Build Anything with Llama 3 Agents, Here’s How

David Ondrej
19 Apr 202412:23

TLDRDavid Andre's video tutorial guides viewers on building AI agents using the Llama 3 model, suitable for those with limited computer resources or programming knowledge. The video covers the use of AMA for local model running, VS Code for coding, and Gro for enhanced performance, achieving an impressive 216 tokens per second. Andre introduces the Llama open-source model, compares it to others like GPT-4, and emphasizes the importance of building AI agents to stay ahead in the field. He provides a step-by-step process for creating an email classifier agent and a responder agent using the LangChain Community package and the Crew AI library in Python. The tutorial also demonstrates integrating the Gro API for improved speed and performance. Despite encountering issues with the Llama 3 model in Crew AI, Andre successfully resolves them and showcases the remarkable speed of the API, encouraging viewers to join his community for further learning and staying at the forefront of AI advancements.

Takeaways

  • 🚀 **Building AI Agents**: David Andre demonstrates how to build AI agents using the Llama 3 model, even without a powerful computer or programming knowledge.
  • 💻 **Local Model Execution**: AMA is used to run the models locally, while VS Code is used for writing the code, and Gro for achieving fast performance.
  • 📈 **Performance Speed**: The video showcases the speed of the Llama model, with 216 tokens per second, highlighting the potential of the larger and smaller models.
  • 🔍 **Model Selection**: Llama 370B, an open-source model, is compared to GBD4, emphasizing the importance of building AI agents to stay competitive.
  • 🛠️ **Step-by-Step Workshop**: David Andre has built a workshop to teach users, step by step, how to build AI agents, accessible through his community.
  • 📚 **Downloading the Model**: The process of downloading the Llama model for the first time is explained, with the smaller model taking about 20 minutes and the larger one about 3 hours.
  • 🔗 **Package Installation**: The script details how to install the necessary packages, such as `Pip install crew AI`, to work with the Llama model in Python.
  • 📧 **Email Classifier Agent**: An example project is introduced, where the first agent classifies an email, and the second agent writes a response based on the classification.
  • 🔑 **API Key Integration**: The video shows how to add the Gro API for improved performance, especially on less powerful computers.
  • 🔬 **Troubleshooting**: The script discusses troubleshooting steps when the model does not work well through crew AI, suggesting that it works perfectly in the terminal but not as expected within crew AI.
  • ⚡ **API Speed**: The final demonstration shows the impressive speed of the Gro API, emphasizing the AI revolution and the importance of staying ahead in the field of AI.

Q & A

  • What is the main topic of the video?

    -The video is about building AI agents using the new Llama 3 model, even for those with limited computer resources or programming knowledge.

  • Who is the presenter of the video?

    -David Andre is the presenter of the video.

  • What tools does David Andre recommend for running the models locally?

    -David Andre recommends using AMA to run the models locally, VS Code to write the code, and Gro to achieve super-fast performance.

  • What is the significance of the Llama 3 model?

    -The Llama 3 model is an open-source model that is better than GBD4, representing the future of AI and offering high-speed performance.

  • How does David Andre suggest building AI agents without programming skills?

    -David Andre has built a workshop that teaches step-by-step how to build AI agents, catering to individuals who are not programmers.

  • What is the first step in building AI agents as suggested by David Andre?

    -The first step is to go to AMA's website, download the necessary components, and then download VS Code from visualstudio.com.

  • What is the recommended model to start with for building AI agents?

    -David Andre recommends starting with the 8 billion model of Llama 3 for its good performance.

  • What is the process for downloading the Llama model?

    -To download the Llama model, one needs to go to the terminal in VS Code, run the appropriate command by copying it from the Llama models page, and wait for the download to complete.

  • What is the purpose of the 'email classifier' agent that David Andre plans to build?

    -The 'email classifier' agent is designed to receive a random email, classify it based on importance, and then the second agent will write a response accordingly.

  • How does David Andre handle the issue of the Llama model not working well through crew AI?

    -David Andre suggests using the Gro API instead, which he demonstrates to work effectively and with high speed.

  • What is the community that David Andre has created for?

    -David Andre has created a community for people who want to take AI seriously and stay at the cutting edge of AI technology.

  • Why is it important to join David Andre's community according to the video?

    -Joining the community is important to not fall behind in the AI revolution, to learn from others who are serious about AI, and to have access to resources like the step-by-step workshop for building AI agents.

Outlines

00:00

🚀 Introduction to Building AI Agents with Llama Model

David Andre introduces the video with an overview of building AI agents using the Llama model. He emphasizes that viewers can follow along even without a powerful computer or programming knowledge. The tools mentioned are AMA for running models locally, VS Code for writing code, and Gro for achieving high performance. David demonstrates the speed of the Llama model by classifying a joke as spam and encourages viewers to build AI agents to stay ahead in the field. He also mentions a workshop for non-programmers and provides a link in the description.

