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Alibaba Cloud, the digital technology and intelligence backbone of Alibaba Group, today announced it has released over 100 of its newly-launched large language models, Qwen 2.5, to the global open-source community. This significant contribution was revealed at the Apsara Conference, its annual flagship event.

In addition, Alibaba Cloud has unveiled a revamped full-stack infrastructure designed to meet the growing demands for robust AI computing. This new infrastructure includes innovative cloud products and services that enhance computing, networking, and data center architecture, all aimed at supporting the thriving development and wide-range applications of AI models.

“Alibaba Cloud is investing, with unprecedented intensity, in the research and development of AI technology and the building of its global infrastructure. We aim to establish an AI infrastructure of the future to serve our global customers and unlock their business potential,” said Eddie Wu, Chairman and Chief Executive Officer of Alibaba Cloud Intelligence.

100 Open-sourced Models Unveiled

The newly released open-source Qwen 2.5 models, ranging from 0.5 to 72 billion parameters in size, feature enhanced knowledge and stronger capabilities in math and coding and are able to support over 29 languages, catering to a wide array of AI applications both at the edge or in the cloud across various sectors from automobile, gaming to science research.

The Qwen model series, Alibaba Cloud’s portfolio of proprietary large language models, has achieved remarkable traction since its debut in April 2023. To date, the Qwen models have surpassed 40 million downloads across platforms such as Hugging Face and ModelScope, an open-source community initiative by Alibaba. Furthermore, these models have inspired the creation of over 50,000 models on Hugging Face.

The Qwen 2.5 release will see over 100 models being made open-source. This extensive range includes base models, instruct models, and quantized models of various precision levels and methods, spanning different modalities such as language, audio, and vision, along with specialized code and mathematical models.

“Today marks a significant milestone as we launch our most expansive open-source initiative to date,” said Jingren Zhou, Chief Technology Officer of Alibaba Cloud Intelligence. “This initiative is set to empower developers and corporations of all sizes, enhancing their ability to leverage AI technologies and further stimulating the growth of the open-source community. We remain committed to investing in advanced AI infrastructure to foster the widespread adoption of generative AI technologies across different industries.

Expanding the Frontier in Multimodal

In addition to its extensive suite of large language models, Alibaba Cloud also unveiled a new text-to-video model as part of its image generator, Tongyi Wanxiang large model family. The new model is capable of generating high-quality videos in a wide variety of visual styles from realistic scenes to 3D animation. The model can generate videos based on Chinese and English text instruction and transform static images into dynamic videos. The model features advanced diffusion transformer (DiT) architecture to enhance video reconstruction quality.

The cloud leader is also deploying a significant update to its vision language model with the introduction of Qwen2-VL, capable of comprehending videos lasting over 20 minutes and support video-based question-answering. Equipped with sophisticated reasoning and decision-making capabilities, Qwen2-VL is designed for integration into mobile phones, automobiles and robots, facilitating the automation of specific operations.

A Full-Stack AI Infrastructure Upgrade

The cloud pioneer has also announced a slew of innovative updates to its full-stack AI infrastructure covering green data center architecture, data management, model training and inferencing.

These updates are designed to provide more comprehensive support for customers and partners to maximize the benefits of the latest technology has to offer for building even more efficient, sustainable and inclusive AI applications. 

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