copy and paste this google map to your website or blog!
Press copy button and paste into your blog or website.
(Please switch to 'HTML' mode when posting into your blog. Examples: WordPress Example, Blogger Example)
Input Form:Deal with this potential dealer,buyer,seller,supplier,manufacturer,exporter,importer
(Any information to deal,buy, sell, quote for products or service)
Qwen3-VL can scan two-hour videos and pinpoint nearly every detail A few months after launching Qwen3-VL, Alibaba has released a detailed technical report on the open multimodal model The data shows the system excels at image-based math tasks and can analyze hours of video footage The system handles massive data loads, processing two-hour videos or hundreds of document pages within a 256,000-token
[2505. 09388] Qwen3 Technical Report - arXiv. org In this work, we present Qwen3, the latest version of the Qwen model family Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, and multilingual capabilities The Qwen3 series includes models of both dense and Mixture-of-Expert (MoE) architectures, with parameter scales ranging from 0 6 to 235 billion A key innovation in Qwen3 is the
Paper page - Qwen3 Technical Report - Hugging Face Thank you for the excellent technical report! I have a question regarding your experience with reasoning generation: have you encountered issues with excessive or endless repetitions, particularly when generating reasoning in languages other than English or Chinese? In my case, I attempted to distill reasoning traces from Qwen3-32B to Qwen3-8B in a target language, and observed that the
Is GPT actually using the encoder NOT the decoder part of the . . . - Reddit In the original paper they mention they are only using the decoder part of the model However, their description and implementations seem to be using the encoder part of the transformer not the encoder For example, this implementation of the original transformer encoder layer matches what the one in the GPT implementation This becomes more evident when you look at the decoder layer which has
Qwen3-VL - Hugging Face Qwen3-VL is a multimodal vision-language model series, encompassing both dense and MoE variants, as well as Instruct and Thinking versions Building upon its predecessors, Qwen3-VL delivers significant improvements in visual understanding while maintaining strong pure text capabilities Key architectural advancements include: enhanced MRope with interleaved layout for better spatial-temporal