Qwen2-VL: To See the World More Clearly
- Source
- qwenlm.github.io
- Author
- Qwen Team
- Date
Context
Qwen's Qwen2-VL changes how images are represented for a . Its architecture uses a variable number of visual according to image resolution, allowing the representation to vary with the input. It also encodes position along height, width, and time so the model can handle spatial relationships and video frames.
The implementation lets developers cap the visual- to balance detail against speed and memory. That makes resolution handling a configurable resource choice for applications reading documents or answering questions about images. Video support has a separate boundary: this release cannot extract audio from video, so visual understanding alone does not cover a recording's spoken content.
- LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
- token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
- token budget — A cap on how many tokens a task, session, or agent run may consume — the practical control on both cost and how long an agent will grind.
“Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.”
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