English(EN) 8 uncensored Qwen 3.8 27B variants, one base, 167 GPU hours - Abliterlitics

Qwen 3.8-27B无审查变体对比，'orcarouter'领先

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-06 13:15

Abliterlitics发布了对Qwen 3.8-27B模型八个无审查变体以及基础模型的比较分析。该研究涉及167小时的GPU计算，并使用HarmBench在基准测试、KL散度和拒绝率上评估了模型。'orcarouter'变体以82.2%的攻击成功率成为表现最佳者，而'apostate'则以接近同等能力和最低KL散度提供了最佳价值。相反，'obliteratus'变体因显著的性能下降和思考循环而被标记为需要规避，基础模型显示出最低的合规性。 AI

影响提供了对无审查LLM变体性能和安全权衡的见解，引导用户选择更强大或更安全的选择。

排序理由对现有开源模型的多个变体进行分析和比较。[lever_c_demoted from research: ic=1 ai=1.0]

在 r/LocalLLaMA 阅读 →

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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: 10 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 对现有开源模型的多个变体进行分析和比较。[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。

报道来源 [1]

r/LocalLLaMA TIER_1 English(EN) · /u/nathandreamfast · 2026-09-06 13:15

8个未经审查的Qwen 3.8 27B变体，1个基础模型，167 GPU小时 - Abliterlitics

<div class="md"><p>This comparison was requested by a few people, and certainly we were all eager to see the final results. The comparison had taken 11 days and the GPU was crunching numbers for ~167 hours.</p> <p>We've been comparing different abliterated models f…

本文如何被排名

报道来源 [1]

8个未经审查的Qwen 3.8 27B变体，1个基础模型，167 GPU小时 - Abliterlitics

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