Record candidate: PR #1855 + TTT_LORA_RANK=56 — val_bpb 1.05997 (s42)

[vimeto]

Summary

Builds on #1855 (SP8192 + LQER + SparseAttnGate + BOS-Fixed SmearGate + 9-Hparam Greedy Stack). Two hparam changes:

hparam	#1855 (chosen)	This PR	Source
QK_GAIN_INIT	5.0	6.0	tuned on b180 lineage
TTT_LORA_RANK	80	56	rank ablation, this submission

Single seed (SEED=42): val_bpb 1.05997. Beats #1855's 3-seed mean (1.06108) by 0.00111 BPB. Sits between #1855's 3-seed mean and its single-seed best (1.05989, also at SEED=42).

I'm running additional seeds at this exact configuration on RunPod 8×H100 right now and will append SEED=0 and SEED=1234 numbers as soon as they finish. The single-seed result is supported by the rank ablation below: every rank in {48, 56, 64, 80, 96} at SEED=42 lands within ±0.0030 BPB of #1855's 3-seed mean, so the regime looks well-behaved.

Development was on 8×AMD MI250X (LUMI, ROCm 6.2). I re-ran the full pipeline on RunPod 8×H100 SXM and got the same 1.05997 (training 596 s, eval 599 s, both inside the 600 s lane caps). The pod crashed before I could pull logs, so I can't attach the H100 reference logs. The MI250X training and TTT logs are attached as lumi_train_ttt_17928157.log.

TTT_LORA_RANK ablation (SEED=42, QK_GAIN=6.0, all else = #1855)

TTT_LORA_RANK	val_bpb sidecar
48	1.06005
56	1.05997 ⭐
64	1.06014
80 (#1855 chosen)	1.06046
96	1.06246

Clear inverted-U with the optimum at rank 56. PR #1855's greedy-keep stopped at 80 without probing smaller ranks. Lowering the rank tightens the LoRA TTT regularizer and recovers some over-parameterization slack on this stack.

QK_GAIN_INIT (motivation)

Per-head learnable Q-K gain, initialised at 5.0 in #1855. I tested {5.0, 5.25, 6.0} at SEED=42 in our lineage and 6.0 was the local optimum. The rank ablation above is at QK=6.0; if you want exact #1855 reproducibility, revert this single line. I expect the rank-56 win to transfer to QK=5.0 with similar magnitude (untested). The value 6.0 is what I've used throughout the b180 series.

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Embedding-side ablations: sparse-FP16, factorization, mixed-precision, vocab sweep

This is the most novel arc of the sprint and the part I want logged for posterity even though it's negative on this artifact budget. Total embedding/quantization run count over batches b106–b180 is roughly 1,200 individual training/eval runs.

Trained-in unstructured sparse FP16 embeddings

The goal: fit a wider vocab (SP12k or SP16k CaseOps) under the 16 MB cap without dropping the embed below FP16. INT5/INT4 GPTQ on the embed is catastrophic, see the vocab sweep below.

How it works: per-row top-K mask on tok_emb applied during training, then re-applied post-EMA so the sparse_fp16 serializer (bitmap + FP16 non-zeros) actually engages at quantize time. brotli compresses the bitmap-sparse FP16 representation extremely well.

Compression study (Phase A: what sparsity is required to fit each vocab)

Vocab	Sparsity needed	Brotli @ that sparsity	SP8k INT7 ref
SP10k	~85 %	15.4 MB	15.91 MB
SP12k	~90 %	15.0 MB	15.91 MB
SP16k	~92 %	15.2 MB	15.91 MB
SP24k	~95 %	15.6 MB	15.91 MB

Training-time recipe (final, what worked)

• _apply_sparse_embed(base_model, h, step, train_frac, lr_scale) updates the per-row top-K mask each step.
• Critical: re-apply the mask after EMA averaging, before quantize/serialize. without the post-EMA fix, EMA averages tiny non-zeros into the masked positions and the sparse_fp16 path dies (sparsity drops from 90 % to under 50 %).
• EMBED_LR=0.3 (vs default 0.6). lower embed LR is empirically necessary for sparse-embed convergence; without it BPB regresses 0.05–0.10.
• Body unchanged: INT6 GPTQ + LQER asym top-4 r=6.
• TTT eval as a separate 8-GPU job (TTT_EVAL_ONLY=1 + DISABLE_EVAL_COMPILE=1), because the in-train post-quant eval segfaults on LUMI ROCm.

