Phi-4-reasoning-vision-15B

모델 소개

**Developer:** Microsoft Corporation **Authorized Representative:** Microsoft Ireland Operations Limited, 70 Sir John Rogerson's Quay, Dublin 2, D02 R296, Ireland **Release Date:** March 4, 2026 **License:** MIT **Parameters:** 15B **Context Length:** 16,384 tokens **Inputs:** Text and Images **Outputs:** Text **Training GPUs:** 240 B200s **Training Time:** 4 days **Training Dates:** February 3, 2025 – February 21, 2026 **Model Dependencies:** Phi-4-Reasoning Phi-4-Reasoning-Vision-15B is a compact open-weight multimodal reasoning model built on the Phi-4-Reasoning language model backbone and the SigLIP-2 vision encoder, using a mid-fusion architecture. In this architecture, the vision encoder first converts images into visual tokens, which are then projected into the language model's embedding space and injected into the pretrained language model. This approach leverages the strengths of both pretrained components while keeping training and inference costs manageable. The model employs a dynamic resolution vision encoder with up to 3,600 visual tokens, enabling high-resolution image understanding critical for tasks such as GUI grounding and fine-grained document analysis. Bidirectional attention is applied within images (intra-image) to improve spatial reasoning without the overfitting risks observed with broader bidirectional schemes. Phi-4-Reasoning-Vision-15B is trained with Supervised Fine-Tuning (SFT) on a carefully curated mixture of reasoning and non-reasoning data. Rather than training separate models for each mode, the model operates as a single system that can invoke extended chain-of-thought reasoning (using ` ... ` blocks) for tasks like mathematical and scientific reasoning, or default to direct inference (tagged with ` `) for perception-focused tasks such as captioning, object detection, and grounding. The training data consists primarily of meticulously filtered and improved open-source vision-language datasets, supplemented by high-quality domain-specific data from internal Microsoft teams and targeted data acquisitions. This data-centric approach, combined with moderate training compute requirements (240 NVIDIA B200 GPUs for 4 days), distinguis

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

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출처 및 추적 기록

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。