Build Anything with Llama 3 Agents, Here’s How
TLDRDavid Andre's video tutorial guides viewers on constructing AI agents using the Llama 3 model, suitable for those with limited programming knowledge or suboptimal computer capabilities. Andre introduces the use of the AMA framework for local model execution, Visual Studio Code for coding, and the Gro API for enhanced performance. He demonstrates the process of downloading the Llama model, setting up the environment, and writing Python code to create an email classifier agent and a responder agent. Despite initial issues with the Llama model's integration with the Crew AI library, Andre successfully resolves the problem by utilizing the Gro API, achieving remarkable speed and performance. The video concludes with an invitation to join Andre's 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 for those with limited computer resources or programming knowledge.
- 💻 **Local Model Execution**: AMA is used to run the models locally, while VS Code is utilized for writing the code, and Gro for achieving high performance.
- 📈 **Performance Speed**: The script showcases the speed of token processing, highlighting the capabilities of both the large and smaller Llama models.
- 📝 **Model Selection**: The Llama 3 model is chosen for its performance, with a recommendation to use the 8 billion parameter model for better results.
- 🔧 **Setup Process**: A step-by-step guide is provided for downloading the model, setting up the environment in VS Code, and running the model for the first time.
- 📚 **Learning Resources**: A workshop is mentioned for those who wish to learn how to build AI agents, accessible through a community link provided in the video description.
- 📧 **Email Classifier Example**: An example project is discussed, where the first agent classifies an email, and the second agent writes a response based on the classification.
- 🔍 **Agent Roles and Goals**: The script details the creation of agents with specific roles and goals, such as accurately classifying emails and responding to them.
- 🛠️ **Coding with Crew AI**: The use of Crew AI for importing necessary modules and creating agents and tasks within a Python environment is explained.
- 🔗 **API Integration**: The process of connecting Gro API for improved performance on lower-end computers is demonstrated, showcasing the significant speed increase.
- 🤖 **Agent Delegation**: The decision to not allow agent delegation is mentioned to keep the task handling within the designed agents.
- ⚙️ **Crew Configuration**: The configuration of a crew in Crew AI is outlined, including assigning agents, defining tasks, and setting up the process for execution.
Q & A
What is the name of the person demonstrating how to build AI agents in the transcript?
-The name of the person is David Andre.
What are the three main tools David Andre mentions for building AI agents?
-The three main tools mentioned are AMA, VS Code, and Gro.
What is the name of the large language model that David Andre suggests using?
-David Andre suggests using the 'llama fre model'.
How does David Andre propose to run the models locally?
-David Andre proposes to run the models locally using AMA.
What is the name of the smaller model that David Andre tests in the transcript?
-The smaller model that David Andre tests is called 'Llama free 8 billion'.
What is the purpose of the first agent David Andre plans to build?
-The purpose of the first agent is to receive a random email and classify it based on its importance.
What is the role of the second agent in David Andre's example?
-The role of the second agent is to write a response based on the classification made by the first agent.
What is the name of the file David Andre creates in VS Code for the Python script?
-The name of the file is 'main.py'.
What package does David Andre install to use the Llama model in the script?
-David Andre installs the 'Lang' chain community package by running 'pip install crew AI'.
What is the issue David Andre encounters when trying to run the Llama model through crew AI?
-David Andre encounters an issue where the Llama model does not seem to work well when running as an agent through crew AI, despite working perfectly in the terminal.
How does David Andre resolve the issue with the Llama model not working well through crew AI?
-David Andre resolves the issue by assigning the necessary environment variables for the Gro API and using it instead of the AMA for running the model.
What does David Andre emphasize as crucial for staying ahead in the AI field?
-David Andre emphasizes surrounding oneself with people who are at the cutting edge of AI and continuously building and experimenting with AI agents.
Outlines
😀 Introduction to Building AI Agents with Llama Model
David Andre introduces himself and presents a tutorial on creating AI agents using the Llama model, which is a new and open-source alternative to established models like GPT-4. Despite potential limitations in computer capabilities and programming knowledge, the video guides viewers through the process of using AMA to run the models locally, VS Code for coding, and Gro to achieve high performance. The video showcases the speed of the Llama model and encourages viewers to build AI agents to stay competitive in the evolving AI landscape. David also mentions a workshop for non-programmers and provides a link to his community in the video description.
👨💻 Setting Up the Development Environment and Model
The video script outlines the initial steps for setting up the development environment, which includes downloading AMA and VS Code, and then obtaining the Llama model. It emphasizes the choice between the larger 40 GB model and the smaller 4.7 GB model, noting the download time for each. The script provides instructions for installing necessary packages using pip and setting up the environment in VS Code. It also details the process of importing the Llama model and creating a Python file for coding the AI agents.
📧 Building Email Classifier and Responder Agents
The script describes the creation of two types of AI agents: an email classifier and a responder. The email classifier's role is to categorize emails as important, casual, or spam, while the responder's task is to write a reply based on the email's classification. The agents are set up with specific roles, goals, and backstories. The script also covers setting verbosity for debugging purposes and disabling delegation. It outlines the process of defining tasks for the agents, creating a crew that includes the agents and their tasks, and running the crew to see the agents in action. The video also addresses a potential issue with the Llama model's performance within the crew AI environment and suggests using the Gro API for better performance.
🚀 Integrating with Gro API for Enhanced Performance
The final paragraph details the integration of the Gro API to improve the performance of the AI agents, particularly for those with less powerful computers. It guides viewers through the process of creating an API key in Gro Cloud, configuring the necessary environment variables, and adjusting the code to use the Gro API instead of the local Llama model. The script highlights the significant speed improvement when using the Gro API and emphasizes the importance of being part of a community at the forefront of AI to fully participate in the AI revolution. It concludes with an invitation to join David Andre's community for further learning and collaboration.
Mindmap
Keywords
💡AI agents
💡Llama 3 model
💡AMA
💡VS Code
💡Gro
💡Tokens per second
💡Email classifier
💡API key
💡Crew AI
💡Sequential process
💡Community
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 for running models and writing code, respectively, to achieve high performance.
Llama 3 model achieves an impressive 216 tokens per second in performance.
Llama 370b, an open-source model, is shown to be superior to GBD4 in the LM arena.
The importance of building AI agents to stay ahead in the competitive landscape is emphasized.
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 requires a one-time setup that can take up to 20 minutes for the smaller model.
The process of installing the necessary packages and setting up the environment for running the Llama model is detailed.
An example of creating a simple email classifier agent and an email responder agent is provided.
The email classifier agent accurately classifies emails as important, casual, or spam.
The email responder agent writes concise responses based on the email's classification.
A method to connect the Gro API for improved performance on lower-end computers is explained.
The integration of the Gro API significantly increases the speed of AI agent operations.
The Llama model works well in the terminal but encounters issues when run as an agent through crew AI.
A solution to the compatibility issue with crew AI is found by assigning specific environment variables.
The community created by David Andre aims to bring together individuals serious about AI to stay ahead of the AI revolution.
The potential of the AI revolution and the need to adapt to stay competitive is discussed.
