Quantization Loss

Quantization loss arises from representing the high-precision weights and activations of large language models (LLMs) and other deep neural networks with lower bit-widths, impacting model accuracy and efficiency. Current research focuses on mitigating this loss through techniques like developing loss-aware quantization grids, employing quantization-aware training, and exploring optimal quantization strategies tailored to different layers or model architectures (e.g., Vision Transformers, LLMs). Reducing quantization loss is crucial for deploying these computationally intensive models on resource-constrained devices, improving their accessibility and applicability across various domains.