Large Language Models (LLMs) have changed the world. However, generating text with them can be slow and expensive. While methods like speculative decoding have been proposed to accelerate the generation speed, their intricate nature has left many in the open-source community hesitant to embrace them.
That’s why we’re thrilled to unveil Medusa: a simpler, more user-friendly framework for accelerating LLM generation. Instead of using an additional draft model like speculative decoding, Medusa merely introduces a few additional decoding heads, following the idea of [Stern et al. 2018] with some other ingredients. Despite its simple design, Medusa can improve the generation efficiency of LLMs by about 2x.
In the following blog post, we’ll explore the fundamental bottlenecks of LLM generation and some limitations of speculative decoding, then show how Medusa manages to tackle them and achieve acceleration.
The implementation is available at this repo.
To continue reading this article, click here.
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