Phi-3.5
by MicrosoftPhi-3.5 is a cutting-edge AI model series developed by Microsoft, comprising three specialized models: Phi-3.5-mini-instruct, Phi-3.5-MoE-instruct, and Phi-3.5-vision-instruct. These models are optimized for lightweight inference, expert mixture systems, and multimodal tasks, respectively. The series supports a 128k context length, excels in multilingual processing, and enhances multi-turn dialogue capabilities. It is licensed under the MIT open-source license and has demonstrated superior performance in benchmark tests against models like GPT4o, Llama 3.1, and Gemini Flash.
What is Phi-3.5?
Phi-3.5 is a new generation AI model series launched by Microsoft, designed for lightweight inference, expert mixture systems, and multimodal tasks. It includes three specialized models: Phi-3.5-mini-instruct, Phi-3.5-MoE-instruct, and Phi-3.5-vision-instruct. These models are optimized for various tasks and support a 128k context length, making them suitable for handling long text data and complex multilingual scenarios.
Key Features and Capabilities
Phi-3.5-mini-instruct
- Parameter Count: Approximately 3.82 billion parameters.
- Design Purpose: Designed for instruction following and fast inference tasks.
- Context Support: Supports 128k token context length.
- Use Cases: Ideal for environments with limited memory or computational resources. Capable of tasks like code generation, math problem solving, and logic-based reasoning.
- Performance: Excels in multilingual and multi-turn dialogue tasks. Outperforms similar-sized models in benchmark tests.
Phi-3.5-MoE-instruct
- Parameter Count: Approximately 41.9 billion parameters.
- Architecture: Uses a mixture of experts (MoE) architecture.
- Context Support: Supports 128k token context length.
- Performance: Excels in code, math, and multilingual understanding. Often outperforms larger models in specific benchmarks.
Phi-3.5-vision-instruct
- Parameter Count: Approximately 4.15 billion parameters.
- Functionality: Integrates text and image processing.
- Use Cases: Suitable for general image understanding, OCR, chart and table understanding, and video summarization.
- Context Support: Supports 128k token context length.
Getting Started
- Environment Setup: Ensure the development environment meets the hardware and software requirements.
- Model Acquisition: Visit the Hugging Face model repository to download the model code.
- Dependency Installation: Install the required libraries as per the model's documentation.
- Model Loading: Load the Phi-3.5 model using an API or code snippet.
- Data Processing: Prepare input data and preprocess it according to the model's requirements.
- Model Configuration: Configure model parameters based on the application scenario.
- Task Execution: Use the model to perform tasks like text generation, question answering, or text classification.
Application Scenarios
- Phi-3.5-mini-instruct: Compact and efficient, suitable for fast text processing and code generation in embedded systems and mobile applications.
- Phi-3.5-MoE-instruct: Provides deep reasoning for data analysis and multilingual text, suitable for interdisciplinary research and professional fields.
- Phi-3.5-vision-instruct: Advanced multimodal processing capabilities, suitable for automatic image annotation, video surveillance, and in-depth analysis of complex visual data.
Model Capabilities
Model Type
multimodal
Supported Tasks
Text Generation
Question Answering
Code Generation
Math Problem Solving
Image Understanding
Ocr
Video Summarization
Tags
AI Models
Machine Learning
Natural Language Processing
Multimodal AI
Lightweight Inference
Expert Mixture Systems
Multilingual Processing
Multi-turn Dialogue
Open Source
Benchmark Performance
Usage & Integration
Pricing
free
API Access
Available
License
Open Source
MIT
Requirements
- Python environment
- Transformers library
- PyTorch or TensorFlow
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