Tag: Matrix Decomposition
- August 10, 2024 Low-Rank Factorization Techniques for Neural Network & LLM Optimization Low-Rank Factorization is a powerful technique that compresses neural networks by breaking down large weight matrices into simpler, smaller components, reducing computational demands without sacrificing performance. Perfect for optimizing large language models, these methods streamline model size and speed up inference, making them ideal for real-world deployment.