仅需两辆踏板车即可运送四人战术单元，灵活穿梭于丛林、沼泽等复杂地貌。近乎静音的电力驱动系统使作战单位能够实施隐蔽包抄、闪电突击，抵达任务区域后更可迅速隐匿，现已成为乌军实施侦察任务与无人机小组突袭行动的关键运载工具。
Специалисты раскрыли преимущества распространенного молочного изделия для диабетиковНутрициолог Свиггард: Творожный продукт минимально влияет на гликемические показатели
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A growing countertrend towards smaller (opens in new tab) models aims to boost efficiency, enabled by careful model design and data curation – a goal pioneered by the Phi family of models (opens in new tab) and furthered by Phi-4-reasoning-vision-15B. We specifically build on learnings from the Phi-4 and Phi-4-Reasoning language models and show how a multimodal model can be trained to cover a wide range of vision and language tasks without relying on extremely large training datasets, architectures, or excessive inference‑time token generation. Our model is intended to be lightweight enough to run on modest hardware while remaining capable of structured reasoning when it is beneficial. Our model was trained with far less compute than many recent open-weight VLMs of similar size. We used just 200 billion tokens of multimodal data leveraging Phi-4-reasoning (trained with 16 billion tokens) based on a core model Phi-4 (400 billion unique tokens), compared to more than 1 trillion tokens used for training multimodal models like Qwen 2.5 VL (opens in new tab) and 3 VL (opens in new tab), Kimi-VL (opens in new tab), and Gemma3 (opens in new tab). We can therefore present a compelling option compared to existing models pushing the pareto-frontier of the tradeoff between accuracy and compute costs.
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