  In this work, we introduce the Qwen-VL series, a set of large-scale
vision-language models (LVLMs) designed to perceive and understand both texts
and images. Starting from the Qwen-LM as a foundation, we endow it with visual
capacity by the meticulously designed (i) visual receptor, (ii) input-output
interface, (iii) 3-stage training pipeline, and (iv) multilingual multimodal
cleaned corpus. Beyond the conventional image description and
question-answering, we implement the grounding and text-reading ability of
Qwen-VLs by aligning image-caption-box tuples. The resulting models, including
Qwen-VL and Qwen-VL-Chat, set new records for generalist models under similar
model scales on a broad range of visual-centric benchmarks (e.g., image
captioning, question answering, visual grounding) and different settings (e.g.,
zero-shot, few-shot). Moreover, on real-world dialog benchmarks, our
instruction-tuned Qwen-VL-Chat also demonstrates superiority compared to
existing vision-language chatbots. Code, demo and models are available at
https://github.com/QwenLM/Qwen-VL.
