Publications
Academic Research & Contributions
Research Papers
Peer-reviewed publications and preprints
Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining
This paper introduces Z-Pruner, a novel post-training pruning technique for Large Language Models that achieves significant efficiency improvements without requiring retraining. Our method addresses the computational challenges of deploying LLMs by strategically removing redundant parameters while maintaining model performance. Extensive experiments demonstrate that Z-Pruner can reduce model size and inference time substantially while preserving the quality of generated outputs.
Research Impact
Contribution to the academic community
Research Focus Areas
Key domains of academic contribution
Efficient AI Systems
Developing techniques for model compression and optimization, including pruning and quantization methods that maintain performance while reducing computational requirements.
Computer Vision
Advancing temporal action localization and video understanding through transformer-based architectures and novel training methodologies.
Future Research Directions
I am actively exploring new research opportunities in efficient AI deployment, multimodal learning, and practical applications of large language models. I welcome collaborations and discussions on these topics.