Multimodal model

Mage VL

Mage-VL is a 4B-parameter multimodal model for image and video understanding, built on a from-scratch codec-native visual encoder (Mage-ViT) paired with a Qwen3-4B language backbone. It allocates video tokens by spatio-temporal importance, cutting token counts over 75% while extending usable context up to 8x and giving up to 3.5x faster inference than uniform frame sampling. A built-in cognition gate enables proactive, event-triggered streaming commentary on live video in one checkpoint.

Type
Multimodal
Weights
Open
Downloads
27k
Likes
405
Pipeline
image text to text
License
apache-2.0