk_tokens = args.ttt_tokens if args.ttt_tokens > 0 else 32768
+        ttt_epochs = args.ttt_steps
+        batch_seqs = 32
+        total_tokens = val_tokens.numel() - 1
+
+        # Select TTT params (CTP only — quinary weights are frozen)
+        for p in base_model.parameters():
+            p.requires_grad_(False)
+        ttt_params = []
+        for name, p in base_model.named_parameters():
+            if any(pat in name for pat in CTP):
+                p.requires_grad_(True)
+                ttt_params.append(p)
+        n_ttt = sum(p.numel() for p in ttt_params)
+        ttt_opt = torch.optim.SGD(ttt_params, lr=args.ttt_lr, momentum=0.9)
+        for pg in ttt_opt.param_groups:
+            pg["initial_lr"] = pg["lr"]
+        log0(f"ttt: {n_ttt} CTP params, lr={args.ttt_lr}")
+
+        # Assign sliding windows to chunks
+        context_size = seq_len - stride
+        window_starts = [ws for ws in range(0, total_tokens, stride)
+                         if ws + context_size < total_tokens]
+        num_chunks = (total_tokens + ttt_chunk_tokens - 1) // ttt_chunk_tokens
+        chunk_windows = [[] for _ in range(num_chunks)]
+        for ws in window_starts:
+            s = 0 if ws == 0 else context_size
+            ci = min((ws + s) // ttt_chunk_tokens, num_chunks - 1)
+            chunk_windows[ci].append(ws)
+        log0(f"ttt: {n_ttt} CTP params, {num_chunks} chunks, {ttt_epochs} epochs, lr={args.ttt_lr}")
+
+        loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+        token_count = torch.zeros((), device=device, dtype=torch.float64)
+        byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+        # Recompile for TTT (find_unused_parameters needed with frozen params)
+        torch._dynamo.reset()
+        compiled_ttt = torch.compile(base_model, mode=args.compile_mode if args.compile_mode != "default" else None)
+
+        for ci in range(num_chunks):
+            windows = chunk_windows[ci]
+            if not windows:
+                continue
+            chunk_start = ci * ttt_chunk_tokens
+            chunk_end = min((ci + 1) * ttt_chunk_tokens, total_tokens)
+            my_s = len(windows) * rank // world_size
+            my_e = len(windows) * (rank + 1) // world_size
+            my_windows = windows[my_s:my_e]
+
+            # Phase 1: SCORE (no_grad, compiled)
+            base_model.eval()
+            with torch.no_grad():
+                for bi in range(0, len(my_windows), batch_seqs):
+                    batch_ws = my_windows[bi:bi + batch_seqs]
+                    bsz = len(batch_ws)
+                    x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                    y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                    wlens = []
+                    for i, ws in enumerate(batch_ws):
+                        we = min(ws + seq_len, total_tokens)
+                        wlen = we - ws
+                        wlens.append(wlen)
+                        chunk_tok = val_tokens[ws:we + 1].to(dtype=torch.int64, device=device)
+                        x_batch[i, :wlen] = chunk_tok[:-1]
+                        y_batch[i, :wlen] = chunk_tok[1:]
+                    with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                        nll = compiled_ttt(x_batch, y_batch, reduction="none").detach()
+                    for i, ws in enumerate(batch_ws):
+                        wlen = wlens[i]
+                        s = 0 if ws == 0 else context_size
+                        scored_nll = nll[i, s:wlen].to(torch.float64)
+                        loss_sum += scored_nll.sum()
+                        token_count += float(wlen - s)
+                        tgt, prev = y_batch[i, s:wlen], x_batch[i, s:wlen]
+                        tb = base_bytes_lut[tgt].to(torch.float64)
+                        tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                        byte_count += tb.sum()
+
+            # Phase 2: TRAIN on chunk (score-first: already scored)
+            if ci < num_chunks - 1 and ttt_epochs > 0:
+                base_model.train()
+                chunk_seqs = (chunk_end - chunk_start) // seq_len
+                if chunk_seqs > 0:
