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Recently, Alibaba Cloud officially announced that BYD successfully integrated the Alibaba Qwen large model. This means that in the future within BYD's smart cockpit, users will be able to easily achieve online services such as ticket booking and meal ordering with just one sentence. This new technology will greatly enhance the driving experience and provide more convenient services. This innovation is called Mobile-Agent and adopts a full visual solution. It relies on BYD's open cockpit application ecosystem and combines the visual recognition and reasoning capabilities of Alibaba Qwen-VL to build a multi-modal system.

According to official data, Alibaba's Qwen3 large model has exceeded 12.5 million downloads globally within just one month since its open-source launch. It has drawn significant attention on major AI open-source platforms such as Hugging Face, ModelScope Community, and Ollama. Currently, Qwen3 offers four model versions with sizes of 0.6B, 8B, 30B, and 32B respectively, each exceeding one million downloads on the above-mentioned platforms, demonstrating strong global developer appeal. Particularly on Hugging Face platform, millions of users

The Alibaba Qwen team recently published a paper titled 'Lessons Learned from the Development of Process Reward Models in Mathematical Reasoning' and introduced two new models in the Qwen2.5-Math-PRM series, featuring 7B and 72B parameters respectively. These models break through the limitations of the existing PRM framework in mathematical reasoning, significantly improving the accuracy and generalization ability of reasoning models through innovative techniques. Mathematical reasoning has long been a major challenge for large language models (LLMs), especially regarding errors in intermediate reasoning steps.

Recently, the HELM MMLU released its latest results from Stanford University's large model evaluation leaderboard. Percy Liang, director of the Stanford Center for Research on Foundation Models, stated that Alibaba's Tongyi Qianwen Qwen2-72B model has surpassed Llama3-70B in ranking, becoming the best-performing open-source large model.