Build Anything with Llama 3 Agents, Here’s How

David Andre, in his video, guides viewers on building AI agents using the Llama 3 model, suitable even for those with limited computer resources or programming knowledge. He demonstrates the use of AMA for local model execution, VS Code for coding, and Gro for high-speed performance, achieving an impressive 216 tokens per second. Andre emphasizes the importance of creating AI agents to stay ahead in the field, offering a step-by-step workshop for beginners within his community. The video showcases building an email classifier agent and a responder agent using the Llama Free model. Despite initial issues with the Llama 3 model in the crew AI environment, Andre successfully connects to the Gro API, significantly improving the speed and performance of the AI agents. He concludes by encouraging viewers to join his community to stay at the forefront of the AI revolution.

Takeaways

• 🚀 **Building AI Agents**: David Andre demonstrates how to construct AI agents using the Llama 3 model, even without extensive computing resources or programming knowledge.
• 💻 **Local Model Execution**: AMA is used to run the models locally, while VS Code is utilized for writing code to achieve high performance.
• 📈 **Performance**: The script showcases the performance of the Llama model, highlighting speeds of 216 tokens per second.
• 📚 **Model Selection**: The Llama 3 model is chosen, with a recommendation for the 8 billion parameter version for its balance between speed and capability.
• 🔗 **Community Workshop**: A workshop is mentioned that teaches step-by-step AI agent construction, accessible through a link in the video description.
• 🔑 **Downloading the Model**: The process of downloading the Llama model is described, noting the size and time required for the download.
• 📝 **Coding with Python**: The script outlines the creation of a Python file and the necessary imports from the LangChain Community and Crew AI.
• 📧 **Email Classifier Agent**: An example of an AI agent is given, which classifies emails and generates responses based on their importance.
• 🔍 **Model Assignment**: Details on how to assign the Llama model to the agent within the code are provided.
• 🤖 **Agent Roles and Goals**: The script explains defining the roles and goals for the email classifier and responder agents.
• 🔗 **Community Resources**: The video emphasizes the value of joining a community for additional resources, such as code and prompts from the video.
• 🔄 **Troubleshooting**: The script describes encountering issues when running the model through Crew AI and the steps taken to resolve them.
• 🌐 **API Integration**: The process of adding the Grover API for improved performance on less powerful computers is outlined.
• 📈 **API Speed**: The speed improvement when using the Grover API is highlighted, emphasizing the potential of AI advancements.

Q & A

Who is the presenter in the video?

The presenter in the video is David Andre.

What is the main topic of the video?

The main topic of the video is how to build AI agents using the Llama 3 model with the help of AMA, VS Code, and Gro.

What are the tools mentioned to run the models locally?

The tools mentioned to run the models locally are AMA and VS Code.

What is the purpose of using Gro for performance?

Gro is used to achieve super fast performance when running AI models.

What is the size of the smaller Llama model?

The smaller Llama model is 8 billion parameters.

How long does it take to download the smaller Llama model?

It takes approximately 20 minutes to download the smaller Llama model.

What is the name of the platform where language models are ranked?

The platform where language models are ranked is called the LLM Arena.

What is the recommended model to use according to the video?

The recommended model to use according to the video is the Llama 3 8 billion model.

What is the first step to build AI agents as per the video?

The first step to build AI agents as per the video is to go to AMA's website, download the required components, and then use VS Code to write the code.

What is the name of the Python package used to import the Llama model?

The Python package used to import the Llama model is called 'llama' from the LangChain community.

What is the main issue encountered when running the Llama model through crew AI?

The main issue encountered is that the Llama 3 model does not seem to work well when running as an agent through crew AI, despite working perfectly in the terminal.

How can one connect Gro to their team of agents to get the benefits of high speed?

One can connect Gro to their team of agents by creating an API key from Gro Cloud, and then assigning the necessary environment variables in the code for the API base URL and the API key.

Outlines

00:00

🚀 Introduction to Building AI Agents with Llama Model

David Andre introduces himself and the video's aim: to guide viewers on building AI agents using the Llama model, despite potential limitations in computer hardware or lack of programming knowledge. The tools mentioned for this process are AMA for running models locally, VS Code for coding, and Gro for achieving high performance. The video showcases the speed of the Llama model, comparing it with a smaller version, and emphasizes the importance of building AI agents to stay competitive. David also mentions a workshop for non-programmers and provides a link in the video description.

05:01

🛠️ Setting Up the Development Environment

The video script outlines the steps to set up the development environment. It guides viewers to download AMA and VS Code, and then proceed to download the Llama model from the Llama website. The process includes instructions for using the terminal in VS Code to run the AMA and download the model. It also provides a tip for ending a chat session and emphasizes the need to download the model only once. The script then transitions into creating a Python file in VS Code and importing necessary packages for building AI agents.