+                    cos_mul = 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1)))
+                    for pg in ttt_opt.param_groups:
+                        pg["lr"] = pg.get("initial_lr", pg["lr"]) * cos_mul
+                    my_seq_s = chunk_seqs * rank // world_size
+                    my_seq_e = chunk_seqs * (rank + 1) // world_size
+                    for _ep in range(ttt_epochs):
+                        for bs in range(0, my_seq_e - my_seq_s, batch_seqs):
+                            be = min(bs + batch_seqs, my_seq_e - my_seq_s)
+                            start_tok = chunk_start + (my_seq_s + bs) * seq_len
+                            end_tok = chunk_start + (my_seq_s + be) * seq_len + 1
+                            if end_tok > val_tokens.numel():
+                                continue
+                            local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64)
+                            x = local[:-1].reshape(-1, seq_len)
+                            y = local[1:].reshape(-1, seq_len)
+                            ttt_opt.zero_grad(set_to_none=True)
+                            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                                loss = compiled_ttt(x, y)
+                            loss.backward()
+                            if distributed:
+                                for p in ttt_params:
+                                    if p.grad is not None:
+                                        dist.all_reduce(p.grad, op=dist.ReduceOp.AVG)
+                            torch.nn.utils.clip_grad_norm_(ttt_params, 1.0)
+                            ttt_opt.step()
+
+            if master_process and ci % max(1, num_chunks // 5) == 0:
+                log0(f"ttt chunk:{ci+1}/{num_chunks}")
+
+        if distributed:
+            for t in (loss_sum, token_count, byte_count):
+                dist.all_reduce(t, op=dist.ReduceOp.SUM)
+        ttt_val_loss = (loss_sum / token_count).item()
+        ttt_bpb = (ttt_val_loss / math.log(2.0)) * (token_count.item() / byte_count.item())
+        for p in base_model.parameters():
+            p.requires_grad_(True)
+        torch._dynamo.reset()
+        torch.cuda.synchronize()
+        ttt_time_ms = 1000.0 * (time.perf_counter() - t_ttt)
+        log0(f"ttt_eval val_loss:{ttt_val_loss:.4f} val_bpb:{ttt_bpb:.4f} "
+             f"time:{ttt_time_ms:.0f}ms "
+             f"eval_tokens:{int(token_count.item())} eval_bytes:{int(byte_count.item())}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/verify_bpb.py b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/verify_bpb.py
new file mode 100644
index 0000000000..2dcb034429
--- /dev/null
+++ b/records/track_non_record_16mb/2026-04-30_Quinary_53M_10L_576d_SP16384_TTT/verify_bpb.py
@@ -0,0 +1,295 @@
+"""Standalone BPB-denominator correctness check.
+
+Verifies that train_gpt.py's per-token byte-count LUT matches independent
+SentencePiece decoding on the *same token stream and same scored slice* used by
+training/evaluation.
+
+Run from the submission directory after setup.sh:
+
+    python3 verify_bpb.py
+
+Optional env vars:
+    TOKENIZER_PATH, DATA_PATH, VOCAB_SIZE, TRAIN_SEQ_LEN
+"""
+
+from __future__ import annotations
+
+import glob
+import hashlib
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+import sentencepiece as spm
+
+
+SP_PATH = os.environ.get(
+    "TOKENIZER_PATH", "./data/canonical/tokenizers/fineweb_16384_bpe.model"
+)
+DATA_PATH = os.environ.get(
+    "DATA_PATH", "./data/canonical/datasets/fineweb10B_sp16384"
+)
+VAL_GLOB = os.environ.get("VAL_GLOB", str(Path(DATA_PATH) / "fineweb_val_*.bin"))
+VOCAB_SIZE = int(os.environ.get("VOCAB_SIZE", "16384"))
+TRAIN_SEQ_LEN = int(os.environ.get("TRAIN_SEQ_LEN", "1024"))
+
+MAGIC = 20240520
+VERSION = 1
+HEADER_INTS = 256
+HEADER_BYTES = HEADER_INTS * np.dtype("<i4").itemsize
+
+
+def sha256_file(path: Path) -> str:
+    h = hashlib.sha256()
+    with path.open("rb") as f:
+        for chunk in iter(lambda: f.read(1 << 20), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def ld_shard(path: Path) -> np.ndarray:
+    """Same shard loader as train_gpt.py: 256-int32 header, then u16 tokens."""
