AI Research
ResCom: Activation Residual Compensation for Large Language Model Quantization
ResCom targets accuracy loss from activation rounding in low-bit quantization.
Low-bit weight--activation quantization reduces the memory and computation needed to deploy large language models, but activation rounding can still degrade accuracy after calibration.
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ArXiv CS.LG
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ArXiv CS.LG
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Low-bit quantization can reduce the memory and computation required to deploy large language models. The work focuses on activation rounding as a source of accuracy loss that remains even after calibration.
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