Core Concepts
Large language models with Mixture of Experts (MoE) architecture, such as Mixtral 8x7B and 8x22B, can achieve high performance while being more computationally efficient than monolithic models like Mistral 7B.
Abstract
The article compares the performance and system requirements of four large language models: Mistral 7B, Mistral 8x7B, Mistral 8x22B, and Mixtral 8x22B. It provides an overview of the Mixture of Experts (MoE) architecture, which allows for more efficient use of computational resources by activating only a subset of the model's parameters for a given input.
The key insights from the article are:
Larger language models generally have more knowledge and can achieve better results, but they are also more computationally expensive.
The MoE approach, as used in the Mixtral models, can significantly improve efficiency by only activating a portion of the model's parameters at a time, while maintaining high performance.
The Mixtral 8x7B model has 47 billion parameters, but only 13 billion are active at any given time. Similarly, the Mixtral 8x22B model has 141 billion parameters, but only 39 billion are active.
The author plans to test the practical performance and system requirements of the four models to compare their capabilities.
Stats
The Mixtral 8x7B model has 47 billion parameters, but only 13 billion are active at any given time.
The Mixtral 8x22B model has 141 billion parameters, but only 39 billion are active at any given time.
Quotes
"Nobody will wait for the chatbot's response if it takes 5 minutes."
"The MoE approach gives us a significant improvement: we can have a large language model that has a lot of knowledge but at the same time works faster, like a smaller one."