TORQ framework enhances LLM accuracy with MXFP4 quantization

Researchers have developed TORQ, a new framework for quantizing Large Language Models (LLMs) using the MXFP4 format. This method addresses accuracy degradation issues by analyzing and correcting imbalances in activation quantization. TORQ employs a two-level orthogonal rotation strategy to optimize the activation space, significantly improving LLM accuracy with 4-bit floating-point quantization. AI

影响 Improves LLM efficiency and accuracy by enabling better low-bit quantization, potentially reducing inference costs.

排序理由 The cluster contains a research paper detailing a new method for LLM quantization. [lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

• LLMs
• MXFP4
• Qwen3

• 论文
• 基础设施

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