Qwen2.5-VL-7B
A 7-billion-parameter vision-language model released by Alibaba's Tongyi Qianwen team. It supports image and text inputs and is commonly used in visual tasks such as GUI agents.
Timeline (last 90 days)
On the AndroidControl benchmark, GUI-JEPA's inference speed is 2.8 to 4.0 times that of a Qwen2.5-VL-7B agent fine-tuned with the same input.
Using only about 6% of Qwen2.5-VL-7B's parameter count, GUI-JEPA reached 86% to 92% of the latter's in-distribution accuracy on AndroidControl.
The Hy-Nomas architecture consists of three main parts: a language model (Qwen2.5-VL-7B), a DiT video diffusion transformer, and a decoder (Pixel UMM).
Realist runs the Qwen2.5-VL-7B vision model through a local Ollama server (default qwen2.5vl:7b), and photos are scaled down and stripped of metadata before being sent only to 127.0.0.1, not flowing externally.
Qwen2.5-VL-7B is a vision-language model released by Alibaba's Tongyi Qianwen team, with a parameter scale of about 7 billion; the 7B size can run locally on consumer-grade hardware.
SLVR is built on top of Qwen2.5-VL-7B.
All Facts (6)
On the AndroidControl benchmark, GUI-JEPA's inference speed is 2.8 to 4.0 times that of a Qwen2.5-VL-7B agent fine-tuned with the same input.
50%UnverifiedUsing only about 6% of Qwen2.5-VL-7B's parameter count, GUI-JEPA reached 86% to 92% of the latter's in-distribution accuracy on AndroidControl.
50%UnverifiedThe Hy-Nomas architecture consists of three main parts: a language model (Qwen2.5-VL-7B), a DiT video diffusion transformer, and a decoder (Pixel UMM).
50%UnverifiedRealist runs the Qwen2.5-VL-7B vision model through a local Ollama server (default qwen2.5vl:7b), and photos are scaled down and stripped of metadata before being sent only to 127.0.0.1, not flowing externally.
50%UnverifiedQwen2.5-VL-7B is a vision-language model released by Alibaba's Tongyi Qianwen team, with a parameter scale of about 7 billion; the 7B size can run locally on consumer-grade hardware.
50%UnverifiedSLVR is built on top of Qwen2.5-VL-7B.
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