Qwen AI introduced Qwen2.5-MAX: large MOE LLMs that carefully carefully studied a large amount of data, and trained after training after selecting the selected SFT and RLHF recipes

The field of artificial intelligence is developing rapidly, and more and more efforts have developed more effective and effective language models. However, extended these models are facing challenges, especially in terms of computing resources and training. Research communities are still exploring the best practice of expanding great models, whether they are the best practice of using dense or mixture (MOE) architecture. Until recently, many details about this process have not been widely shared, so it is difficult to improve and improve large -scale AI systems.

The purpose of Qwen AI is to further improve through Qwen2.5-MAX (a large MOE model, which has carefully considered more than 200 trillion token, and has been further improved by supervising the fine-tuning (SFT) and enhanced human feedback (RLHF). This method is fine -tuned to the model in order to better consistent with human expectations while maintaining zoom efficiency.

Technically, Qwen2.5-MAX uses a mixture of the Experts architecture to make it only activate a subset of its parameters during the reasoning process. This optimizes computing efficiency while maintaining performance. The extensive pre -processing phase provides a good foundation for knowledge, while SFT and RLHF improve the ability of the model to generate coherence and related response. These technologies help improve the inference of the model and the availability of various applications.

Qwen2.5-MAX has evaluated the leading models on MMLU-Pro, LiveCodebench, Livebench and Arena-Hard. The results showed that it was competitive and surpassed Deepseek V3 in tests such as Arena-Hard, Livebench, LiveCodebench and GPQA-Diamond. Its performance on MMLU-PRO is also very strong, highlighting its functions in knowledge retrieval, coding tasks, and broader AI applications.

In short, while maintaining efficiency and performance, Qwen2.5-MAX proposes a thoughtful scaling language model. By using the MOE architecture and strategic training methods, it solves key challenges in the development of AI models. With the progress of AI research, models such as Qwen2.5-MAX showed the thoughtful and reliable AI systems of thoughtful data use and training technology.

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Qwen AI introduced Qwen2.5-MAX: large MOE LLMs carefully studied on a large amount of data, and for the first time on MarktechPost, it was trained through the selected SFT and RLHF recipes for the first time.