Phi-3.5-MoE-instruct
| Property | Value |
|---|---|
| Total Parameters | 41.9B |
| Active Parameters | 6.6B |
| Context Length | 128K tokens |
| License | MIT |
| Technical Paper | Phi-3 Technical Report |
| Languages Supported | 23 languages including English, Chinese, Arabic, etc. |
What is Phi-3.5-MoE-instruct?
Phi-3.5-MoE-instruct is Microsoft's latest Mixture-of-Experts (MoE) language model that achieves remarkable performance while maintaining computational efficiency. With 41.9B total parameters but only 6.6B active parameters during inference, it represents a significant advancement in efficient AI model design.
Implementation Details
The model utilizes a mixture-of-experts architecture with 16 expert networks, though only 2 experts are active during inference. It supports a massive 128K token context window and operates with BF16 precision. The model was trained on 4.9T tokens over 23 days using 512 H100-80G GPUs.
- Advanced flash attention mechanism for improved performance
- Comprehensive safety post-training implementation
- Support for 23 different languages
- Integration with popular frameworks like PyTorch and Transformers
Core Capabilities
- Strong performance in reasoning tasks, particularly in code, math, and logic
- Competitive multilingual capabilities despite smaller active parameter count
- Long-context understanding with 128K token support
- Efficient operation in memory/compute constrained environments
- State-of-the-art performance in various benchmarks, often outperforming larger models
Frequently Asked Questions
Q: What makes this model unique?
The model's key innovation lies in its efficient MoE architecture that achieves high performance with only 6.6B active parameters, making it both powerful and computationally efficient. It outperforms many larger models while requiring fewer resources.
Q: What are the recommended use cases?
The model excels in scenarios requiring strong reasoning capabilities, particularly in code generation, mathematical problem-solving, and logical reasoning. It's especially suitable for deployment in memory-constrained environments or latency-sensitive applications.
