Tencent's WeVisOne-VL-4B picked up 5,986 Hugging Face downloads last month, and I suspect a lot of those people expected another small chat-about-your-image model. It's a perception model instead: boxes, points, counts, text locations, segmentation masks and alpha mattes, all from one 4B checkpoint built on Qwen3-VL. You can run WeVisOne-VL-4B locally on an 8 GB GPU with a Q4_K_M GGUF in llama.cpp, and the Hub offers a one-line Ollama pull too. You just lose the masks when you do.

I went through the model card, the GitHub README, the config and the GGUF repos. This post covers what the model is, Tencent's numbers against Qwen3-VL-8B (and the baseline they left out), VRAM per format, and the two ways to run it.

What WeVisOne-VL-4B actually is

The model card says it comes from WeChat Vision at Tencent and is built on Qwen3-VL. The config.json confirms it: Qwen3VLForConditionalGeneration , 36 text layers, hidden size 2560, a 24-layer ViT with patch size 16.…