phi-4-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Base Model | Microsoft Phi-4 (14B) |
| License | MIT |
| Context Length | 16K tokens |
| Quantization | Dynamic 4-bit |
| Original Training | 9.8T tokens on 1920 H100-80G GPUs |
What is phi-4-unsloth-bnb-4bit?
This is an optimized version of Microsoft's Phi-4 language model, transformed using Unsloth's dynamic 4-bit quantization technology. The model maintains the powerful capabilities of the original 14B parameter Phi-4 while significantly reducing memory requirements and improving inference speed. It's been converted to use Llama's architecture for better compatibility and fine-tuning capabilities.
Implementation Details
The model employs a sophisticated dynamic 4-bit quantization approach that selectively preserves critical parameters while compressing others, resulting in minimal accuracy loss compared to standard 4-bit quantization methods. This implementation achieves a 70% reduction in memory usage while delivering 2x faster inference speeds.
- Converted to Llama architecture for improved compatibility
- Includes Unsloth's specific bugfixes for Phi-4
- Supports efficient fine-tuning with reduced resource requirements
- Optimized for both inference and training scenarios
Core Capabilities
- Strong performance on MMLLU (84.8%), MATH (80.4%), and HumanEval (82.6%)
- Excels in reasoning tasks and code generation
- 16K token context window for handling longer inputs
- Optimized for chat-format interactions
- Supports efficient fine-tuning on custom datasets
Frequently Asked Questions
Q: What makes this model unique?
The model combines Microsoft's high-quality Phi-4 base model with Unsloth's innovative dynamic 4-bit quantization, offering exceptional performance while requiring significantly fewer computational resources. It's particularly notable for maintaining high accuracy while reducing memory usage by 70%.
Q: What are the recommended use cases?
The model is ideal for memory-constrained environments, latency-sensitive applications, and scenarios requiring strong reasoning capabilities. It's particularly well-suited for chat applications, code generation, and academic/scientific tasks where computational efficiency is crucial.
