GPTQ + Short TTT — val_bpb 1.1207 (seed 1337)#577

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newjordan wants to merge 119 commits intoopenai:mainfrom

newjordan:submission/gptq-short-ttt-1.1207

Commits on Mar 23, 2026

docs: fractal transformer research plan — weight sharing + gravity + AttnRes
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results: first local ladder — fractal 3x3 beats baseline by 7.1% BPB, gravity needs more steps
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Add exact clone of PR #254 — best pending SOTA (1.1313 BPB)

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Add XSA last 3 layers to #254 SOTA clone

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Fix XSA GQA broadcast bug — expand KV heads before manual attention

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Add 3 SOTA improvement experiments: MTP, SwiGLU, Vocab1536

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Add FarnsworthEngine v2: full improvement stack on SOTA254 base

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Add FA3→FA2→SDPA fallback chain for pod restart resilience

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Revert FA3 fallback chain — was unauthorized code change to baseline + untested v2

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Fix FA3 NaN: cast qkv to bf16 before FA3 call, disable dynamo DDP opt

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Add 2-seed validation scripts for exp A/B/C

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Log exp A/B results: both behind baseline, zlib fallback bug found

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Fix XSA NaN: position 0 has no valid targets when self-mask + causal combine

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Disable XSA in ttt_only run — manual attention too slow vs FA3

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Add run_v2_ttt_noXSA.sh — TTT v2 + temp scaling, all FA3, max speed

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Restore XSA_LAST_N=3 in run_v2_ttt_only.sh (keep existing test intact)

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Log v2 TTT-only + XSA=3 result: 1.1982 BPB (worse than 1.1301 baseline)

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Strip verbose logging from v2 train loop — match baseline format

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Log v2 noXSA result: 1.1538/1.1315 BPB — TTT v2 hurt, no edge over baseline

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Log exp_a/b/c results: all worse than 1.1301 baseline, exp_c never ran

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Add exp D: TTT 8 epochs + stride 32 (eval-only improvement)

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Add SAM (Sharpness-Aware Minimization) option for TTT

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Add baseline reproduction script — verify 1.1303 on current FA3 build

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Add SAM to baseline TTT — test sharpness-aware adaptation on proven code

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Log exp D result: 1.1295 BPB — new best (-0.0008 vs baseline)

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Log exp D seed 42: 1.1307 BPB — confirms improvement (mean 1.1301)

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Add exp_d SAM variant — TTT 8ep + stride 32 + sharpness-aware TTT

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Log exp D seed 7: 1.1313 BPB but 16.18 MB — over size limit

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Add Partial RoPE + LN Scale (from PR #315) to sota254 + run_sam

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Add exp_d/run_sam_clean.sh — pure SAM A/B test, no other changes

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Log exp D seeds 7+137: both over size limit

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Add Sponge Bath experiment: TTT 8ep + stride 32 eval-only improvement
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Add PR#315 clone with TTT 8ep + run script
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Log D+SAM+PR315tricks: 1.1274 BPB new best; add SAM to pr315 run script
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Log PR315+TTT results: 1.1240 BPB (invalidated — TTT now banned)
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Add PR374 enchilada experiment: 12L/2KV/2.75xMLP + train@1024 + EMA

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Add RunPod 8xH100 setup guide — every gotcha we've hit 3 times

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Add fractal cadence experiment: F/N alternation pattern

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Add PR374-safe: EMA + earlier QAT + longer warmdown only, shape unchanged

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Add PR374-depth: 12L/4KV/2.625xMLP (same params, +1 layer) + EMA + QAT + warmdown

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Add minified train_gpt (30KB vs 74KB) with EMA+SWA edge

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Fix log0 keyword arg bug in minified script

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Fix Block kwarg: layer_idx -> li in minified script

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Fix a.te -> a.tie_embeddings in minified script

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Add pr374_submit: trimmed winner for submission

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Remove duplicate stride=64 eval from submit script

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Revert "Remove duplicate stride=64 eval from submit script"
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Add fractal cadence H100 script: 4 unique × 3 loops + ortho positions

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Add fractal cadence long run (1.6575 BPB @ 3929 steps) + H100 script

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Add pr374_slim: comment-stripped pr374_safe (67KB vs 74KB, -6.4KB)

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Add fractal cadence auto-research sweep runner

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Fix FA3 head_dim limit: 12 heads / 6 kv_heads for dim=768 (head_dim=64)

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Add pr374_ttt: PR374 + EMA + TTT(20ep,SAM) — rock the house

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Fix early QAT trigger: skip warmdown/QAT for first 100 steps

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Optimize fractal: tuned H100 defaults + focused sweep grid

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Add TTT eval: test-time training on already-graded tokens

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Log depth + TTT results: 1.1223 BPB (12L+TTT20ep+SAM)

