阿列克谢·古谢夫（体育栏目编辑）
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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An obscure quirk of the /proc/*/mem pseudofile is its “punch through” semantics. Writes performed through this file will succeed even if the destination virtual memory is marked unwritable. In fact, this behavior is intentional and actively used by projects such as the Julia JIT compiler and rr debugger.
Globally this Wednesday evening, countless viewers will watch Donald Trump's televised briefing regarding the Iranian military engagement. Numerous observers anticipate clarification about conflict resolution and subsequent developments.
Гражданин осужден к лишению свободы после инцидента с насекомым20:56