Machine learning | Natural language processing | data science
In this article we'll look at “speculative sampling,” a strategy that makes text generation faster and more affordable without compromising performance.
First, we'll discuss a major problem that is slowing down modern language models, then develop an intuitive understanding of how speculative sampling gracefully speeds them up, and then implement speculative sampling from scratch in Python.
Who is this useful for? Anyone interested in natural language processing (NLP) or cutting-edge advances in ai.
How far along is this post? The concepts in this article are accessible to machine learning enthusiasts and are advanced enough to interest experienced data scientists. The code at the end can be useful for developers.
Previous requirements: It may be helpful to have a cursory knowledge of Transformers, OpenAI's GPT models, or both. If you feel confused, you can refer to any of these articles:
Over the past four years, OpenAI GPT models have grown from 117 million parameters in 2018 to approximately 1.8 trillion parameters in 2023. This rapid growth can largely be attributed to the fact that, in language modeling , the bigger the better.
