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English(EN) Gemma 4 QAT could be improved further by Google aligning the QAT model to modern q4_k instead of q4_0

Google Gemma 4 QAT 在用户基准测试中表现不一

Reddit 的 r/LocalLLaMA 社区的一位用户观察到,Google 的 Gemma 4 QAT 模型虽然在减少内存消耗方面有效,但在与其他量化方法(如 q4_k_l)相比时,其整体保真度可能并未得到全面提升。该用户进行的内部基准测试,包括需要细微理解和记忆的代码生成和创意写作任务,表明 q4_k_l 量化有时表现更好。这表明 Gemma 4 的量化过程的进一步优化,特别是将其与现代 q4_k 标准对齐,可以提高其整体性能。 AI

影响 通过优化的量化技术,有可能提高模型性能和效率。

排序理由 用户对现有模型的量化方法进行的生成分析和基准测试。[lever_c_demoted from research: ic=1 ai=1.0]

在 r/LocalLLaMA 阅读 →

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

Google Gemma 4 QAT 在用户基准测试中表现不一

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用户对现有模型的量化方法进行的生成分析和基准测试。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    Google Gemma 4 QAT可通过将QAT模型与现代q4_k而非q4_0对齐来进一步改进

    <!-- SC_OFF --><div class="md"><p>Hello,</p> <p>For the past few days I have been benchmarking Gemma 4 26b QAT UD Q4_K_XL extensively versus Bartowski's Q4_K_L.</p> <p>While QAT is certainly very effective and reducing memory consumption versus the highest q4 quant from him, I al…