10:04

📧 Building an Email Classifier AI Agent

David Andre decides to build an email classifier AI agent that can categorize emails and generate responses based on their classification. He details the process of creating the first agent, named 'email classifier', with the goal of accurately classifying emails as important, casual, or spam. The agent is given a backstory and configured with verbosity and delegation settings. The script then moves on to creating a second agent, the 'responder', which is tasked with writing responses based on the email's importance. The agents are assigned to tasks, and a 'crew' is defined to manage them. However, the script notes an issue with the Llama model's performance when run through crew AI, despite working well in the terminal. The video concludes with troubleshooting steps and a successful demonstration of the model's capabilities.

🔑 Integrating with Gro API for Enhanced Performance

The video script addresses a potential workaround for users with less powerful computers by integrating with the Gro API. It details the process of creating an API key on Gro Cloud and using it within the code to enhance the performance of the AI agents. The script demonstrates the significant speed improvement when using the Gro API and emphasizes the importance of staying updated with AI advancements. David Andre encourages viewers to join his community to stay at the forefront of AI development.

Keywords

💡AI agents

AI agents, in the context of the video, refer to autonomous systems or programs that perform tasks on behalf of a user. They are designed to simulate human behavior in order to complete specific objectives. In the video, David Andre demonstrates how to build AI agents using the Llama 3 model, which is a type of AI language model.

💡Llama 3 model

The Llama 3 model is an AI language model mentioned in the video that is used for building AI agents. It is distinguished by its ability to process and generate human-like text based on input data. The video focuses on using this model to create agents that can classify emails and respond to them.

💡AMA

AMA, which stands for 'Ask Me Anything', is a command-line tool used in the video to run the Llama 3 model locally. It is a part of the process for setting up the environment to work with AI agents and is used to download and manage the Llama model.

💡VS Code

VS Code, or Visual Studio Code, is a popular source-code editor used in the video for writing the code necessary to build AI agents. It is a development environment that supports various programming languages and provides features like debugging, Git integration, and syntax highlighting.

💡Gro

Gro, as mentioned in the video, is a tool or platform that is used to achieve super-fast performance when running AI models. It is integrated with the Llama 3 model to enhance the speed of the AI agents' operations, which is crucial for handling large volumes of data quickly.

💡Email classifier

An email classifier is an AI agent that categorizes emails based on their content. In the video, the first agent created is an email classifier, which receives an email and assigns it a label such as 'important', 'casual', or 'spam'. This classification is used to determine the response strategy of the second AI agent.

💡Crew AI

Crew AI is a software package used in the video for creating and managing AI agents. It provides a framework for defining the roles, goals, and behaviors of the agents. The video demonstrates how to use Crew AI to build an email classifier and a responder agent.

💡API key

An API key is a unique identifier used to authenticate a user, developer, or calling program to an API. In the context of the video, an API key is created for Gro to enable the use of its services for enhancing the performance of the AI agents. It is crucial for accessing and utilizing cloud-based AI services.

💡LLM (Large Language Model)

LLM, or Large Language Model, refers to advanced AI models that are designed to understand and generate human language at scale. The Llama 3 model is an example of an LLM, and the video discusses how to utilize such models to create functional AI agents for tasks like email classification.

💡Spam

In the context of the video, 'spam' refers to unwanted or unsolicited emails, often for malicious purposes like phishing scams. The email classifier agent is tasked with identifying such emails and categorizing them as 'spam' to help manage and filter out unwanted communications.

💡GitHub

GitHub is a web-based platform for version control and collaboration that is mentioned in the video as a source for official documentation and possibly code related to the Crew AI package. It is a common place to find and share code, as well as to contribute to open-source projects.

Highlights

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

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

Llama 3 achieves 216 tokens per second in performance benchmarks.

Llama 370b, an open-source model, outperforms GBD4, indicating a promising future for AI.

Building AI agents is crucial to not fall behind in the AI revolution.

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 necessary, with sizes ranging from 4.7 GB to 40 GB.

Quick tip: Use 'SL bu' to end a chat without killing the terminal.

Importing the Llama model and necessary packages from LangChain and Crew AI.

Creating a simple email classifier as the first AI agent task.

The email classifier agent accurately classifies emails as important, casual, or spam.

The email responder agent generates responses based on the email's classification.

Crew AI simplifies the creation of agents and tasks with predefined structures.

Llama 3 uses approximately 3 to 6 GB of RAM, making it accessible for most users.

Unexpected issues with Llama 3 when running through Crew AI, despite terminal success.

Adding the Gro API to utilize its speed and performance capabilities.

Gro API integration significantly improves speed and performance of the AI agents.

The AI revolution is happening now, and joining a community of AI enthusiasts can help one stay ahead.