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New MARR technique boosts low-bit quantization for LLMs and ViTs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new technique called Module-Adaptive Residual Reconstruction (MARR) to improve low-bit post-training quantization for large language models and vision transformers. MARR addresses limitations in existing methods by adaptively balancing error correction and bias across different model modules. This approach uses a module-specific scaling coefficient and a PID-based update strategy to refine coefficients, leading to significant performance gains, particularly at quantization levels of 4-bit or lower. AI

IMPACT Enhances efficiency of LLMs and ViTs by improving low-bit quantization techniques.

RANK_REASON Academic paper detailing a new method for model quantization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MARR technique boosts low-bit quantization for LLMs and ViTs

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Academic paper detailing a new method for model quantization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zhi Jin ·

    MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization

    Recently, residual reconstruction-based model quantization methods have achieved promising performance in low-bit post-training quantization (PTQ) by introducing cross-layer residuals to reduce error accumulated from previous layers.However, these residuals may also introduce add…

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