Search Results for author:
Found 4 papers, 4 papers with code
AffineQuant: Affine Transformation Quantization for Large Language Models
Among these techniques, Post-Training Quantization (PTQ) has emerged as a subject of considerable interest due to its noteworthy compression efficiency and cost-effectiveness in the context of training.
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
In particular, we first formulate the oscillation in PTQ and prove the problem is caused by the difference in module capacity.
OMPQ: Orthogonal Mixed Precision Quantization
Instead of solving a problem of the original integer programming, we propose to optimize a proxy metric, the concept of network orthogonality, which is highly correlated with the loss of the integer programming but also easy to optimize with linear programming.
An Information Theory-inspired Strategy for Automatic Network Pruning
This practically limits the application of model compression when the model needs to be deployed on a wide range of devices.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
