Hymba Novel Architecture For Small Language Models
2 min read
The paper introduces Hymba, a novel architecture for small language models that combines transformer attention mechanisms with state space models (SSMs) in a hybrid-head parallel structure. This design aims to enhance efficiency and performance by leveraging the strengths of both attention and SSM heads.
Hybrid-Head Architecture
Hymba integrates attention heads for high-resolution recall and SSM heads for efficient context summarization within the same layer. This parallel processing approach allows the model to handle diverse information flows and memory access patterns more effectively.
Learnable Meta Tokens
The model introduces learnable meta tokens that are added to the beginning of prompts. These tokens store critical information and reduce the burden on attention mechanisms, improving performance across various tasks.
Optimization Techniques
Hymba incorporates cross-layer key-value (KV) sharing and partial sliding window attention to optimize cache size and throughput. These optimizations result in a more efficient and compact model.
Performance Benchmarks
Extensive evaluations show that Hymba achieves state-of-the-art results for small language models. For example, the Hymba-1.5B-Base model outperforms other sub-2B models and even surpasses the Llama-3.2-3B model in terms of accuracy, cache size reduction, and throughput.
Conclusion
Hymba represents a significant advancement in the design of small language models, offering enhanced efficiency and performance through its hybrid-head architecture and optimization techniques. The model's ability to outperform larger models underscores its potential for various applications, including on-device tasks.
