Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world. Phone Number: +1-650-246-9381 Email: [email protected]
228 Park Avenue South New York, NY 10003 United States
Website: Publisher: https://towardsai.net/#publisher Diversity Policy: https://towardsai.net/about Ethics Policy: https://towardsai.net/about Masthead: https://towardsai.net/about
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Founders: Roberto Iriondo, , Job Title: Co-founder and Advisor Works for: Towards AI, Inc. Follow Roberto: X, LinkedIn, GitHub, Google Scholar, Towards AI Profile, Medium, ML@CMU, FreeCodeCamp, Crunchbase, Bloomberg, Roberto Iriondo, Generative AI Lab, Generative AI Lab Denis Piffaretti, Job Title: Co-founder Works for: Towards AI, Inc. Louie Peters, Job Title: Co-founder Works for: Towards AI, Inc. Louis-François Bouchard, Job Title: Co-founder Works for: Towards AI, Inc. Cover:
Towards AI Cover
Logo:
Towards AI Logo
Areas Served: Worldwide Alternate Name: Towards AI, Inc. Alternate Name: Towards AI Co. Alternate Name: towards ai Alternate Name: towardsai Alternate Name: towards.ai Alternate Name: tai Alternate Name: toward ai Alternate Name: toward.ai Alternate Name: Towards AI, Inc. Alternate Name: towardsai.net Alternate Name: pub.towardsai.net
5 stars – based on 497 reviews

Frequently Used, Contextual References

TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e

Resources

Take our 85+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!

Publication

A Detailed Explanation of Mixtral 8x7B Model
Latest   Machine Learning

A Detailed Explanation of Mixtral 8x7B Model

Last Updated on January 10, 2024 by Editorial Team

Author(s): Florian

Originally published on Towards AI.

Including principles, diagrams, and code.

Since the end of 2023, the Mixtral 8x7B[1] has become a highly popular model in the field of large language models. It has gained this popularity because it outperforms the Llama2 70B model with fewer parameters (less than 8x7B) and computations (less than 2x7B), and even exceeds the capabilities of GPT-3.5 in certain aspects.

This article primarily focuses on the code and includes illustrations to explain the principles behind the Mixtral model.

The overall architecture of the Mixtral model, similar to Llama and other decoder-only models, can be divided into three parts: the input embedding layer, several decoder blocks, and the language model decoding head. This is illustrated in Figure 1.

Figure 1 : The overall architecture of the Mixtral model. Image by author.

The architecture of the decoder layer is depicted in Figure 2. Each decoder layer mainly consists of two modules: attention and a sparse mixture of experts(SMoE).

Figure 2: Decoder layer. Image by author.

We can see that the Mixtral model incorporates additional features, such as a sparse mixture of experts(SMoE), Sliding Window Attention(SWA), Grouped-Query Attention(GQA), and Rotary Position Embedding (RoPE).

Next, this article will explain these important features.

From Figure 1 and Figure 2, we already know the position of SMoE in the entire… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming aΒ sponsor.

Published via Towards AI

Feedback ↓