Efficient data representation and storage
Efficient data representation and storage in the context of LLMs extends beyond quantization and pruning to encompass a variety of techniques and strategies. These approaches aim to reduce a model’s memory footprint and speed up computation, which are crucial for storage limitations and quick data retrieval. Let’s take a detailed look at advanced methods for efficient data representation and storage:
- Model compression:
- Weight sharing: Reduces the model size by having multiple connections in the neural network share the same weight, effectively reducing the number of unique weights that need to be stored
- Sparse representations: Beyond pruning, employing formats specifically designed for storing sparse matrices (such as CSR or CSC) can dramatically reduce the memory needed to store weights that are predominantly zeros
- Low-rank factorization: Decomposes weight matrices into smaller, lower-rank matrices that require less storage...
