Record: 11L GPTQ-lite + Int6 MLP3x (val_bpb=1.1257)#379
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dannywillowliu-uchi wants to merge 2 commits intoopenai:mainfrom
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Record: 11L GPTQ-lite + Int6 MLP3x (val_bpb=1.1257)#379dannywillowliu-uchi wants to merge 2 commits intoopenai:mainfrom
dannywillowliu-uchi wants to merge 2 commits intoopenai:mainfrom
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mrdavtan
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Mar 22, 2026
Tries 5 clip percentiles (0.9, 0.95, 0.99, 0.999, 0.99999) per row, keeps the one minimizing reconstruction MSE. Zero training cost. Default ON (GPTQ_LITE=1). Inspired by PR openai#379.
anthony-maio
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From arXiv:2603.09078. Projects out the self-value component from attention output, forcing the network to use contextual information. Applied via GQA-aware zero-alloc view reshape on last 4 of 11 layers. Both top unmerged submissions (PR openai#374 at 1.1246 and PR openai#379 at 1.1260) use XSA as a key technique. Full next-gen stack now includes: 11L, XSA, Partial RoPE 16/64, Late QAT STE, Tight SWA, GPTQ-lite, LN Scale, FA3, SmearGate, BigramHash, int6+zstd, Muon WD, OrthoInit. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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rarce
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original_model.md: - Discard depth recurrence (amplifies quant error 900×, throughput loss) - New direction: eval-time optimization stack (PPM-C + GPTQ-lite) - Document all our experiment results (v3, v4, v4_30m, ringgolf) - Add TTT/XSA interaction findings (PR openai#303: mutually exclusive) - Add PR openai#375 meta-insight (1ms overhead = 0.006 BPB) - 4-phase execution plan targeting PPM-C as original contribution review_pr_records_track_10min_16mb.md: - Add 2026-03-22 update with PRs openai#374, openai#379, openai#390, openai#375, openai#303, openai#363 - New SOTA at 1.1246 (PR openai#374: Tight SWA + VE128) - Document negative results from $500 compute spend (PR openai#375) - Unexplored opportunities: PPM-C, Neural Cache review_records_track_10min_16mb.md: - Add timestamp note (17 records, no changes) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
val_bpb: 1.1257 (sliding window, stride=64) | 8xH100 SXM, 600s
Built on PR #374's SOTA stack with GPTQ-lite: per-layer optimal clip percentile search during int6 quantization.
Novel: GPTQ-lite
Standard int6 quantization uses row-wise absolute max for clipping. GPTQ-lite searches 5 clip percentiles per weight matrix (100%, 99.9%, 99.5%, 99%, 98%) and selects the one minimizing reconstruction error. This reduces quantization degradation at zero training cost.
Architecture: 11L, XSA4, Tight SWA, Partial RoPE 16/64, LN Scale, Late QAT, Value Embedding, SmearGate, BigramHash, FA3, int6+zstd-22, WD=0.04.
Full source and experiment history: https://github.com/dannywillowliu-uchi/parameter-golf-entry