A New Dawn for Open-Source LLMs

The chatter about open-source Large Language Models (LLMs) being weaker than the likes of GPT-4 might not be putting the full picture together. What if, instead of pitting them against giants like GPT-4, we could just get them to work together? Enter, the Mixture of Agents (MoA) approach.

MoA is an innovative method that harnesses the combined strengths of multiple LLMs. Imagine a layered setup, where several LLM agents huddle in each layer. This strategic collaboration propels the performance of these open-source models beyond what’s conventionally expected. It's kind of like hosting a tech meetup—everyone brings their specific expertise to the table, leading to unexpectedly brilliant results.

On the AlpacaEval 2.0—our benchmarking stage—the MoA setup soared past the GPT-4 Omni’s 57.5% score, marking a commendable 65.1% using entirely open-source models. This showcases the power of collaboration and specialized roles among models, breaking new ground for what open-source LLMs can achieve.

Now, you might wonder about the speed. Surely, coordinating multiple models must be sluggish. Typically, yes. But not when you're using Groq hardware. In this scenario, performance speed isn't quite the barrier you might expect.

So, while statistics might have once whispered that open-source couldn't hang with the big guns, MoA is here to shout back—louder and with stronger numbers. In this scenario, if you can't join them, just team up.

Revolutionizing Open-Source LLMs with the Mixture of Agents Approach

Explore how the Mixture of Agents (MoA) method elevates open-source Large Language Models by combining their strengths, resulting in performance that rivals proprietary giants like GPT-4.

A New Dawn for Open-Source LLMs