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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.
，这一点在WhatsApp Web 網頁版登入中也有详细论述
配套推出的 App Builder 则用于配置哪些节点参数对外开放。创作者可以自行决定哪些输入项和输出项展示给用户，其余复杂参数继续保留在节点图内部。ComfyUI 方面举例称，一个文生图工作流可能包含采样器、步数、CFG、调度器、LoRA 权重、种子和分辨率等大量参数，但实际应用中可能只需开放其中少数几个。
Limitless Case (Pixelsnap)。关于这个话题，手游提供了深入分析
ANALYZE test_orders;
[&:first-child]:overflow-hidden [&:first-child]:max-h-full"，这一点在whatsapp中也有详细论述