05:01

🛠️ Setting Up the Development Environment

The second paragraph details the initial setup for building AI agents. It starts with downloading AMA and VS Code, then obtaining the Llama model from the Llama website. David provides instructions for installing the necessary packages in the correct environment using the terminal in VS Code. He guides viewers on how to import the Llama model and other required modules from the LangChain and Crew AI libraries. The goal is to create an email classifier agent that can categorize emails and a responder agent that writes responses based on the classification.

10:04

📈 Optimizing Performance with Gro

In the third paragraph, David addresses an issue with the Llama model's performance when run through Crew AI, despite working well in the terminal. He suspects that the model might be causing hallucinations and decides to show viewers how to use the Gro API instead. He guides through creating an API key on Gro Cloud, setting up the necessary environment variables, and modifying the code to use the Gro API for better performance. The paragraph concludes with a demonstration of the improved speed and accuracy of the email classification task using the Gro API.

Mindmap

Keywords

💡AI agents

AI agents, or artificial intelligence agents, are autonomous systems that can perform tasks, make decisions, and interact with their environment on behalf of a user or another program. In the video, the main theme revolves around building AI agents using the Llama 3 model, which is a type of language model capable of understanding and generating human-like text. The script discusses creating email classifiers and responders as examples of AI agents.

💡Llama 3 model

The Llama 3 model refers to a specific version of an AI language model that is used for natural language processing tasks. It is mentioned in the script as a new and improved model for building AI agents. The video demonstrates using both a smaller 'Llama free 8 billion' model and a larger 'Llama 370b' model to achieve fast performance in text-based tasks.

💡AMA

AMA stands for 'Ask Me Anything', which is a platform or command used in the context of the video to run AI models locally. It is used to interact with the Llama language model directly from the command line interface, allowing the user to chat with the model and test its capabilities before integrating it into more complex AI agents.

💡VS Code

VS Code is an abbreviation for Visual Studio Code, a popular source-code editor developed by Microsoft. It is mentioned in the script as the recommended integrated development environment (IDE) for writing the code necessary to create and control AI agents. The video instructs viewers to download VS Code for coding purposes.

💡Gro

Gro, in the context of the video, refers to an AI platform that allows for the execution of tasks with high performance. It is used to connect to a team of AI agents, providing the benefits of speed and efficiency. The video demonstrates how to use Gro to achieve 'super fast performance' with the AI agents, highlighting its importance in handling large-scale or complex tasks.

💡LLM

LLM stands for 'Large Language Model', which is a type of AI model designed to process and understand large volumes of language data. In the video, LLMs are used to rank language models like Llama, with the Llama 370b model being noted as superior to others like GPT-4.

💡API key

An API key is a unique identifier used in software development to authenticate requests to an API, or Application Programming Interface. In the script, the presenter guides viewers on how to create an API key for Gro, which is then used to connect and utilize the Gro platform's services within their AI agent project.

💡Email classifier

An email classifier is a type of AI agent designed to automatically categorize emails based on their content. In the video, the email classifier agent is given the goal of accurately classifying emails as important, casual, or spam. This is a practical application of AI agents in managing and sorting through large volumes of email.

💡Crew AI

Crew AI is a Python library used in the video for creating and managing AI agents. It provides functionalities for defining agents, tasks, and the crew that coordinates them. The script demonstrates using Crew AI to set up the workflow for the email classifier and responder agents.

💡Sequential process

A sequential process refers to a type of computational process where tasks are performed in a specific order, one after another. In the context of the video, the crew of AI agents is set to operate in a sequential manner, meaning that the email is classified before a response is generated.

💡Spam

Spam, in the context of email, refers to unsolicited, irrelevant, or inappropriate messages sent in bulk. The video uses the term in relation to the email classifier agent's task of identifying and sorting spam emails, highlighting the practical utility of AI in filtering out unwanted messages.

Highlights

David Andre demonstrates building AI agents using the Llama 3 model, suitable for those with limited programming knowledge.

AMA and VS Code are used to run the models locally, with Gro for super-fast performance.

The Llama 3 model achieves 216 tokens per second, showcasing its speed.

Llama 370b, an open-source model, is compared to be better than GBD4, indicating the future of AI.

A step-by-step workshop is available for non-programmers to learn how to build AI agents.

Downloading the Llama model for the first time is a one-time process, with the 8 billion model being recommended.

The process of installing necessary packages like 'llama' and 'crew AI' using pip in VS Code is detailed.

Building an email classifier as the first AI agent, which categorizes emails into important, casual, or spam.

The second agent is an email responder that writes responses based on the classification by the first agent.

The importance of setting the correct model and agent parameters within the crew AI framework is emphasized.

The agent's performance is tested with a Nigerian Prince scam email, successfully identifying it as spam.

An issue with the Llama 3 model's performance within the crew AI environment is encountered but later resolved.

Integration of the Gro API is shown for users with slower computers, highlighting the ease of setup.

The Gro API provides significant speed improvements, demonstrating the potential of cloud-based AI models.

The video concludes with an invitation to join a community for those serious about staying at the cutting edge of AI.

The importance of not missing out on the AI Revolution is stressed, urging viewers to engage with AI seriously.

The video provides a link to the community in the description for those interested in learning more about AI agents.

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