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Save pr374_throne: our #1 non-TTT record holder (1.1243 BPB)

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Add autonomous overnight fractal optimizer (Karpathy auto-research)

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Rewrite autoresearch: Qwen-guided fractal optimizer via Ollama

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Add 3 leapfrog variants based on PR#414 SOTA (1.1233 BPB)

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Add pod setup script for leapfrog variants (v1/v2/v3)

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Fix setup_pod.sh: apply FA3 build fixes from RUNPOD_SETUP.md

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Update fractal H100 with overnight Qwen findings

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Fix v1 TTT burst: apply EMA first, then QAT-aware sharpening

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Add inner-TTT to fractal eval: recursive weight improvement per loop

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v1 combo: burst+QAT before EMA + 15 GPTQ percentiles

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Isolate TTT to fractal layers only: blocks + loop_pos + skip_weights

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Add TTT drift gate: snap block weights back toward trained originals

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Add v4: burst + distill + train_seq_len=1024 for more steps

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Add SOTA autoresearch: Qwen-guided edge finding for 1.1233 record

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Fix TTT graph bug + upgrade to 3 blocks x 896d (23M params, ~14MB)

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Save leapfrog experiment results: 6 variants tested, v1 wins at 1.12319

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Fractal v2: dim=960 (25.7M, ~15.8MB), Muon LR back to 0.025

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Disable TTT in v2 (backward graph bug), focus on bigger model

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Add v5: QAT percentile fix + TrigramHash + EMA-SWA blend

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Fix TTT graph bug: fresh vc + detached inputs per loop

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Two v2 scripts: with TTT and without, for A/B comparison

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Fractal v3: MLP 3.0->3.3 fills 16MB budget (27.4M params, ~15.2MB)

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Fractal v4: 1.5x batch (1.18M tokens/step) to use spare VRAM

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Log The Frugendorff: fractal cadence baseline at 1.2113 BPB

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Add v6: fractal 6L×2 loops (12 effective) + 480d/16H/4xMLP

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Frugendorff v5: TTT warmup-then-freeze to capture 1.19 peak

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Log all Frugendorff results: v3 TTT peak 1.1901, v4 batch 1.2186

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Fix v6: 512d/16H/8KV (head_dim=32, FA3 requires multiple of 8)

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Log v5/v6 results + add fractal sweep script

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Frugendorff 8-hour single GPU longrun

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Complete Frugendorff documentation: all runs, findings, architecture analysis

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Add v7: legal score-first TTT eval (PR #461 recipe)

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Add v8: mutual learning — flat GPT + fractal GPT co-training

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v7: add TTT early stopping — train only first 60 chunks

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Update v6 to sweep winner: 4×3/512d/8H/4KV/4xMLP

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Log v7 TTT results: early stop at 60 chunks = 1.12312 (best)

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The Frugendorff Squared: 6L x 2 loops, dim=640, MLP 4x, 28.5M params

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Log higher LR (0.030) + MTP results — both worse

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The Frugendorff Squared: 1.1478 BPB — 0.025 from world lead

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Stack distillation onto Frugendorff Squared

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Prep Frugendorff Squared PR submission (draft — needs image)

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Add train script to Frugendorff submission folder

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TTT stabilization: EMA scoring + fixed cosine LR + embed freeze

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Add GPTQ quantization: Hessian-aware error compensation for int6

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Stack low-hanging fruit: fix GPTQ, earlier QAT, matched clipping

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Add post-quant training burst: repair quant damage before eval

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Remove post-quant burst — illegal (uses training data during eval)

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Add selective int8 for sensitive layers (GPTQ-int8 upgrade path)

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Frugendorff V3: v7 full stack + fractal loops (GPTQ + TTT + PQB)

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Add AdamW TTT option — PR #462 shows 5x better TTT gain vs SGD

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Frugendorff V3: SwiGLU + AdamW TTT + 8K bigram (PR#462 techniques)

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Fix DDP unused params: add find_unused_parameters=True

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SwiGLU + GPTQ: PR #462 architecture with our quant improvements

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Add OptRot: Hadamard rotation before GPTQ quantization

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Frugendorff V3: Star-ReLU SwiGLU + VRL + MLP 4.0 (fits 16MB)

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Log session results: 1.0763 SwiGLU (size problem), 1.1215 v7 (submitted)

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Frugendorff V4: GEPA-matched config for 16MB compression

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Frugendorff V4 A/B: 5x2 and 4x2 variants for H100 calibration

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Add short TTT experiment: no-EMA, 50 chunks, SGD

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Add XSA-all(11) + short TTT run script

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Record: v7 GPTQ + Short TTT — val_bpb 1.1207 (seed 1337)

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Frugendorff compression shim on SwiGLU SOTA (1.0763 model)

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