New Llama 3 Model BEATS GPT and Claude with Function Calling!?

Summary

In this video, the presenter explores the groundbreaking open-source Llama 3 model developed by Grok, which excels in function calling and challenges proprietary models like GPT. The script details a comparison between GPT and Llama 3 using an AI personal assistant for task management in Asana, demonstrating the impressive speed and accuracy of Llama 3. The presenter highlights the significance of this open-source model in promoting AI transparency and accessibility, marking a significant step forward for the community.

Takeaways

• 🌟 The first open-source, large language model for function calling has been introduced by Grok, challenging proprietary models like GPT or Claude.
• 🏆 Grok's Llama 3 model has achieved top rankings on the Berkeley function calling leaderboard, with both its 70 billion and 8 billion parameter versions performing exceptionally well.
• 🔢 The 70 billion parameter Llama 3 model has a 90% accuracy, ranking it first on the leaderboard, while the 8 billion parameter version is only 1% less accurate, placing it third.
• 📊 The benchmarking for function calling is done through the Berkeley function calling leaderboard, which aims to represent real-world use cases for large language models.
• 🛠️ The video demonstrates using Grok's Llama 3 model with an AI personal assistant developed in the AI Master Class series for task management in Asana.
• 📝 The script details a comparison between GPT and Llama 3, showcasing the process of changing the code to use the new model for function calling tasks.
• 🔧 The AI agent is designed to interact with Asana on behalf of the user to manage projects and tasks, utilizing tools defined in the code.
• ⏱️ The video shows that the Llama 3 model is notably faster than GPT in executing function calling tasks, although it may require additional confirmation steps.
• 🗂️ The Llama 3 model successfully replicates the task management operations that GPT performs, including creating tasks, marking them as complete, and deleting tasks.
• 🤖 The script highlights the potential of using local, open-source models as AI agents in workflows, emphasizing the importance of transparency and accessibility in AI.
• 🎉 The video concludes by celebrating the success of the open-source Llama 3 model in performing function calling tasks, almost as effectively as proprietary models, marking a significant advancement for open-source AI.

Q & A

What major milestone has been achieved in the field of AI language models?

For the first time, the best large language model for function calling is an open-source model that can be run locally, breaking away from proprietary models like GPT or CLA.

Which company has developed their own version of Llama 3 for function calling?

A company called Gro has developed their own version of Llama 3, specifically designed for high performance in function calling.

How does Gro's Llama 3 model perform on the Berkeley function calling leaderboard?

Gro's Llama 3 model, both the 70 billion parameter version and the 8 billion parameter version, are ranked highly on the Berkeley function calling leaderboard, with the 70 billion parameter version being number one.

What is the significance of the 70 billion parameter version of Llama 3 being number one on the leaderboard?

The 70 billion parameter version of Llama 3 achieving a 90% accuracy on the leaderboard is significant as it demonstrates its superior performance in function calling compared to other AI models.

How does the 8 billion parameter version of Llama 3 compare to other models in terms of accuracy?

The 8 billion parameter version of Llama 3 is only 1% worse in overall accuracy compared to the 70 billion parameter version, making it a more efficient model in terms of size and performance.

What is the Berkeley function calling leaderboard and how is it used to benchmark AI models?

The Berkeley function calling leaderboard is a tool used to benchmark AI models based on their performance in function calling. It evaluates models based on how they are used in real-world scenarios like agents and enterprise workflows.

What AI personal assistant is being used in the video to test the Llama 3 model?

The AI personal assistant used in the video is one that the presenter has been developing in their AI Master Class video series, designed to help with task management.

How does the presenter plan to evaluate the effectiveness of the Gro Llama 3 model for function calling?

The presenter plans to evaluate the Gro Llama 3 model by comparing it to another powerful model, GPT 40, using the same AI agent for task management and observing their performance.

What tasks does the presenter assign to test the function calling capabilities of the AI models?

The presenter assigns tasks such as creating a project in Asana, adding steps as tasks with due dates, marking tasks as complete, deleting tasks, and adding new tasks to test the function calling capabilities of the AI models.

What are the key differences in performance between GPT and the Gro Llama 3 model observed in the video?

GPT is observed to handle tasks more smoothly and quickly, especially in understanding and executing multiple tasks without needing additional prompts. However, the Gro Llama 3 model, while slower, is still able to perform the tasks, demonstrating its effectiveness as an open-source model.

What is the presenter's final verdict on the Gro Llama 3 models in comparison to GPT?

While the presenter acknowledges that GPT is slightly better at handling tokens and executing tasks, they are impressed with the Gro Llama 3 models, especially considering they are open-source and perform almost as well as proprietary models like GPT.