+    header = np.fromfile(path, dtype="<i4", count=HEADER_INTS)
+    if header.size != HEADER_INTS:
+        raise ValueError(f"{path}: short header ({header.size} int32s)")
+    if int(header[0]) != MAGIC or int(header[1]) != VERSION:
+        raise ValueError(
+            f"{path}: bad header magic/version "
+            f"({int(header[0])}, {int(header[1])})"
+        )
+    ntok = int(header[2])
+    expected_min_size = HEADER_BYTES + ntok * np.dtype("<u2").itemsize
+    actual_size = path.stat().st_size
+    if actual_size < expected_min_size:
+        raise ValueError(
+            f"{path}: file too small for ntok={ntok}: "
+            f"size={actual_size}, expected_at_least={expected_min_size}"
+        )
+    return np.fromfile(path, dtype="<u2", offset=HEADER_BYTES, count=ntok).astype(
+        np.int64
+    )
+
+
+def build_luts(sp: spm.SentencePieceProcessor, vocab_size: int):
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes = np.zeros((table_size,), dtype=np.int32)
+    has_leading_space = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token = np.ones((table_size,), dtype=np.bool_)
+    for tid in range(sp_vocab_size):
+        if sp.is_control(tid) or sp.is_unknown(tid) or sp.is_unused(tid):
+            continue
+        is_boundary_token[tid] = False
+        if sp.is_byte(tid):
+            base_bytes[tid] = 1
+            continue
+        piece = sp.id_to_piece(tid)
+        if piece.startswith("\u2581"):
+            has_leading_space[tid] = True
+            piece = piece[1:]
+        base_bytes[tid] = len(piece.encode("utf-8"))
+    return base_bytes, has_leading_space, is_boundary_token
+
+
+def lut_byte_count(
+    tokens: np.ndarray,
+    base_bytes: np.ndarray,
+    has_leading_space: np.ndarray,
+    is_boundary_token: np.ndarray,
+) -> int:
+    """Sum eval bytes over tokens[1:], with tokens[:-1] as previous context."""
+    tgt = tokens[1:]
+    prev = tokens[:-1]
+    base = base_bytes[tgt].astype(np.int64)
+    extra = (has_leading_space[tgt] & ~is_boundary_token[prev]).astype(np.int64)
+    return int((base + extra).sum())
+
+
+def bos_positions(tokens: np.ndarray, bos_id: int) -> np.ndarray:
+    return np.flatnonzero(tokens == bos_id)
+
+
+def decoded_bytes_docwise(
+    tokens: np.ndarray,
+    sp: spm.SentencePieceProcessor,
+    bos_id: int,
+    is_boundary_token: np.ndarray,
+) -> int:
+    """Decode bytes for tokens[1:] using BOS as an explicit document reset.
+
+    This mirrors the LUT convention: BOS/control tokens emit zero bytes, and the
+    first text token after BOS does not pay an artificial leading-space byte.
+
+    The normal canonical validation stream should start with BOS. If it does not,
+    we fall back to a prefix-subtraction check for the initial partial segment.
+    """
+    if len(tokens) < 2:
+        return 0
+
+    bpos = bos_positions(tokens, bos_id)
+    total = 0
+
+    # Initial partial segment before the first BOS in this slice. This should not
+    # happen for the exact eval slice; if it does, compare by prefix subtraction.
+    first_bos = int(bpos[0]) if len(bpos) else len(tokens)
+    if first_bos > 0:
+        initial = tokens[:first_bos]
+        full = sp.decode(initial.tolist()).encode("utf-8")
+        prefix = sp.decode(initial[:1].tolist()).encode("utf-8")
+        total += len(full) - len(prefix)
+
+    # BOS-delimited document segments. For a BOS at position s, target bytes start
+    # at s+1 and continue until the next BOS or end of slice.
+    for i, s0 in enumerate(bpos):
+        s = int(s0)
+        e = int(bpos[i + 1]) if i + 1 < len(bpos) else len(tokens)
+        if e > s + 1:
+            total += len(sp.decode(tokens[s + 1 : e].tolist()).encode("utf-8"))
+        # If e == s+1, the document is empty or the slice ends at BOS; emits 0.
+    return total
+
+
+def check_slice(
+    label: str,
+    tokens: np.ndarray,
+    sp: spm.SentencePieceProcessor,
+    base_bytes: np.ndarray,
+    has_leading_space: np.ndarray,
+    is_boundary_token: np.ndarray,
+    bos_id: int,
+) -> bool:
+    lut_sum = lut_byte_count(tokens, base_bytes, has_leading_space, is_boundary_token)
+    decoded_bytes = decoded_bytes_docwise(tokens, sp, bos_id, is_boundary_token)
+    delta = lut_sum - decoded_bytes
+    status = "PASS" if delta == 0 else "FAIL"
+    starts_boundary = bool(is_boundary_token[int(tokens[0])])
+    print(
+        f"  {status}  {label:36s} targets={len(tokens)-1:>10,d} "
+        f"lut_bytes={lut_sum:>12,d} decoded_bytes={decoded_bytes:>12,d} "
+        f"delta={delta:+d} start_boundary={starts_boundary}"
+    )
+    return delta == 0
+
+
+def main() -> int:
+    print("=" * 78)
+    print("BPB byte-count LUT verification")
+    print("=" * 78)
+
+    sp_path = Path(SP_PATH)
+    if not sp_path.exists():
+        print(f"Tokenizer not found at {sp_path}; run setup.sh first.")
