Qwen2-VL-7B-Instruct

Alibaba Cloud’s Qwen team built this seven billion parameter vision language model that can understand both images and videos. It’s the latest version of their Qwen-VL model and represents about a year of improvements.

What it does

This model lets you ask questions about images and videos using text. Think of it as having a conversation about visual content.

Images at any resolution

Unlike older models that need images in specific sizes, this one handles whatever resolution you throw at it. Whether you’re analyzing a tiny icon or a massive document scan, the model adapts to the native resolution. Higher resolutions give better results but take more time to process.

Long video understanding

You can feed it videos over 20 minutes long. The model can answer questions about what’s happening, create summaries, or have a conversation about the content. This makes it useful for analyzing everything from tutorial videos to recorded meetings.

Multilingual text recognition

Besides English and Chinese, the model can read text within images in most European languages, Japanese, Korean, Arabic, Vietnamese, and more. So if you have a photo of a street sign in Paris or a menu in Tokyo, it can understand the text.

State-of-the-art performance

On visual understanding benchmarks like MathVista, DocVQA, RealWorldQA, and MTVQA, this model achieves some of the best scores available. It’s particularly strong at understanding documents and mathematical content.

Example uses

Document analysis: Extract information from invoices, forms, receipts, or any other document. The model handles complex layouts and can read text at different orientations.

Video content: Summarize long videos, answer questions about specific moments, or create descriptions of what’s happening.

Multilingual content: Analyze images with text in multiple languages, useful for translating signs, menus, or documents.

Chart and diagram understanding: Explain graphs, flowcharts, technical diagrams, or data visualizations.

Technical details

The model uses what they call “Naive Dynamic Resolution” which maps images into a flexible number of visual tokens based on the actual resolution. It also uses Multimodal Rotary Position Embedding (M-ROPE) to handle the position information for text (1D), images (2D), and videos (3D).

The Qwen team released three model sizes: two billion, seven billion, and seventy-two billion parameters. This is the seven billion parameter instruction-tuned version, which offers a good balance between performance and speed.

Things to know

Let’s be honest about the limitations:

No audio support: The model doesn’t process audio from videos, only the visual content.

Knowledge cutoff: The training images go up to June 2023, so it won’t recognize events, products, or people from after that date.

Counting accuracy: In complex scenes with lots of objects, the counting isn’t always perfect. If you need precise counts, double-check the results.

Complex instructions: When you give it intricate multi-step instructions, the model sometimes struggles. Breaking complex tasks into simpler steps usually works better.

People and brands: The model has limited ability to recognize specific individuals or intellectual property. It might not identify every celebrity or branded product.

Try it yourself

You can try the Qwen2-VL-7B-Instruct model on the Replicate Playground.