Qwen3.6 27B INT4 AutoRound — Code Calibrated (Best Recipe) A W4A16 (INT4 weight, FP16 activation) quantization of Qwen/Qwen3.6 27B , produced with Intel's AutoRound. Key difference from the standard AutoRound quant: This variant was calibrated on a normalized and sampled subset of nvidia/OpenCodeInstruct — a ~5 M sample, execution verified coding dataset — instead of the default general purpose pile corpus. Calibrating on domain specific data guides AutoRound's weight rounding optimization to minimize quantization error on the token distributions that matter most for code, improving accuracy on code generation, reasoning, and instruction following for programming tasks. The auto round best preset was used (1000 iterations, 512 calibration samples), which runs ~4–5× slower than the standard recipe but achieves the best possible INT4 accuracy. MTP (speculative decoding) and image/vision inputs work out of the box with no post processing required. TL;DR Base : Qwen3.6 27B (27B dense VLM) Quant : INT4 W4A16, group size 128, symmetric Tool : auto round best (1000 iters, 512 samples, torch.compile) Calibration dataset : nvidia/OpenCodeInstruct (coding domain) Size : ~18 GB (down from ~54…
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