Here’s how to setup Groq’s very own MoA project.
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Links:
https://x.com/KapadiaSoami/status/1811657156082712605?t=TVYYMu9pSqeELamR7OyKUg&s=19
https://github.com/skapadia3214/groq-moa
source
date: 2024-07-27 14:54:54
duration: 00:05:50
author: UCawZsQWqfGSbCI5yjkdVkTA
Here is a summary of the transcript in 300 words:
The speaker, a Web 3 DeFi tech editor, is excited to share a new project that combines the power of Large Language Models (LLMs) with the speed of Groq’s API. This project, called “MoA” (Mixture of Agents), allows you to take less capable models and make them more capable, achieving a nearly GPT-40 level performance.
The speaker provides a step-by-step guide on how to set up the project, which requires VSS code, a Groq API key, and a GitHub repository. The project comes with an interface, making it easy to experiment with different models and settings.
The MoA architecture consists of multiple agents working together over multiple layers to produce the best possible output. The speaker explains that when they first read about this project, their immediate thought was to power it with Groq’s API, leveraging the massive speed advantage. They also provide a link to a video explaining how MoA works.
The tutorial shows how to set up the project, install dependencies, and run the app using Streamlit. The speaker also demos the interface, highlighting the settings and capabilities of the project, including selecting main models, customizing agents for each layer, and experimenting with different temperatures and settings.
The speaker is enthusiastic about the potential of MoA and hopes it will be built into the main interface of Groq’s platform. They also highlight the potential for inference companies to build similar projects, such as MoA, LLMA, and Chain of Thought, into their interfaces. Overall, the speaker is excited about the prospects of this project and invites interested readers to explore it further.
