What is the degradation in GQA benchmark scores for LLaVA-1.5 when applying activation-aware weight

Abstract

Abstract: Large language models (LLMs) demand substantial computational and memory resources, creating deployment challenges. Quantization-aware training (QAT) addresses these challenges by reducing model precision while maintaining performance. However, the scaling behavior of QAT, especially at 4-bit precision (W4A4), is not well understood. Existing QAT scaling laws often ignore key factors such as the number of training tokens and quantization granularity, which limits their applicability. This paper proposes a unified scaling law for QAT that models quantization error as a function of model size, t

Research Question

What is the degradation in GQA benchmark scores for LLaVA-1.5 when applying activation-aware weight quantization versus standard post-training quantization?

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