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ENOSYS/Kimi-Linear-48B-A3B-Instruct-abliterated-1000-v1-GGUF

sourceHugging Facemitupdated 5mo agoView on Hugging Face
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Experimental global target bits‑per‑weight quantization of huihui-ai/Huihui-Kimi-Linear-48B-A3B-Instruct-abliterated

  • —Using non-standard (forked) LLaMA C++ branch for quantization.
  • —Using a CLI tool to build KLD evaluation and imatrix calibration datasets for GGUF models, sourced from eaddario/imatrix-calibration.
  • —Using dataset sources: tools, math, code, texten, textru.
  • —Using dataset chunks: 1000.
  • —Tensors quantinization F16 instead of BF16, Nvidia Pascal architecture friendly like P100.
  • —Small set of patches added.

Many thanks to Ed Addario for an impressive job.

Quantization comparison

BPW/TGSSIZE (MiB)PPL correlationPPL mean ratioΔPPLMean KLDMedian KLDMaximum KLD99.9% KLDMean ΔpRMS Δp
3.0001757497.54%1.099746 ± 0.0011980.703413 ± 0.0089590.122940 ± 0.0004260.0667149.2941352.637652-2.015 ± 0.019 %10.272 ± 0.039 %
3.2501903898.25%1.064443 ± 0.0009690.454454 ± 0.0069780.089130 ± 0.0003080.05062410.3238971.992209-1.732 ± 0.016 %8.814 ± 0.035 %
3.5002050298.69%1.042289 ± 0.0008210.298224 ± 0.0058350.064519 ± 0.0002650.0325508.3117511.765223-1.217 ± 0.014 %7.481 ± 0.033 %
3.7502196699.04%1.030411 ± 0.0006970.214459 ± 0.0049890.047054 ± 0.0002010.0228468.3463861.258301-0.654 ± 0.012 %6.340 ± 0.031 %
4.0002343099.15%1.028018 ± 0.0006530.197583 ± 0.0046680.042084 ± 0.0001780.0208437.0314581.095171-0.639 ± 0.011 %5.992 ± 0.029 %
4.2502489499.25%1.021574 ± 0.0006100.152141 ± 0.0043270.036486 ± 0.0001580.0182976.7637601.009924-0.568 ± 0.011 %5.619 ± 0.028 %
4.5002635899.50%1.012304 ± 0.0004950.086766 ± 0.0035060.024419 ± 0.0001050.0120785.7421870.643191-0.204 ± 0.009 %4.565 ± 0.023 %
4.7502782299.61%1.007470 ± 0.0004340.052677 ± 0.0030630.019040 ± 0.0000860.0094034.8605350.504090-0.200 ± 0.008 %4.035 ± 0.020 %
5.0002928699.67%1.006065 ± 0.0003970.042769 ± 0.0027990.015935 ± 0.0000730.0080213.6588250.425957-0.201 ± 0.007 %3.731 ± 0.020 %