Model Compression: Techniques for Efficient NLP Models

Description

This lecture on model compression in modern NLP covers various techniques such as pruning, quantization, weight factorization, weight sharing, knowledge distillation, and sub-quadratic transformers. It discusses the challenges of large language models, the benefits of compressing models, and the impact on memory footprint and inference time. The lecture also explores the concept of knowledge distillation, leveraging soft labels for student models, and the importance of attention mechanisms in transformer models.

Official source

https://mediaspace.epfl.ch/media/0_elalu0cs

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Google Docs

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Google

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Lossless compression

Lossless compression is a class of data compression that allows the original data to be perfectly reconstructed from the compressed data with no loss of information. Lossless compression is possible because most real-world data exhibits statistical redundancy. By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates (and therefore reduced media sizes).

Vector quantization

Vector quantization (VQ) is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms.