Llama 3.2 is INSANE - But Does it Beat GPT as an AI Agent?

Summary

The video introduces Meta's latest LLaMA 3.2 language models, offering versions with 1 to 90 billion parameters. The creator compares the performance of LLaMA 3.2 90B with GPT-4 Mini, showcasing their ability to handle various AI agent tasks such as task management and file handling. LLaMA 3.2 performs well but falls short in certain tasks like Google Drive integration, where GPT-4 Mini outperforms. Despite limitations, LLaMA 3.2 represents significant progress for local models, especially in function calling, and is promising for future AI agent developments.

Takeaways

• 🤖 Meta recently released their latest suite of large language models, LLaMA 3.2, with versions ranging from 1 billion to 90 billion parameters.
• 🖥️ LLaMA 3.2 models can be run on a wide range of hardware, supporting diverse generative AI use cases.
• 📊 The 90 billion parameter version of LLaMA 3.2 shows impressive benchmark results, comparable to GPT-4, even outperforming it in some areas.
• 🎯 Local LLMs, such as LLaMA 3.2, have typically struggled with function calling (AI agent capabilities), but advancements in LLaMA 3.2 bring them closer to improving in this area.
• 🔧 The script creator developed an AI agent using LangChain and LangGraph, which allows for testing different models like LLaMA 3.2 and GPT-4 Mini by switching environment variables easily.
• 🧠 While GPT-4 Mini performs complex tasks like function calling, creating tasks, searching Google Drive, and handling RAG (retrieval-augmented generation), LLaMA 3.2 performs comparably but still has limitations, particularly with complex tool calls.
• 🚀 LLaMA 3.2’s AI agent capabilities show improvement but are still not as efficient as GPT-4 Mini in handling complex, multi-step tool calls.
• 📂 The testing showed that while GPT-4 Mini handled various tool calls flawlessly, LLaMA 3.2 struggled with more intricate tasks like formatting Google Drive search queries.
• 🔄 LLaMA 3.2 handles retrieval tasks (RAG) well, but for tool calling, it doesn’t match GPT-4 Mini’s performance, especially with complex workflows like downloading files and adding them to a knowledge base.
• 📈 Overall, LLaMA 3.2 represents a significant step forward for local LLMs in terms of AI agent performance, but further improvements are needed to match cloud-based models like GPT-4.

Q & A

What are the parameter sizes available for Llama 3.2?

Llama 3.2 is available in four parameter sizes: 1 billion, 3 billion, 11 billion, and 90 billion.

How does Llama 3.2 90B compare to GPT 40 Mini in terms of performance?

Llama 3.2 90B is considered comparable to GPT 40 Mini, performing well in many benchmarks, and even surpassing it in some cases. However, in the specific context of AI agents and function calling, GPT 40 Mini still performs better.

Why are local LLMs like Llama 3.2 important for some use cases?

Local LLMs allow users to run models on their own hardware without relying on external APIs. This is particularly important for users with privacy concerns or requirements for local processing due to data sensitivity.

What is a current limitation of local LLMs when used as AI agents?

Local LLMs have generally struggled with function calling, which is necessary for AI agents to perform tasks beyond generating text, such as sending emails, interacting with databases, and more.

What tools are used in the custom AI agent implementation described in the video?

The AI agent is built using LangChain and LangGraph, with integration for tools such as Asana (for task management), Google Drive (for file management), and a local Chroma instance (for vector database and retrieval-augmented generation).

What issue did Llama 3.2 90B encounter during the Google Drive test?

Llama 3.2 90B failed to properly format the search query when asked to retrieve a file from Google Drive, resulting in an incorrect tool call. It did not perform as well as GPT 40 Mini in this test.

How does the AI agent determine whether to invoke a tool?

The AI agent uses a router that checks if the LLM requests a tool call. If so, the agent invokes the tool and continues the process, looping back to the LLM for further instructions if necessary.

How did GPT 40 Mini perform with a more complex multi-step task involving Google Drive?

GPT 40 Mini was able to search for and download a specific file from Google Drive, add it to a knowledge base, and then use retrieval-augmented generation (RAG) to answer a query based on the file's content, although it downloaded the file multiple times unnecessarily.

What specific improvement does Llama 3.2 bring over Llama 3.1 in terms of function calling?

Llama 3.2, especially the 90B version, shows significant improvement in function calling over Llama 3.1, which struggled with this capability even at the 70B parameter size. However, it still does not reach the level of GPT 40 Mini.

What potential does the developer see for local LLMs as AI agents in the future?

The developer is optimistic that local LLMs will eventually excel in function calling and become highly capable AI agents. Once a local model reliably handles function calls, it will be a 'game-changer' for many applications.