Hybrid SWA Efficiency and Improvements with MiMo v2.5

MiMo v2.5 combines Stochastic Weight Averaging (SWA) with dynamic pruning and quantization to achieve exceptional inference efficiency. This architecture reduces model size by 60% while maintaining 98% of original accuracy through three core innovations: adaptive weight averaging, latency-aware pruning, and hybrid quantization.

The optimization in MiMo v2.5 offers advantages such as a 3x reduction in inference latency on mobile GPUs and 45% lower memory usage compared to standard SWA. Hybrid SWA presents a suitable solution for real-time applications while not recommended for applications requiring full precision outputs.