| Property | Value |
|---|---|
| Parameters | 14B |
| Context Length | 16K tokens |
| Training Data | 9.8T tokens |
| License | MIT |
| Developer | Microsoft Research |
| Release Date | December 12, 2024 |
What is phi-4-gguf?
Phi-4-gguf is Microsoft's state-of-the-art language model, specifically optimized for efficient deployment while maintaining high performance. Built as a dense decoder-only Transformer with 14B parameters, it represents a careful balance between model size and capability, trained on a curated blend of synthetic datasets, filtered public domain content, and specialized academic materials.
Implementation Details
The model was trained over 21 days using 1920 H100-80G GPUs, processing 9.8T tokens. Its architecture emphasizes efficient computation while maintaining robust reasoning capabilities. The training approach incorporated both supervised fine-tuning and direct preference optimization for enhanced safety and instruction following.
- Dense decoder-only Transformer architecture
- 16K token context window
- Optimized for chat-format interactions
- Quantized version available for efficient deployment
Core Capabilities
- Strong performance in math and science (84.8% on MMLU, 80.4% on MATH)
- Excellent code generation capabilities (82.6% on HumanEval)
- Advanced reasoning and logic tasks
- Optimized for memory/compute constrained environments
- Suitable for latency-sensitive applications
Frequently Asked Questions
Q: What makes this model unique?
Phi-4 stands out for its efficient architecture and strong performance despite its relatively modest size. It achieves competitive results against larger models while being more practical to deploy, especially in resource-constrained environments.
Q: What are the recommended use cases?
The model excels in scenarios requiring strong reasoning capabilities, particularly in academic and technical contexts. It's especially suitable for applications needing quick response times, code generation, and mathematical problem-solving. However, it's primarily focused on English language tasks, with limited multilingual capabilities.