等了大半年的Qwen3-VL终于也开源了！

Core Viewpoint - The article discusses the recent open-source release of various AI models, particularly focusing on the Qwen3-VL model, highlighting its improvements over previous versions and its performance in various tasks. Model Improvements - The Qwen3-VL model has made significant enhancements compared to the Qwen2.5-VL model, including changes in the vision encoder, projector, and LLM decoder components. The patch size increased from 14 to 16, and the activation function was changed from silu to gelu_pytorch_tanh [6][7]. - The model now incorporates a DeepStack in the projector, integrating features from multiple layers of the vision encoder into the LLM [6]. Performance Metrics - The Qwen3-VL model's text capabilities are comparable to the Qwen3-235B-A22B model, with various performance metrics listed in a comparative table against other leading models [10]. - In specific tasks, Qwen3-VL demonstrated superior performance in OCR recognition, table recognition, and understanding complex visual tasks compared to mainstream open-source models [11][13][17]. Task-Specific Results - The model showed strong capabilities in recognizing handwritten text and extracting information from complex images, outperforming previous versions and other models in accuracy [11][13]. - In table recognition tasks, Qwen3-VL successfully extracted and formatted data into HTML, demonstrating its ability to follow instructions accurately [17][18]. Overall Assessment - The Qwen3-VL model is positioned as a top-tier visual language model, with substantial improvements in various capabilities, including data extraction, reasoning, and visual understanding tasks [14][30]. - The article concludes with a positive outlook on the model's performance, indicating a significant leap forward in the capabilities of visual language models [106].