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papersSEP 11 04:00 UTC

RDQ quantization method targets accuracy loss below 4-bit in LLMs

A research paper proposes Residual Distribution Quantization, a post-training quantization approach for large language models. The authors attribute the sharp accuracy drop seen below 4-bit precision to distributional drift in the residual stream, where quantization error introduced at each transformer layer builds up in the shared representation. Their method aims to correct this accumulated error to preserve model quality at lower bit widths.

Residual Distribution Quantization distributional drift large-language-models low-bit quantization post-training quantization residual-stream

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGRDQ: Residual Distribution Quantization for Large Language Models ↗SEP 11 04:00 UTC