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yeasah Qwen3.8-27B model quantization achieves lowest KLD

By PulseAugur Editorial · [1 sources] ·

A new quantization of the Qwen3.8-27B model, developed by yeasah, has achieved a KLD (Kullback-Leibler divergence) mean of 0.0002. This represents a lower divergence than other tested quantizations, including those from Unsloth and Minachist. The yeasah quantization also requires less VRAM, utilizing 18.8 GiB for weights compared to others that require more. AI

IMPACT Offers improved efficiency and performance for local LLM deployments.

RANK_REASON Release of a quantized model with benchmark performance metrics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

yeasah Qwen3.8-27B model quantization achieves lowest KLD

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13 / 100
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Release of a quantized model with benchmark performance metrics. [lever_c_demoted from research: ic=1 ai=1.0]
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model release
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/suprjami ·

    yeasah/Qwen3.8-27B-exl3 quants - lowest KLD of 0.0002

    <!-- SC_OFF --><div class="md"><p>Noticed this new EXL3 quant of 3.8-27B which achieves lowest KLD than anyone else and does it using only 18.8 GiB VRAM for weights. Pretty impressive numbers!</p> <p><a href="https://huggingface.co/yeasah/Qwen3.8-27B-exl3">https://huggingface.co/…

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