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Phi 4 Multimodal Instruct

Developed by mjtechguy
Phi-4-multimodal-instruct is a lightweight open-source multimodal foundation model that supports text, image, and audio inputs to generate text outputs, with a context length of 128K tokens.
Downloads 18
Release Time : 2/28/2025

Model Overview

This model integrates the language, vision, and speech research data from Phi-3.5 and 4.0 models. Through supervised fine-tuning, direct preference optimization, and reinforcement learning from human feedback (RLHF), it excels in instruction-following accuracy and safety measures.

Model Features

Multimodal Support
Supports text, image, and audio inputs to generate text outputs, with a context length of 128K tokens.
Multilingual Support
Supports text, vision, and audio processing in multiple languages, covering major global languages.
High Performance
Outperforms WhisperV3 and SeamlessM4T-v2-Large in automatic speech recognition and speech translation tasks, ranking first on the Huggingface OpenASR leaderboard.
Lightweight
Suitable for memory/computation resource-constrained environments and latency-sensitive scenarios.

Model Capabilities

Text Generation
Image Understanding
Speech Recognition
Speech Translation
Speech Summarization
Visual Question Answering
Optical Character Recognition
Chart and Table Understanding
Multi-Image Comparison
Multi-Image or Video Clip Summarization
Audio Understanding

Use Cases

Business Applications
Intelligent Customer Service
Provides accurate customer service responses through multimodal inputs.
Speech Translation
Real-time translation of speech into multiple languages, supporting cross-language communication.
Education
Visual Math Problem Solving
Solves complex math problems through image inputs.
Multilingual Learning
Supports learning assistance for multilingual text and speech.
Research
Multimodal Research
Used for research and development of multimodal models.
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