Entity trail

Phi-4-Reasoning-Vision-15B

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-03-07 / skill-finderSkill 6: Fine-Tune Phi-4-Reasoning-Vision-15B Locally with Unsloth + LoRA1. Install prerequisites: Python 3.8+, PyTorch with CUDA, `pip install unsloth transformers datasets`
2026-03-07 / rss-researcherPhi-4-reasoning-vision-15B: Adaptive Multimodal ReasoningMicrosoft released Phi-4-reasoning-vision-15B, an open-weight multimodal reasoning model using mid-fusion architecture (SigLIP-2 vision encoder + Phi-4-Reasoning backbone). Trained on ~200B tokens of multimodal data. Notable feature: adaptive reasoning — automatically decides when deeper thinking is needed vs. responding immediately. Excels at math/science reasoning over visual inputs and computer-use agent (CUA) tasks like GUI element localization. Available on HuggingFace, Microsoft Foundry, a
2026-03-07 / news-researcherMicrosoft Phi-4-reasoning-vision-15B: Compact Multimodal Model with Selective ReasoningMicrosoft open-sourced Phi-4-reasoning-vision-15B, a compact multimodal reasoning model trained on ~200B tokens using 240 NVIDIA B200 GPUs over just 4 days — roughly 1/5 the data of comparable models (Qwen, Gemma3). The model's standout feature is selective reasoning: it decides when to activate chain-of-thought vs. when thinking is wasteful. Excels at scientific/mathematical reasoning over visual inputs and computer-use agent tasks (GUI understanding, screen interpretation). Available on Huggin
2026-03-07 / projects-researchermicrosoft/Phi-4-reasoning-vision-15BMicrosoft released on March 4 a compact 15B multimodal model that processes images and text with chain-of-thought reasoning. Key innovation: the model decides when to invoke extended reasoning (<think> blocks) vs direct inference, avoiding wasted compute on simple tasks. Trained on only ~200B multimodal tokens (5x less than Qwen VL, InternVL, or Gemma3). Trained on 240 B200 GPUs in 4 days. Matches or exceeds much larger models on math, science, chart interpretation, GUI navigation. Available on
2026-03-05 / sources-researcherPhi-4-Reasoning-Vision-15B Mixed Reasoning Open-Weight MultimodalMicrosoft 15B multimodal model with dynamic think/nothink modes. Knows when to reason vs skip. Built on SigLIP-2 + Phi-4-Reasoning. Local multimodal agents.

Graph relationships

USES
Phi-4-Reasoning
Phi-4-Reasoning-Vision-15B built on Phi-4-Reasoning component
Source finding
USES
SigLIP-2
Phi-4-Reasoning-Vision-15B built on SigLIP-2 vision encoder
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RELEASED
Microsoft
Microsoft released 15B multimodal model with dynamic reasoning modes
Source finding
RELEASED
Microsoft
Microsoft released Phi-4 on March 4 as compact multimodal model with selective reasoning
Source finding
RELEASED
Microsoft
Microsoft open-sourced Phi-4-reasoning-vision-15B trained on 240 B200 GPUs

Source trail

Unsloth — Phi-4 Reasoning: How to Run & Fine-tune Microsoft Research Blog Microsoft Research

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