Phi-3.1-mini-4k-instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 3.82B |
| Context Length | 4K tokens |
| License | MIT |
| Research Paper | arXiv:2404.14219 |
| Training Data Size | 3.3T tokens |
What is Phi-3.1-mini-4k-instruct-GGUF?
Phi-3.1-mini-4k-instruct-GGUF is Microsoft's enhanced version of their language model series, featuring significant improvements in instruction following and reasoning capabilities. This GGUF-quantized version makes the model more accessible for local deployment while maintaining its powerful capabilities.
Implementation Details
The model is built as a dense decoder-only Transformer architecture, fine-tuned using both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). It implements a specialized prompt template using system and user tags, making it particularly effective for conversational applications.
- Trained on carefully filtered public documents and high-quality educational data
- Includes synthetic "textbook-like" training data for enhanced reasoning
- Optimized for 4K token context length
- Implements Phi 3 prompt template format
Core Capabilities
- Common sense reasoning and language understanding
- Mathematical computation and problem-solving
- Code generation and analysis
- Long-context processing
- Multi-turn conversation handling
- Structured output generation
Frequently Asked Questions
Q: What makes this model unique?
This model represents a significant upgrade over previous Phi versions, with enhanced instruction following, improved reasoning capabilities, and explicit support for system prompts. It's particularly notable for its efficient size-to-performance ratio at only 3.82B parameters.
Q: What are the recommended use cases?
The model excels in educational applications, coding tasks, mathematical problem-solving, and general conversational AI scenarios. It's particularly suitable for applications requiring strong reasoning capabilities within a moderate parameter footprint.