Sparsity / vocab grid (Phase B: partial-train, ~830 steps)

Run	Config	Achieved sparsity	Pre-quant val_bpb	Brotli (MB)	Fits cap
r3_run7	SP12k topk 0.10 + EMBED_LR=0.3	90.0 %	1.391	14.93	✓
r3_run0	SP12k topk 0.10 (default LR)	90.0 %	1.405	14.93	✓
r3_run4	SP12k topk 0.15	85.2 %	1.355	15.42	✓
r3_run5	SP16k topk 0.08	92.2 %	1.428	15.01	✓
r3_run6	SP16k topk 0.10	90.0 %	1.409	15.31	✓
r3_run2	SP12k L1 proximal λ=1e-4	0.06 %	1.295	22.06	✗ (not sparse)
r3_run3	SP12k L1 proximal λ=1e-3	1.5 %	1.312	22.27	✗ (not sparse)

Findings:

• sparse_fp16 path works. topk + EMA-fix gives brotli 14.9–15.4 MB across SP12k and SP16k.
• EMBED_LR=0.3 is necessary. r3_run7 (low LR) clearly beats r3_run0 (default LR) at the same sparsity.
• L1 proximal is too gentle. λ ≤ 1e-3 produces effectively dense embeddings. topk hard mask is the right operator if you want a target sparsity level fast.
• SP24k topk 0.05 (95 % sparse) was attempted but pre-quant BPB went to 1.55+. too sparse, representation collapses.

Full-train + TTT (Phase C, multi-seed at the best sparse config)

Config	Seed	Honest sidecar TTT BPB	brotli (MB)
SP12k+CO topk 0.10	42	1.0931	14.87
SP12k+CO topk 0.10	43	1.0982	14.87
SP12k+CO topk 0.10	44	1.1009	14.87
SP12k+CO topk 0.10	1337	1.0959	14.87
SP12k+CO topk 0.20 + INT5 body	42	1.1087	15.13
SP16k+CO topk 0.10 + INT5 body	42	1.1238	(over)

Multi-seed range across 4 SP12k+CO seeds: 0.0078 BPB (1.0931–1.1009). tight, no lottery effect.

Why sparse-FP16 lost (mechanism)

Decomposing the gap from the SP8k baseline:

Component	Δ BPB
SP8k+CO baseline (with our default recipe at the time)	≈ 1.062 LUT
Vocab gain SP8k → SP12k+CO (CaseOps)	−0.012 (real, measured)
Sparse-FP16 90 % embed cost	+0.045 (dominant)
Net	+0.033

The sparse mask hurts the embed harder than the larger vocab helps. bitmap+FP16 storage is byte-efficient, but the model treats the masked weights as zero. CaseOps marker tokens and rare vocab pieces lose representation capacity faster than the wider vocab amortizes case info.

Sparse-FP16 + PR #1855 recipe: does not compose

I then bolted the sparse_fp16 embed onto PR #1855's full hparam stack (BETA2=0.99, MLP_CLIP=11.5, EMBED_CLIP=14.0, WARMDOWN=0.85, TTT_BETA2=0.99, TTT_WD=0.5, TTT_LORA_RANK=80, PHASED_TTT_PREFIX_DOCS=2500, BOS-fix SmearGate). The hypothesis was that #1855's recipe + a larger vocab via sparse embed wins.

Config	Pre-quant val_bpb	brotli (MB)	Sidecar TTT BPB
SP12k+CO sparse-FP16 + #1855 recipe + BOS-fix, SEED=42	1.3264	15.04	1.32092

Pre-quant looks normal. but TTT eval plateaus at rb≈1.32 instead of dropping to ~1.10 the way our default recipe does on the same sparse-embed model. that's +0.260 BPB worse than the shipped non-sparse rank=56 recipe.

Likely interactions (not isolated): TTT_LORA_RANK=80 × sparse-embed gradient dynamics, or WARMDOWN_FRAC=0.85 × sparse-EMA. recipe stacks don't compose blindly. each one is a small attractor in hparam space. shelved.

Mixed-precision embedding attempts (failed)

Factorized embedding (ALBERT V × r + r × d)

tok_emb ≈ E_v · E_r with rank r ≪ d:

Config	Rank	INT bits	TTT BPB	Brotli (MB)
SP32k fact r=64 INT8	64	8	1.15067	15.80
SP32k fact r=128 INT8	128	8	1.10267	17.79 (over)
SP32k fact r=256+ INT8	256	8	—	won't fit at INT8

The rank you need at d=512 is too high to fit budget. ALBERT was designed for BERT (d=768, vocab 30k). it doesn't transfer to small d.

Aggressive embed quantization to fit a larger vocab

Vocab	Embed bits	TTT BPB	Brotli (MB)
SP32k INT7	7	1.05076	22.52 (over by 6.52)
SP32k INT5	5	1.07930 (+0.012)	18.49 (over)
SP32k INT4	4	1.18759 (+0.120 collapse)	16.88 (close)
SP32k INT3	3	1.79203 (+0.724 collapse)	14.72 (fits)

INT ≤ 5 on the embed degrades TTT much more than the vocab gain at SP32k recovers. the quantization Pareto floor for the embed at our scale is INT7.

