GRIN-MoE
| Property | Value |
|---|---|
| Total Parameters | 41.9B |
| Active Parameters | 6.6B |
| License | MIT |
| Context Length | 4K tokens |
| Paper | Technical Report |
What is GRIN-MoE?
GRIN-MoE (Gradient-Informed Mixture of Experts) is Microsoft's innovative language model that achieves remarkable performance while maintaining computational efficiency. Using a novel SparseMixer-v2 architecture, it operates with just 6.6B active parameters despite having a total capacity of 41.9B parameters.
Implementation Details
The model utilizes a unique architecture comprising 16 experts of 3.8B parameters each, implementing a decoder-only Transformer design. What sets it apart is its gradient-informed routing mechanism that eliminates the need for expert parallelism or token dropping, traditionally required in MoE models.
- Innovative SparseMixer-v2 architecture for gradient estimation
- Trained on 4.0T tokens over 18 days using 512 H100-80G GPUs
- 4K token context length with 32,064 vocabulary size
Core Capabilities
- Exceptional performance in coding and mathematics tasks
- Strong reasoning abilities across various benchmarks
- Competitive performance against larger models like GPT-3.5 and Mixtral
- Optimized for memory/compute constrained environments
Frequently Asked Questions
Q: What makes this model unique?
GRIN-MoE's distinctive feature is its ability to achieve state-of-the-art performance with significantly fewer active parameters through its gradient-informed routing mechanism, making it more efficient than traditional MoE models.
Q: What are the recommended use cases?
The model is particularly well-suited for scenarios requiring strong reasoning capabilities, especially in coding and mathematics. It's optimized for commercial and research applications in memory-constrained environments and latency-sensitive scenarios.
