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Model Quantization is a technique used to reduce the size and computational requirements of machine learning models by representing weights and activations with lower precision. This is particularly useful for deploying models on resource-constrained devices, such as mobile phones and embedded systems.
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 STaMP: Sequence Transformation and Mixed Precision for Low-Precision Activation Quantization Marco Federici Riccardo Del Chiaro Boris van Breugel Paul Whatmough Markus Nagel | |||
 LoRAQuant: Mixed-Precision Quantization of LoRA to Ultra-Low Bits Amir Reza Mirzaei Yuqiao Wen Yanshuai Cao Lili Mou | |||
 INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats Mengzhao Chen Meng Wu Hui Jin Zhihang Yuan Jing Liu ...Jin Ma Zeyue Xue Zhiheng Liu Xingyan Bin Ping Luo | |||
 Sharpness-Aware Data Generation for Zero-shot QuantizationInternational Conference on Machine Learning (ICML), 2025 | |||
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