Microsoft Phi-4
Microsoft's latest small language model that punches well above its weight class. Phi-4 delivers exceptional performance with just 14B parameters, combining text, vision, and audio capabilities in a highly efficient package.
Key Features
High Efficiency
Exceptional performance with only 14B parameters, optimized for inference speed and memory usage.
Multimodal
Native support for text, images, and audio processing in a single unified model.
Code Generation
Strong coding capabilities across multiple programming languages and frameworks.
Fine-tuning Ready
Optimized for domain-specific adaptation and custom fine-tuning workflows.
Technical Specifications
| Parameter | Value |
|---|---|
| Parameters | 14 billion |
| Context Length | 128K tokens |
| Architecture | Transformer-based with Mixture of Experts |
| Modalities | Text, Vision, Audio |
| Training Data | Up to June 2024 |
Performance Benchmarks
Common Use Cases
Conversational AI
Chatbots and virtual assistants with multimodal understanding
Code Assistant
Programming help, code generation, and debugging
Document Analysis
Text extraction, summarization, and document understanding
Educational Tools
Tutoring systems and learning assistance
Quick Start
# Using Ollama (Local)
ollama run phi4
# Using Hugging Face Transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-4")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-4")
# Using Azure AI
from azure.ai.inference import ChatCompletionsClient
from azure.core.credentials import AzureKeyCredential
client = ChatCompletionsClient(
endpoint="your-endpoint",
credential=AzureKeyCredential("your-key")
)