English(EN) yeasah/Qwen3.8-27B-exl3 quants - lowest KLD of 0.0002

yeasah Qwen3.8-27B 模型量化实现最低KLD

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-29 03:33

yeasah 开发的 Qwen3.8-27B 模型的新量化版本实现了 0.0002 的平均 KLD（Kullback-Leibler 散度）。这代表了比包括 Unsloth 和 Minachist 在内的其他测试量化更低的散度。yeasah 量化版本还需要更少的 VRAM，权重占用 18.8 GiB，而其他版本需要更多。 AI

影响为本地 LLM 部署提供了更高的效率和性能。

排序理由发布了具有基准性能指标的量化模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 r/LocalLLaMA 阅读 →

模型发布

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 15 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 发布了具有基准性能指标的量化模型。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

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r/LocalLLaMA TIER_1 English(EN) · /u/suprjami · 2026-10-29 03:33

yeasah/Qwen3.8-27B-exl3 量化 - 最低 KLD 0.0002

<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/…

本文如何被排名

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yeasah/Qwen3.8-27B-exl3 量化 - 最低 KLD 0.0002

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