+        return 2
+    sp = spm.SentencePieceProcessor(model_file=str(sp_path))
+    print(f"tokenizer    : {sp_path} (vocab={sp.vocab_size()})")
+    print(f"tokenizer sha: {sha256_file(sp_path)}")
+
+    bos = sp.bos_id()
+    eos = sp.eos_id()
+    unk = sp.unk_id()
+    print(f"BOS id={bos} ({sp.id_to_piece(bos)!r})  EOS id={eos} ({sp.id_to_piece(eos)!r})  UNK id={unk}")
+
+    val_files = sorted(glob.glob(VAL_GLOB))
+    if not val_files:
+        print(f"No val shards matched {VAL_GLOB}")
+        return 2
+    print(f"val shards   : {len(val_files)} ({val_files[0]} ... )")
+
+    base_bytes, has_leading_space, is_boundary_token = build_luts(sp, VOCAB_SIZE)
+
+    n_byte = sum(1 for tid in range(sp.vocab_size()) if sp.is_byte(tid))
+    n_control = sum(
+        1
+        for tid in range(sp.vocab_size())
+        if sp.is_control(tid) or sp.is_unknown(tid) or sp.is_unused(tid)
+    )
+    n_lead = int(has_leading_space[: sp.vocab_size()].sum())
+    n_bound = int(is_boundary_token[: sp.vocab_size()].sum())
+    print(
+        f"LUT stats    : byte-fallback={n_byte}  control/unknown/unused={n_control}  "
+        f"with-leading-space={n_lead}  boundary={n_bound}"
+    )
+
+    tokens_full = np.concatenate([ld_shard(Path(f)) for f in val_files])
+    print(f"shard tokens : {tokens_full.size:,}")
+    if tokens_full.size < 2:
+        print("Too few tokens")
+        return 2
+
+    bpos = bos_positions(tokens_full, bos)
+    print(
+        f"BOS positions: {len(bpos):,}; first={int(bpos[0]) if len(bpos) else 'NONE'} "
+        f"(expect 50,000 docs and normally first=0)"
+    )
+    unk_count = int((tokens_full == unk).sum()) if unk >= 0 else 0
+    print(f"UNK count    : {unk_count}")
+
+    all_pass = True
+    print("\nPer-slice LUT-vs-decode parity:")
+
+    # Exact scored slice used by train_gpt.py: truncate to a TRAIN_SEQ_LEN target multiple.
+    u = ((tokens_full.size - 1) // TRAIN_SEQ_LEN) * TRAIN_SEQ_LEN
+    tokens_eval = tokens_full[: u + 1]
+    omitted_targets = (tokens_full.size - 1) - u
+    print(
+        f"eval slice   : train_seq_len={TRAIN_SEQ_LEN} target_count={u:,} "
+        f"omitted_tail_targets={omitted_targets:,}"
+    )
+    all_pass &= check_slice(
+        "exact eval slice",
+        tokens_eval,
+        sp,
+        base_bytes,
+        has_leading_space,
+        is_boundary_token,
+        bos,
+    )
+
+    # Whole untruncated stream.
+    all_pass &= check_slice(
+        "full untruncated stream",
+        tokens_full,
+        sp,
+        base_bytes,
+        has_leading_space,
+        is_boundary_token,
+        bos,
+    )
+
+    # BOS-delimited prefix document checks.
+    if len(bpos) >= 2:
+        all_pass &= check_slice(
+            "doc at first BOS",
+            tokens_full[int(bpos[0]) : int(bpos[1])],
+            sp,
+            base_bytes,
+            has_leading_space,
+            is_boundary_token,
+            bos,
+        )
+    for n_docs in (10, 100, 1000):
+        if len(bpos) >= n_docs + 1:
+            all_pass &= check_slice(
+                f"first {n_docs} BOS docs",
+                tokens_full[int(bpos[0]) : int(bpos[n_docs])],
+                sp,
+                base_bytes,
+                has_leading_space,
+                is_boundary_token,
+                bos,
+            )
+
+    print()
+    if all_pass and unk_count == 0:
+        print("ALL CHECKS PASS — LUT bytes match SentencePiece decoder bytes on the eval slice.")
+        return 0
+    if unk_count != 0:
+        print("FAIL — validation contains UNK tokens; zero-byte UNK accounting is unsafe.")
+    else:
+        print("AT LEAST ONE CHECK FAILED — investigate before trusting BPB scores.")
+    return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())