Developer Implements GPTQ from Scratch, Achieves 1.1% Perplexity Degradation

A developer implemented GPTQ quantization from scratch on a nanoGPT model, achieving only 1.1% perplexity degradation across 61 quantized layers. The approach uses second-order optimization to distribute quantization error across remaining weights. The implementation demonstrates how to reduce model size while preserving accuracy using calibration data and Hessian approximation.