Large Language Models, Artificial Intelligence, Data Science, Machine Learning, Editors Pick.
set of remaining non-quantized weights F to make up for the removal: where quant(w) is the weight rounding given by the quantization and Hꟳ is the Hessian. Using OBQ, we can quantize the easiest weight first and then adjust all remaining non-quantized weights to compensate for this precision loss.