QMIX, a very classical multi-agent reinforcement learning (MARL) algorithm, is often considered to be a weak performance baseline due to its representation capability limitations. However, we found that by improving the implementation techniques of QMIX we can enable it to achieve state-of-the-art under the StarCraft Multi-Agent Challenge (SMAC). Further, we found that the monotonicity constraint of QMIX is a key factor for its superior performance. We have open-sourced the code at https://github.com/xxxx/xxxx (Anonymous) for researchers to evaluate the effects of these proposed techniques. Our work has been widely used as a new QMIX baseline.