研究人员开发了 ExTernD，一种用于大型语言模型 (LLM) 的新型训练后量化技术。该方法将 LLM 权重矩阵分解为三元因子和一个对角缩放向量，从而扩展了内部秩以纠正量化误差。ExTernD 在 Gemma-4-E2B 和 Qwen3.5-4B 等模型上，展示了与 Q4_K 和 Q5_K 等更高比特宽度量化方法相当的精度，同时保持了高效的内存和计算使用。 AI
影响 这项研究通过在减少内存和计算需求的同时实现高精度，可能能够更有效地部署 LLM。
排序理由 该集群包含一篇详细介绍 LLM 量化新方法的论文。
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arXiv:2607.20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces. Even nominally open-ended tasks can often be solved by retr…
arXiv:2607.19438v1 Announce Type: cross Abstract: Apple's M5 generation introduces a redesigned GPU architecture in which every core carries a dedicated Neural Accelerator: on-die matrix units exposed through the Metal~4 tensor API. We show that BaseRT, our native Metal inference…
arXiv:2607.17733v1 Announce Type: cross Abstract: 4-bit quantization enables efficient LLM inference, but suffers from significant accuracy degradation due to outliers. Prior work addresses this problem via data rotation or mixed-precision integer quantization, but often relies o…
arXiv:2607.13511v1 Announce Type: cross Abstract: We introduce ExTernD (Expanded-rank Ternary Decomposition), a post-training factorization of each LLM weight matrix $A \in \mathbb{R}^{m \times n}$ into $A \approx B \mathrm{diag}(D) C$ with ternary factors $B \in \{-1,0,+1\}^{m \…
We introduce ExTernD (Expanded-rank Ternary Decomposition), a post-training factorization of each LLM weight matrix $A \in \mathbb{R}^{m \times n}$ into $A \approx B \mathrm{diag}(D) C$ with ternary factors $B \in \{-1,0,+1\}^{m \times k}$, $C \in \{-1,0,+1\}^{k \times n}$ and a …
We introduce ExTernD (Expanded-rank Ternary Decomposition), a post-training factorization of each LLM weight matrix $A \in \mathbb{R}^{m \times n}$ into $A \approx B \mathrm{diag}(D) C$ with ternary factors $B \in \{-1,0,+1\}^{m \times k}$, $C \in \{-1,0,+1\}^{k \times n}$ and a …
<!-- SC_OFF --><div class="md"><p><a href="https://arxiv.org/pdf/2607.13511">https://arxiv.org/pdf/2607.13511</a></p> <p>the core idea is, we cannot have ternary PTQ with fixed matrix size, trying to do that is dead end. so i tried decomposing the matrix to 2 ternary matrices and…