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ByteDance Research has announced the open-sourcing of ChatTS-14B, a 14-billion parameter large language model (LLM) specifically designed for understanding and reasoning with time series data. Released under the Apache2.0 license, ChatTS-14B's open-source release has garnered significant attention within the AI community, marking a substantial advancement in the intersection of time series analysis and generative AI. ChatTS-14B: An Intelligent Conversational Engine for Time Series. ChatTS-14B is based on Qwen2.5-1...

The Kimi team recently released a technical report and open-sourced the preview version of Kimina-Prover, including 1.5B and 7B parameter distilled models, the Kimina-Autoformalizer-7B model for data generation, and a revised miniF2F benchmark dataset. Kimina-Prover, jointly developed by the Numina and Kimi teams, is a mathematical theorem proving model that excels in the field of formal theorem proving.

Stanford University's AI Index Report 2025 offers a fresh perspective on the global AI landscape. The report highlights Alibaba's significant contribution, ranking third globally among major large language models, establishing it as a leading Chinese tech company. In 2024, China contributed 15 models globally, with Alibaba contributing 6, trailing only Google and OpenAI with 7 models each. This achievement reflects Alibaba's ongoing commitment to technological innovation.

Researchers at the Hebrew University of Jerusalem recently discovered that in Retrieval Augmented Generation (RAG) systems, the number of documents processed significantly impacts language model performance, even when the total text length remains constant. The research team conducted experiments using 2,417 questions from the MuSiQue validation dataset, each linked to 20 Wikipedia paragraphs. Two to four paragraphs contained relevant answer information, with the remaining paragraphs serving as distractors. To study the impact of the number of documents, the team created multiple data partitions, progressively reducing the number of documents from 20 to...

On March 27th, Alibaba launched its first multimodal large model, Qwen2.5-Omni-7B. This model boasts powerful capabilities, handling various input modalities such as text, images, audio, and video, and generating text and natural speech outputs in real-time. This innovative technological breakthrough marks another significant advancement for Alibaba in the field of artificial intelligence. In the authoritative OmniBench multimodal fusion task benchmark, Qwen2.5-Omni achieved...

The Alibaba Cloud Tongyi Qianwen Qwen team announced the launch of Qwen2.5-Omni, a new generation of end-to-end multimodal flagship model in the Qwen family. Designed for comprehensive multimodal understanding, this new model seamlessly handles various input formats including text, images, audio, and video, and generates text and natural speech synthesis outputs simultaneously via real-time streaming response.

Alibaba is making waves in the AI field with the recent open-source release of its latest multimodal model, Qwen2.5-VL-32B-Instruct. This model is part of the Qwen2.5 series, which also includes 3B, 7B, and 72B versions. The 32B version prioritizes convenient local execution while maintaining performance. Enhanced through reinforcement learning, Qwen2.5-VL-32B excels in several areas. Notably, its responses are more aligned with human expectations.

A powerful newcomer has emerged in the fintech arena. The Fin-R1 model, jointly developed by Professor Liwen Zhang's team (SUFE-AIFLM-Lab) at the School of Statistics and Data Science, Shanghai University of Finance and Economics, and Caiyue Xingchen, has been officially open-sourced, attracting significant attention due to its impressive performance. This financial specialized large language model, based on Qwen2.5-7B and trained with reinforcement learning, achieves leading performance across multiple financial benchmark tests. Remarkably, Fin-R1 surpasses most models of comparable size, and even many significantly larger models, despite having only 7B parameters.

Tongyi Lingma has announced the launch of its latest inference model, Qwen2.5-Max, offering developers powerful programming and mathematical capabilities. Trained on over 20 trillion tokens and incorporating a meticulously designed post-training scheme, Qwen2.5-Max demonstrates exceptional performance.

On February 25th, Alibaba announced the launch of QwQ-Max-Preview, a reasoning model based on Qwen2.5-Max, and plans to fully open-source its latest reasoning models, QwQ-Max and Qwen2.5-Max. The released QwQ-Max-Preview is a preview version. Alibaba stated that the official version will be released soon and fully open-sourced under the Apache2.0 license. Unlike previous releases, this open-sourcing includes not only the model itself but also smaller versions such as Q...

Alibaba Cloud Tongyi has open-sourced a new visual model Qwen2.5-VL, launching three sizes: 3B, 7B, and 72B. The flagship model Qwen2.5-VL-72B won the championship in visual understanding across 13 authoritative evaluations, surpassing GPT-4o and Claude 3.5. According to Alibaba Cloud's official introduction, the new Qwen2.5-VL can analyze image content more accurately and support breakthrough video understanding of over 1 hour. This model can search for specific events in videos, and provide insights on...

Today, the Alibaba Cloud Tongyi team officially released the new mathematical reasoning process reward model Qwen2.5-Math-PRM. This model offers two sizes, 72B and 7B, with performance significantly outperforming similar open-source process reward models, especially excelling in identifying reasoning errors. The 7B version of Qwen2.5-Math-PRM astonishingly surpasses the widely popular GPT-4o, marking an important step in Alibaba Cloud's research and development of reasoning models.

Recently, Alibaba Damo Academy launched a multi-modal large language model named Valley 2, designed for e-commerce scenarios. It aims to enhance performance across various fields and expand the application boundaries of e-commerce and short video scenarios through a scalable vision-language architecture. Valley 2 utilizes Qwen 2.5 as its LLM backbone, paired with the SigLIP-384 visual encoder, incorporating MLP layers and convolution for efficient feature transformation.