This model is a compressed version of Qwen/Qwen3-Coder-Next.
It is obtained by reducing the number of experts in each MoE layer from 512 to 384.
This reduction is achieved by the REAM method described in https://bknyaz.github.io/blog/2026/moe/.
Compared to other models obtained in this collection, more code data is used in the calibration data during pruning/merging
to better preserve original's model coding abilities. Specifically, the ratio between c4, math and coding data (see https://bknyaz.github.io/blog/2026/moe/) is 0.0, 0.7, 0.3.
The calibration data used here is the same as in our Qwen3-Coder-Next-REAP.
Compared to other REAM models, here we used C=32 (number of experts in groups) instead of C=16, which we found to work better.
The compressed model has 60B params (120GB) instead of 80B (160GB) of the original model,
reducing storage and GPU memory requirements by roughly 25%. At the same time,
the model retains 100% (or very close) of the original model's performance on a variety of benchmarks (see Results section below).
Additional efficiency optimization (e.g., quantization) can be added similarly to the original model.
