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arXiv cs.AI·

SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs

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In three linesSignRoundV2 is a post-training quantization framework for LLMs maintaining performance under extreme compression (2-4 bits). It combines adaptive mixed-precision strategy guided by gradients and lightweight stabilization techniques. Results show ~1% performance gap at 4.5 bits average in mixed MXFP, with substantial improvements in challenging 2-bit weight-only quantization.
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