SpectrumQuant: Spectrum Alignment for LLM Quantization
Orthogonal transforms have emerged as an effective tool for low-bit quantization of large language models (LLMs), as they reshape activation distributions without changing the underlying model function. Existing approaches typically rely either on fixed data-agnostic transforms, such as Hadamard rotations, or on calibr...
Dwaipayan Haldar, I. Patra, Azhar Yousuf et al.
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