Phi-3-mini-4k-instruct-gguf
| Property | Value |
|---|---|
| Parameter Count | 3.8B |
| Context Length | 4K tokens |
| Training Data | 3.3T tokens |
| License | MIT |
| Author | Microsoft |
What is Phi-3-mini-4k-instruct-gguf?
Phi-3-mini-4k-instruct-gguf is a lightweight, state-of-the-art language model that represents a significant advancement in efficient AI model design. Developed by Microsoft, this 3.8B parameter model is optimized for performance in resource-constrained environments while maintaining impressive capabilities across various tasks including reasoning, mathematics, and code generation.
Implementation Details
The model is implemented as a dense decoder-only Transformer architecture, fine-tuned using both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). The training process involved 512 H100-80G GPUs over 7 days, processing 3.3T tokens of carefully curated data.
- Available in multiple quantization formats (4-bit and 16-bit)
- Optimized for compute-constrained environments
- Supports chat-format interactions
- Compatible with popular frameworks like Ollama and Llamafile
Core Capabilities
- Strong reasoning abilities in mathematics and logic
- Efficient performance in memory-constrained scenarios
- Robust instruction following
- Code generation (primarily Python)
- Common sense reasoning and language understanding
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional performance-to-size ratio, offering state-of-the-art capabilities in a compact 3.8B parameter package. It's particularly notable for its strong reasoning abilities and efficient resource utilization, making it ideal for deployment in constrained environments.
Q: What are the recommended use cases?
The model is best suited for applications requiring quick response times in resource-limited settings, particularly those involving mathematical reasoning, code generation, and logical problem-solving. It's designed for commercial and research use in English language applications, especially in scenarios requiring strong reasoning capabilities with minimal computational overhead.
