The Aliyun Bailing platform recently announced the launch of the Qwen2.5-Turbo million long text model, developed by the Tongyi Qianwen team. The Qwen2.5-Turbo model supports processing ultra-long contexts of up to 1 million tokens, equivalent to 1 million English words or 1.5 million Chinese characters.

The Tongyi Qianwen team recently announced the open sourcing of their latest Qwen2.5-Coder full series, aimed at promoting the development of Open Code LLMs. Qwen2.5-Coder has garnered attention for its power, diversity, and practicality. The Qwen2.5-Coder-32B-Instruct model has achieved SOTA levels in coding capabilities, comparable to GPT-4o, demonstrating comprehensive abilities.

Recently, an open source multimodal AI model named Molmo has drawn widespread attention in the industry. This AI system, based on Qwen2-72B and leveraging OpenAI's CLIP as the visual processing engine, is challenging the dominance of traditional commercial models with its outstanding performance and innovative features. Molmo's standout characteristic is its efficient performance. Despite its relatively small size, it can compete with competitors that are ten times larger in processing capability. This 'small but exquisite' design philosophy not only enhances the model's

The 2024 Yunqi Conference opened grandly in Hangzhou, with Alibaba Group CEO and Alibaba Cloud Intelligence Group Chairman and CEO Wu Yongming delivering a keynote speech. During the conference, Alibaba announced a series of groundbreaking releases, including the launch of the next-generation open-source model Qwen2.5, which outperforms Llama405B and firmly holds the position of the 'strongest open-source large model'.

The Tongyi Qianwen team announced the official open source of the latest member of the Qwen family, the Qwen2.5 series language models, three months after the Qwen2 release. This marks one of the largest open source releases in history, including the general language model Qwen2.5, as well as the Qwen2.5-Coder and Qwen2.5-Math models specifically designed for programming and mathematics.

Alibaba Group's Tongyi Qwen QwenLM project has encountered an unexpected takedown of its Github page, resulting in a 404 error message when users try to access it. Project leader Lin Junyang responded in a social media post, stating that the team has not disappeared and is in communication with officials to resolve the page takedown issue. Despite the team's efforts, the page remains inaccessible. It is worth noting that the team recently released the Qwen2-VL model, which has excelled in processing video content up to 20 minutes long, surpassing multiple authoritative evaluation metrics for multimodal models, with some metrics even exceeding those of GPT.

On September 2nd, Tongyi Qwen announced the open sourcing of its second-generation visual language model Qwen2-VL, and launched APIs for the 2B and 7B sizes as well as their quantized versions on the Aliyun Bailian platform for direct user access. The Qwen2-VL model achieves comprehensive performance improvements in several areas. It can understand images of different resolutions and aspect ratios, setting global leading performance on benchmarks such as DocVQA, RealWorldQA, and MTVQA.

Alibaba's cloud computing division has released the new AI model Qwen2-VL, which possesses strong capabilities to understand visual content. It can analyze videos lasting up to 20 minutes in real-time, summarize the video content, answer related questions, and provide online chat support. In third-party benchmark tests, Qwen2-VL surpassed several metrics including Meta's Llama 3.1, OpenAI's GPT-4o, Anthropic's Claude 3 Haiku, and Google's Gemini 1.5 Flash.

On August 30, 2024, Alibaba Damo Academy's Tongyi Qianwen team announced a significant update to its latest achievement—the Qwen2-VL model. The Qwen2-VL model has achieved remarkable improvements in image understanding, video processing, and multilingual support, setting new benchmarks for key performance indicators.

Alibaba's Tongyi Qianwen team has released a math model named Qwen2 Math Demo, which excels at processing mathematical problems in text input and formulas in images and screenshots. It even surpasses GPT-4 on the MATH dataset, improving the score by 9.6%. The model has three versions, with the 72B version being particularly outstanding, outshining GPT-4 in challenging math problems and also outperforming Llama-3.1 on classic test sets like GSM8K and MATH.

Alibaba Group recently released its financial report for the first quarter of fiscal year 2025, highlighting the outstanding performance of Alibaba Cloud. The report shows that Alibaba Cloud's revenue grew by 6% year-on-year, reaching 26.549 billion yuan, with AI-related product revenue achieving triple-digit growth and public cloud business also realizing double-digit growth. Adjusted EBITA profit increased by 155% year-on-year, reaching 2.337 billion yuan.

Alibaba recently launched the new open-source voice model Qwen2-Audio, which excels in speech recognition, translation, and audio analysis, achieving significant performance improvements. Qwen2-Audio offers a basic version and an instruction fine-tuning version, supporting multiple languages such as Chinese, Cantonese, French, English, and Japanese, facilitating sentiment analysis and translation applications. Compared to Qwen-Audio, Qwen2-Audio features comprehensive optimizations in architecture and performance, utilizing more natural language prompts during the pre-training phase.

Aliyun has launched the Qwen2-Math series of large language models, focusing on mathematical problem-solving abilities that surpass existing open-source models and rival well-known closed-source models, including GPT-4, Claude-3.5-Sonnet, Gemini-1.5-Pro, and Llama-3.1-405B. This series is based on the Qwen2 model, which has undergone deep pre-training on a specialized mathematical corpus that includes mathematical web texts, professional books, code examples, and exam questions, as well as data autonomously generated by Qwen2.

Recently, Arcee AI has officially launched its latest open-source language model — Arcee-Nova. This new model, based on Qwen2-72B-Instruct, has quickly emerged in the open-source community as one of the most outstanding models. Evaluations show that Arcee-Nova's performance is nearly on par with GPT-4 from May 2023, marking a significant milestone for Arcee AI and bringing new hope to the entire AI community.Arcee-Nova is a sophisticated model that combines Qwen2-72B-Instruct with a custom-tune

Alibaba Cloud has recently released a large-scale audio language model named Qwen-Audio, which can accept various audio signal inputs and perform audio analysis or directly respond to voice commands, significantly enhancing the voice interaction experience.Product Entry:https://top.aibase.com/tool/qwen2-audioIn this release, Qwen2-Audio offers two unique voice interaction modes: voice chat and audio analysis. Users can interact with Qwen2-Audio via voice without the need for text input, and can

Recently, a model named Arcee Spark based on Qwen2 has undergone fine-tuning on 1.8 million sample data, featuring a 128k token context. The release of Arcee Spark has attracted widespread attention, particularly sparking a surge of interest among professionals in the field of artificial intelligence. In benchmark tests such as MT-Bench, it has performed exceptionally well, achieving the highest scores among similar models and even surpassing GPT-3.5 on multiple tasks. It is reported that Arcee

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.

In the early hours of today, Alibaba's Tongyi Qianwen team released the Qwen2 series of open-source models. This series includes pre-trained and instruction-tuned models in five sizes: Qwen2-0.5B, Qwen2-1.5B, Qwen2-7B, Qwen2-57B-A14B, and Qwen2-72B. Key information indicates that these models show significant improvements in parameter count and performance compared to the previous generation Qwen1.5.