More Efficient Hybrid Language Models through Group-Aware SSM Pruning

Hybrid large language models (LLMs), which utilize both attention mechanisms and state space models (SSMs), currently achieve top performance in terms of accuracy and runtime. While compression and distillation techniques have already been successfully used in purely attention-based models to train smaller and more efficient models, this research focuses on the compression of hybrid architectures. A novel approach, group-aware SSM pruning, allows for preserving the structural integrity of SSM blocks and their sequence modeling capabilities.

Traditional compression methods reach their limits with hybrid models. Group-aware SSM pruning, on the other hand, offers the possibility to significantly improve model size and inference speed without sacrificing accuracy. This technique focuses on the targeted removal of redundant parameters within the SSM blocks, taking into account the grouping of parameters to maintain the functionality of the SSMs.

The presented compression strategy combines SSM pruning with FFN pruning, reduction of the embedding dimension, and layer pruning. Subsequently, the model is retrained using knowledge distillation, similar to the MINITRON technique. This approach was demonstrated on the Nemotron-H 8B hybrid model and enabled a reduction to 4 billion parameters with up to 40 times fewer training tokens required. The resulting model surpasses the accuracy of comparably sized models while achieving twice the inference speed. This represents a significant advancement in the relationship between model size, accuracy, and speed.

The Importance of SSM Pruning for Hybrid Models

SSMs play a crucial role in hybrid LLMs by effectively modeling long-range dependencies in text. Traditional pruning methods that do not consider the specific structure of SSMs can significantly impair the performance of these models. Group-aware SSM pruning addresses this challenge by selectively reducing parameters within the SSM blocks while considering the relevant group structures. This preserves the SSMs' ability for sequence modeling, leading to improved accuracy and inference speed.

Future Perspectives and Application Possibilities

The results of this research open up new possibilities for the use of hybrid LLMs in resource-constrained environments. Smaller and more efficient models enable the integration of LLMs into mobile devices, embedded systems, and other applications that were previously not feasible due to high computational requirements. The combination of high accuracy and fast inference speed makes these compressed models attractive for a variety of applications, including chatbots, text generation, translation, and much more.

Mindverse and the Future of AI Development

The development of more efficient AI models is a central concern of Mindverse. As a German provider of all-in-one content tools for AI text, images, and research, Mindverse develops customized solutions such as chatbots, voicebots, AI search engines, and knowledge systems. Research in the field of model compression, such as the group-aware SSM pruning presented here, contributes to improving the performance and accessibility of AI technologies for a wider audience.

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