Frequency-aware mixed-bit embed (per-row bits by token frequency)

The idea: high-frequency tokens get more bits, tail rows get fewer. staged in specs/batch125/run_atlas.py using the BPB damage atlas to allocate bits per row.

• Tested layouts: top-1024 INT8 + rest INT5; top-2048 INT8 + rest INT4; uniform INT5 baseline.
• Best frequency-aware config beat uniform INT5 by ~0.005 BPB at SP32k. but uniform INT5 was already +0.012 over INT7, so net ~0.007 above INT7 reference. not enough.

Sparse + low-rank residual (LSR) embeddings

tok_emb = sparse_topk + low_rank_residual. the sparse path captures structured info, the residual covers the rest with a low-rank approximation. implementation in specs/batch125/run_lsr.py.

• 90 % sparse + r=64 residual: brotli 15.7 MB (fits), TTT BPB +0.030 vs no-residual sparse. residual didn't help.
• Hypothesis (unconfirmed): the residual sees the gradient through the masked path, so it doesn't learn a useful complement to the sparse pattern.

STE-trained mixed-bit embed (planned but blocked)

The conceptually right answer: train with STE forward = "round to row's bit width" so the model adapts to the exact bit scheme. implemented but training was unstable. STE forward produces gradient-mismatched updates that diverge under Muon. marked pending and not retried before deadline.

Vocab sweep (SP8k → SP32k, with corresponding INT-bits)

For reference (full-train, full TTT, single-seed):

Vocab	Embed bits	TTT BPB	Brotli (MB)	Fits
SP8192 + CO (locked best)	INT7	1.06755	15.91	✓
SP12k + CO	INT7	1.06024	18.10	✗
SP16k + CO	INT7	1.06024	18.10	✗
SP24k + CO	INT6	1.05828	18.75	✗
SP24k + CO	INT7	1.05338	20.36	✗
SP32k + CO	INT7	1.05076	22.52	✗

Doubling vocab gives roughly −0.008 to −0.01 BPB at TTT. not yet saturating at SP32k. Compression eats the vocab gain at this artifact budget. the "if compression were free" floor is the SP32k INT7 number 1.05076, which I never figured out how to ship.

CaseOps tokenizer ("lossless caps"): biggest single delta of the sprint

fineweb_8192_bpe_lossless_caps_caseops_v1_reserved.model. reserves operator tokens (U+E001–U+E004) for case transformations (TitleCase / UPPER / lowercase / single-cap-letter), so vocab pieces only need to encode case-insensitive surface forms.

Stage	SP8192 raw	CaseOps SP8192	Δ
pre-quant	1.06983	1.06744	−0.00239
post-quant	1.07887	1.07714	−0.00173
TTT	1.06755	1.06397	−0.00358

About 9.9 % of val tokens are case markers. CaseOps amortizes case info into a shared marker, freeing vocab budget for content patterns. all subsequent SP8k results in this PR are on CaseOps base, including the shipped recipe.

Honest byte counting fix: PUE markers (U+E001–U+E004) get base_bytes_lut = 0 because CaseOps post-decode strips these markers. without the fix the byte denominator inflates ~9 % and BPB undercount by ~0.08. ZERO_PUE_MARKERS=1 ships in train_gpt.py.

LQER capacity sweep (low-rank quant error recovery)

The best post-hoc compensation method I found for INT6 GPTQ residual error. sweep at b115 base + DISABLE_EVAL_COMPILE + TTT_FIXED_SEQ_COMPILE:

top_k	rank	post-quant BPB	TTT BPB	Δ vs baseline (top3 r4)
3	4 (baseline)	1.07965	1.06845	—
4	4	1.07936	1.06808	−0.00037
3	6	1.07928	1.06800	−0.00045
3	8	1.07955	1.06834	−0.00012
2	8	1.07939	1.06799	−0.00046
4	6	1.07887	1.06758	−0.00088 ⭐

top_k=4 rank=6 (asym, group=64) is the sweet spot. more tensors (top_k 3→4) helps as long as rank stays moderate. increasing rank past 6 overfits the LQER residual to calibration. bytes overhead +40-50 KB. this is what this PR ships.

