phi-2-GGUF
| Property | Value |
|---|---|
| Parameter Count | 2.7B |
| Context Length | 2048 tokens |
| License | Microsoft Research License |
| Base Model | Microsoft/phi-2 |
What is phi-2-GGUF?
phi-2-GGUF is a converted version of Microsoft's Phi-2 model, optimized for efficient CPU and GPU inference using the GGUF format. The model represents a significant advancement in compact language models, offering near state-of-the-art performance among models under 13B parameters. It's specifically designed for research purposes and excels in common sense reasoning, language understanding, and logical reasoning tasks.
Implementation Details
The model comes in multiple quantization formats ranging from 2-bit to 8-bit precision, allowing users to balance between model size and performance. The recommended Q4_K_M variant offers a balanced compromise at 1.79GB file size. The model supports various deployment options including llama.cpp, text-generation-webui, and Python integrations.
- Multiple quantization options (Q2_K through Q8_0)
- GPU layer offloading support
- Integration with popular frameworks and UIs
- 2048 token context window
Core Capabilities
- Strong performance in QA format tasks
- Code generation and completion
- Chat-style interactions
- Common sense reasoning and logical analysis
- Python code generation with common package support
Frequently Asked Questions
Q: What makes this model unique?
The model combines efficient performance with relatively small size, making it accessible for research and development while maintaining high-quality outputs. Its GGUF format allows for flexible deployment across different computing environments.
Q: What are the recommended use cases?
The model is best suited for research purposes, particularly in areas of toxicity reduction, bias understanding, and model controllability. It excels in QA formats, chat interactions, and code generation tasks, especially with Python.
