We are pleased to announce Phi-4-reasoning-vision-15B, a 15 billion parameter open‑weight multimodal reasoning model, available through Microsoft Foundry (opens in new tab), HuggingFace (opens in new tab) and GitHub (opens in new tab). Phi-4-reasoning-vision-15B is a broadly capable model that can be used for a wide array of vision-language tasks such as image captioning, asking questions about images, reading documents and receipts, helping with homework, inferring about changes in sequences of images, and much more. Beyond these general capabilities, it excels at math and science reasoning and at understanding and grounding elements on computer and mobile screens. In particular, our model presents an appealing value relative to popular open-weight models, pushing the pareto-frontier of the tradeoff between accuracy and compute costs. We have competitive performance to much slower models that require ten times or more compute-time and tokens and better accuracy than similarly fast models, particularly when it comes to math and science reasoning.
The first problem is wasted work again. If cell A1 references B8, and cell A2 also references B8, then when we update all the cells, we still only want to evaluate B8 once, and then reference it in both A1 and A2. We can do this through caching — whenever we calculate a cell’s value, we store it somewhere, and then all future cell references can used the stored value instead of recalculating.。关于这个话题，新收录的资料提供了深入分析
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Стало известно о массовом вывозе убитых после удара по пансионату под Николаевом14:33，详情可参考新收录的资料