Hunyuan-A52B-Instruct
| Property | Value |
|---|---|
| Total Parameters | 389B |
| Active Parameters | 52B |
| Model Type | Mixture of Experts (MoE) |
| Paper | arXiv:2411.02265 |
| Context Length | 128K tokens |
What is Hunyuan-A52B-Instruct?
Hunyuan-A52B-Instruct is currently the largest open-source Transformer-based MoE model in the industry, featuring an innovative architecture that balances computational efficiency with powerful performance. The model utilizes 389 billion total parameters while actively employing 52 billion parameters during operation, making it both powerful and resource-efficient.
Implementation Details
The model implements several advanced technical features that contribute to its exceptional performance:
- High-quality synthetic data training for enhanced representation learning
- KV Cache Compression using Grouped Query Attention (GQA) and Cross-Layer Attention (CLA)
- Expert-specific learning rate scaling for optimized training
- Long-context processing capability up to 128K tokens
- Comprehensive multilingual support with strong performance in both English and Chinese tasks
Core Capabilities
- Exceptional performance on MMLU (89.9%) and MATH (77.4%) benchmarks
- Superior results in Chinese language tasks (CMMLU: 90.4%, C-Eval: 88.6%)
- Strong performance in coding tasks (HumanEval: 90.0%)
- Advanced reasoning capabilities demonstrated through BBH (89.5%) scores
- High alignment scores on MT-Bench (9.4) and AlignBench (8.3)
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its MoE architecture that enables it to achieve state-of-the-art performance while maintaining computational efficiency through selective parameter activation. It outperforms many larger dense models while using fewer active parameters.
Q: What are the recommended use cases?
The model excels in various applications including academic knowledge testing, mathematical reasoning, coding, and multilingual tasks. It's particularly strong in scenarios requiring deep reasoning and complex problem-solving, making it suitable for educational, research, and enterprise applications.
