Recently, the demand for small, efficient reasoning models to support real-world applications has driven the exploration of knowledge distillation approaches that balance reasoning performance and inference speed. In this paper, we further extend the DistilQwen model collection, initialized from Qwen models, by introducing four model series specifically designed to meet industrial needs. The distilled model collection includes: (1) slow-thinking models, optimized for reasoning tasks requiring high accuracy; (2) two series of adaptive-thinking models, which dynamically adjust their reasoning strategies based on input tasks to maximize efficiency across varied scenarios; and (3) distilled reward models for adaptive thinking, which support further reinforcement learning of reasoning models utilizing distilled knowledge. Comprehensive evaluations across several benchmarks demonstrate the inference efficiency and strong reasoning performance of reasoning models, together with the usefulness of distilled reward models. We further show how these models benefit industry practitioners by providing scalable model training and inference functionalities in an AI platform.