Moonlight-16B-A3B
| Property | Value |
|---|---|
| Total Parameters | 16B |
| Active Parameters | 3B |
| Training Tokens | 5.7T |
| Context Length | 8K |
| Model Type | Mixture-of-Expert (MoE) |
| Paper | arXiv:2502.16982 |
What is Moonlight-16B-A3B?
Moonlight-16B-A3B is a state-of-the-art Mixture-of-Expert (MoE) language model that represents a significant advancement in efficient AI model training. Developed by Moonshot AI, it utilizes the innovative Muon optimizer to achieve superior performance with substantially fewer training FLOPs compared to traditional approaches.
Implementation Details
The model employs two crucial techniques for scaling: Weight Decay and Consistent RMS Updates. It features a distributed implementation with ZeRO-1 style optimization for memory efficiency. The architecture is compatible with popular inference engines like VLLM and SGLang.
- Optimized with Muon, achieving 2x sample efficiency vs Adam
- 8K context length support
- Achieves 70.0 on MMLU, surpassing comparable models
- Strong performance across English, Code, Math, and Chinese tasks
Core Capabilities
- General language understanding and generation
- Strong performance in mathematical reasoning (77.4 on GSM8K)
- Code generation capabilities (48.1 on HumanEval)
- Multilingual support with strong Chinese language capabilities
- Efficient inference with popular deployment tools
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its use of the Muon optimizer, which provides 2x sample efficiency compared to Adam, allowing it to achieve superior performance with fewer training FLOPs. Its MoE architecture enables efficient scaling while maintaining strong performance across diverse tasks.
Q: What are the recommended use cases?
The model excels in various applications including general language understanding, mathematical reasoning, code generation, and multilingual tasks. It's particularly suitable for applications requiring high performance with efficient resource utilization.
