CLLMs: Consistency Large Language Models | AI Paper Explained
In this video we dive into Consistency Large Language Models (CLLMs), a new method which was introduced in a recent research paper, to significantly improve the inference latency of Large Language Models (LLMs).
CLLMs efficiently decode multiple tokens in one forward pass, which makes the response generation faster since there is no need to do a forward pass for each generated token.
CLLMs rely on Jacobi Decoding which is an existing method to decode multiple tokens in a single forward pass, so we provide an overview for what that is. However, the original Jacobi Decoding was not proved to significantly improve LLMs latency. Consistency LLMs improve the Jacobi decoding process by leveraging a technique with a strong analogy from computer vision, which are consistency models that improve the performance of diffusion models. In this stage we shortly explain what are diffusion models and consistency models in computer vision.
Then, we finally review what are Consistency LLMs and how they can be used to improve LLMs latencies.
Paper page - https://arxiv.org/abs/2403.00835
GitHub page - https://github.com/hao-ai-lab/Consist...
Blog post - https://aipapersacademy.com/consisten...
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Chapters:
0:00 Introduction
1:09 Jacobi Decoding
2:58 Diffusion Models
3:53 Consistency Models
4:43 Consistency LLMs
6:00 Results