| Property | Value |
|---|---|
| Parameters | 14B |
| Context Length | 16K tokens |
| Training Data | 9.8T tokens |
| License | MIT |
| Release Date | December 12, 2024 |
What is phi-4-bnb-4bit?
Phi-4-bnb-4bit is Unsloth's optimized version of Microsoft's Phi-4 model, converted to Llama's architecture for enhanced performance and efficiency. This 4-bit quantized version delivers impressive performance while requiring significantly less computational resources, achieving 2x faster training speeds with 50% less memory usage.
Implementation Details
The model is built on a dense decoder-only Transformer architecture with 14B parameters. It has been trained on a diverse dataset of 9.8T tokens, including synthetic datasets, filtered public domain content, and academic materials. The training process utilized 1920 H100-80G GPUs over 21 days.
- Converted to Llama architecture for better fine-tuning capabilities
- 4-bit quantization for efficient deployment
- 16K token context window
- Optimized for both accuracy and computational efficiency
Core Capabilities
- Strong performance in MMLLU (84.8%) and MATH (80.4%) benchmarks
- Excellent code generation capabilities (82.6% on HumanEval)
- Advanced reasoning and logic tasks
- Optimized for memory-constrained environments
- Suitable for latency-sensitive applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimal balance between performance and resource efficiency. The 4-bit quantization combined with Unsloth's optimizations enables faster training and inference while maintaining high accuracy across various benchmarks.
Q: What are the recommended use cases?
The model is particularly well-suited for research applications, general-purpose AI systems, and scenarios requiring strong reasoning capabilities. It's especially valuable in compute-constrained environments and latency-sensitive applications. The model excels in tasks involving math, code generation, and complex reasoning.