Phi-3.1-mini-4k-instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 3.82B |
| Context Length | 4K tokens |
| License | MIT |
| Research Paper | arXiv:2404.14219 |
| Base Model | Microsoft Phi-3-mini-4k-instruct |
What is Phi-3.1-mini-4k-instruct-GGUF?
Phi-3.1-mini-4k-instruct-GGUF is an enhanced version of Microsoft's Phi series, featuring significant improvements in instruction following, structured output generation, and reasoning capabilities. This GGUF-quantized model represents a major leap forward from its predecessors, trained on 3.3T tokens of carefully curated data.
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) to ensure alignment with human preferences and safety guidelines. The training data comprises high-quality educational content, code, synthetic textbook-like material, and carefully filtered public documents.
- Optimized for 4K token context length
- Implements Phi 3 prompt template with system, user, and assistant tags
- GGUF quantization for efficient deployment
- Extensive post-training optimization for improved performance
Core Capabilities
- Advanced instruction following and structured output generation
- Strong performance in common sense and logical reasoning
- Proficient in mathematics and coding tasks
- Enhanced multi-turn conversation quality
- Explicit support for system prompts
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its optimized balance between size and capability, offering strong performance across reasoning, coding, and conversation tasks while maintaining a relatively compact 3.82B parameter count. The additional post-training optimizations specifically enhance its instruction-following and structured output capabilities.
Q: What are the recommended use cases?
The model excels in educational applications, coding assistance, mathematical problem-solving, and general conversational tasks. It's particularly well-suited for applications requiring structured reasoning and clear, well-formatted outputs.