Quant Pareto floor at brotli ≤ 16 MB cap (negative findings)

After a lot of sweeping in specs/batch124/run_atlas.py, the post-quant pre-TTT BPB floor at the cap is ~1.11450 (b172 SP8k+CO baseline). none of these levers moved the shippable Pareto floor by more than ~0.0005 BPB:

Lever	Effect	Notes
AWQ-style activation-aware scaling	+0.016 BPB at α=0.5; explodes at α=1.0	incompatible with per-row absmax GPTQ. non-salient cols get 1/s_col error amplified on dequant.
Damage-aware per-tensor bit allocation (BPB damage atlas + greedy)	atlas predictions accurate to 1e-3, but raw bit-bytes ≠ brotli bytes (entropy coder recovers savings non-linearly).	even at favourable raw budgets, brotli often grew.
Outlier extraction (top-K by W²·H_diag, FP16)	−0.0005 BPB / +145 KB brotli at 0.1 % outliers	redundant with LQER. both target the residual, they don't compose.
INT8 embed (vs INT7)	−0.002 BPB / +500 KB brotli	would need ~300 KB compensation elsewhere.

Real progress requires training-side levers, not quant tricks. STE-based mixed-bit embed, quant-grid-aware Muon, vocab/tokenizer changes (CaseOps wins ~10×).

QAT noise scale (training-side quant friendliness)

Quantization-aware training during warmdown by adding noise to weights:

QAT_NOISE_SCALE	Pre-quant BPB	Post-quant gap	Result
0.00 (off)	1.506	0.005	baseline
0.05	≈ 1.506	0.005	no effect
0.10	≈ 1.506	0.005	no effect
0.20	1.506	−0.001	NEGATIVE gap, post-quant better than pre-quant
0.50	1.631	0.005	catastrophic pre-quant cost

Sweet spot at 0.20. didn't ship in this PR because it doesn't compose with #1855's WARMDOWN_FRAC=0.85 recipe at our wallclock budget.

GPTQ block size

block_size	Quant gap	Notes
64	0.050 (10× worse)	never use < 128
128 (default)	0.005	optimal
256	0.007	slightly worse

Default 128 is correct. don't change.

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Per-group lrzip artifact

Composition follows #1855 exactly: per-group bucketing + L1 sim-sort on hot 2D groups + lrzip ZPAQ on each group blob.

Component	Bytes
Per-group lrzip quant blob	15,920,473
Pyminified train_gpt.py wrapper	33,270
Total	15,953,743
Cap	16,000,000
Margin	46,257 (under cap)

Roundtrip verified lossless: 275 quant tensors decompress byte-exact via Docker amd64 lrzip 0.651 (scripts/pergroup_lrzip_recompress.py --roundtrip).

Eval host needs apt install lrzip (same as #1855).

Test plan

• TTT_LORA_RANK ablation at SEED=42 (5 ranks): 48 / 56 / 64 / 80 / 96.
• Per-group lrzip artifact roundtrip verified lossless (275 tensors, byte-exact).
• Total submission ≤ 16 MB (15,953,743 / 16,000,000 = 99.71 %).
• H100 cross-hardware re-run matches MI250X result (596 s train + 599 s eval, both under 600 s lane caps; logs lost to pod crash).
• SEED=0 + SEED=1234 at rank=56: running on RunPod 8×H100, will append.

Files changed

• records/submission_2026_04_29_b180_tlr56_SUB106/ (new)
  • final_model.int6.ptz: per-group lrzip quant blob (15,920,473 B)
  • train_gpt.py: recipe. behavioural diff vs Record: SP8192 + LQER + Sparse Attn Gate + BOS-Fixed SmearGate + 9-Hparam Greedy Stack — val_bpb 1.06108 (3-seed mean) #1855 is QK_GAIN_INIT=6.0 and TTT_LORA_RANK=56. source-level diff is bigger because this is our ROCm-ported lineage with FA fallback / inductor shims, but the H100 path is structurally equivalent.
  • submission.json: metadata (val_bpb 1.05997, ablation tables, artifact byte breakdown)
  • lossless_caps.py, prepare_caseops_data.py: CaseOps preprocessing (unchanged from Record: SP8192 + LQER + Sparse Attn Gate + BOS-Fixed SmearGate + 9-Hparam Greedy Stack — val_bpb 1.06108 (3-seed mean) #1855 lineage)
  • fineweb_8192_bpe_lossless_caps_caseops_v1_reserved.model: tokenizer
  • lumi_train_ttt_17928157.log: combined MI250X training + TTT eval log (rank=56 SEED=42, single SLURM job, ~37 KB)

[vimeto]

Closing this PR — the multi-seed verification work it promised in the test plan has been consolidated into PR #2157 with H100-side reference logs.

PR #2157 continues the b180-tlr56 lineage but adds AWQ-lite top_k=3 + LQER 60k on top, since those levers became available in the meantime (PR #1908 lineage). The single-seed headline there (SEED=0, val_bpb 1.06043) is a continuation of the same direction this PR was exploring — the SEED=0 + SEED=1234 logs originally promised here are now in PR #2157 with the slightly evolved recipe.

Closing here for queue cleanliness; please review PR #2157 instead.