RUQuant uses block-wise composite orthogonal matrices from Householder reflections and Givens rotations plus a fine-tuned global reflection to achieve 99.8% full-precision accuracy at W6A6 and 97% at W4A4 for 13B LLMs in about one minute.
Mixdq: Memory-efficient few-step text-to-image diffusion models with metric-decoupled mixed precision quantization
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LoRaQ enables fully sub-16-bit quantized diffusion models by optimizing low-rank error compensation in a data-free way, outperforming prior methods at equal memory cost on Pixart-Σ and SANA while supporting mixed low-precision branches.
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RUQuant: Towards Refining Uniform Quantization for Large Language Models
RUQuant uses block-wise composite orthogonal matrices from Householder reflections and Givens rotations plus a fine-tuned global reflection to achieve 99.8% full-precision accuracy at W6A6 and 97% at W4A4 for 13B LLMs in about one minute.
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LoRaQ: Optimized Low Rank Approximation for 4-bit Quantization
LoRaQ enables fully sub-16-bit quantized diffusion models by optimizing low-rank error compensation in a data-free way, outperforming prior methods at equal memory cost on Pixart-Σ and SANA while supporting mixed low-precision branches.