Running this model locally is fastest when deployed through a PowerShell script.
Go through the configuration rules shown below.
The installer automatically pulls the model (could be multiple GBs).
To save you time, the system will automatically determine efficient resource allocation.
Qwen3-VL-Embedding-2B is a compact yet powerful multimodal embedding model that processes text, images, and videos into a unified vector space. It leverages a vision-language transformer architecture with 2âŻbillion parameters, delivering stateâofâtheâart retrieval performance across diverse benchmarks. The model supports highâresolution visual inputs and can handle up to 2048âtoken text sequences, enabling flexible downstream tasks such as image search and crossâmodal retrieval. Its training pipeline incorporates largeâscale paired datasets, ensuring robust semantic alignment between modalities while maintaining computational efficiency. The resulting embeddings are widely adopted in production systems due to their fast inference and low memory footprint.
| Spec | Value |
|---|---|
| Parameters | 2âŻB |
| Embedding Dim | 1024 |
| Supported Modalities | Text, Image, Video |
| Max Text Tokens | 2048 |
| Max Image Resolution | 1024Ă1024 |
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