How To Connect Llama3 to CrewAI [Groq + Ollama]
TLDRIn this informative video, the host guides viewers on how to utilize Llama 3 with Crew AI for creating Instagram content, specifically for advertising a smart thermos that maintains the temperature of coffee. The video is divided into three main sections: an introduction to Llama 3, its comparison with other language models, and a live demonstration; a tutorial on running a Crew using Llama 3 locally with Ollama; and finally, an exploration of enhancing the Crew's performance with Gro, allowing access to the larger 7 billion parameter version of Llama 3. The host also discusses the practical applications of Llama 3, such as generating catchy taglines and mid-journey descriptions for Instagram posts, and provides a source code for free download. Additionally, the video offers a troubleshooting community for coding issues and concludes with a demonstration of the Crew's ability to generate effective marketing content using the 7 billion parameter model of Llama 3, showcasing its speed and intelligence in completing complex tasks.
Takeaways
- 🚀 **Llama 3 Introduction**: Llama 3 is the third generation of Meta's open-source large language model, with significant improvements over its predecessor, Llama 2.
- 🔍 **Context Window**: A key enhancement in Llama 3 is the doubled context window, allowing it to process up to 8,000 tokens, making it comparable to Chat GPT 4.
- 🤖 **Cooperative Model**: Llama 3 is designed to be more cooperative, reducing instances where it would refuse tasks it previously could not perform.
- 📈 **Parameter Versions**: There are two versions of Llama 3 available: an 8 billion parameter model for local, faster tasks, and a 70 billion parameter model for more complex tasks.
- 💻 **Local Deployment with Olama**: Olama allows users to run Llama 3 locally on their computers for free, maintaining data privacy.
- 🌐 **Gro Integration**: For faster processing, Llama 3 can be integrated with Gro, which also provides access to the larger 70 billion parameter version of Llama 3.
- ⏱️ **Speed Comparison**: Llama 3 on Gro is significantly faster than using Chat GPT 4, with speeds up to 20 times faster for certain tasks.
- 📚 **Source Code Availability**: The presenter is offering the source code used in the tutorial for free, allowing viewers to skip setup and start experimenting.
- 👥 **Community Support**: A community has been created for users to post issues and receive help from the presenter or other developers.
- 📉 **Rate Limiting**: When using Gro with Llama 3, it's important to be aware of and manage rate limits to prevent the service from being restricted.
- 🎨 **Creative Output**: The video demonstrates how Llama 3 can be used to generate creative content, such as Instagram posts and mid-journey descriptions for advertisements.
Q & A
What is the main topic of the video?
-The main topic of the video is teaching viewers how to use Llama 3 with Crew AI to run their AI operations completely for free.
What are the three major parts covered in the video?
-The three major parts covered are: an introduction to Llama 3 and a live demo, running a Crew using Llama 3 locally on your computer with Ollama, and updating the Crew to work with Gro for faster operations and access to the larger Llama 3 model.
What is a key feature of Llama 3 compared to Llama 2?
-A key feature of Llama 3 is the doubled context window, which allows it to handle more complex tasks and makes it more comparable to other advanced language models like Chat GPT 4.
How does the video demonstrate the capabilities of Llama 3?
-The video demonstrates Llama 3's capabilities through a live demo and by using it to generate Instagram posts for a hypothetical smart thermos product, including text, taglines, and mid-journey descriptions for images.
What is the purpose of the Crew that is being run in the video?
-The purpose of the Crew is to generate content for advertising a product, specifically creating Instagram posts with text and images that can be used for marketing.
How does the video address the issue of running Llama 3 locally?
-The video addresses this by showing how to use Ollama to run Llama 3 locally, which allows for free and private use of the model on the user's own computer.
What is the significance of using Gro with Llama 3?
-Using Gro with Llama 3 allows for faster processing and access to the larger 7 billion parameter version of Llama 3, which is capable of more complex tasks.
What is the recommended starting point for someone new to Llama 3?
-The video recommends starting with the 8 billion parameter model of Llama 3, which is smaller, faster, and suitable for running locally on a personal computer.
How does the video provide support for viewers who may encounter issues?
-The video provides support by directing viewers to a free school community created by the presenter, where they can post questions and screenshots related to their code issues and receive help from the presenter or other developers in the community.
What is the source code's role in the tutorial?
-The source code is provided for free and allows viewers to skip the setup process. They can download it and start experimenting with the code to understand and utilize Llama 3 with Crew AI more effectively.
How does the video compare Llama 3 to other large language models?
-The video compares Llama 3 to other models like MistrAL and Jimena, showing that Llama 3 outperforms them in most aspects, especially with the 70 billion parameter model which is very competitive with state-of-the-art models like Chat GPT 4.
Outlines
🚀 Introduction to Llama 3 and Crew AI
The video introduces Llama 3, a third-generation large language model by Meta, and its capabilities when used with Crew AI for running tasks locally on a computer. It covers three main parts: understanding Llama 3, a live demo, running a Crew using Llama 3 locally, and transitioning to using Gro with Llama 3 for faster operations. The video also mentions the availability of source code for free and a community for support.
📚 Downloading and Setting Up Ollama
The paragraph explains the process of downloading and setting up Ollama, a tool for running large language models locally. It details the file sizes for different Llama 3 models and emphasizes the importance of knowing the model sizes for a developer. It also demonstrates the speed of Llama 3 on Gro Cloud and how to get started with Ollama by installing it, downloading the Llama 3 model, and interacting with it locally.
🤖 Customizing Llama 3 for Crew AI
This section describes how to customize Llama 3 to work with Crew AI by creating a model file that defines specific properties for the custom large language model. It guides through the process of using Ollama commands to create a new, specialized model for the Crew and setting up the environment for coding the Crew in Visual Studio Code.
💻 Environment Setup and Building Crews
The paragraph outlines the initial steps for setting up the environment for the Crew project using the `pyproject.toml` file and Poetry, a tool for Python dependency management. It explains how to install dependencies, activate the Poetry shell, and set up the Python virtual environment. The video also discusses the structure of the `main.py` file, which includes setting up the Crew, agents, and tasks.
🔌 Integrating Llama 3 with Crew Agents
The video script explains how to integrate Llama 3 with Crew agents by setting up the `self.llm` property within the agent classes to interact with the local Llama 3 model through the `chat_openai` library. It also emphasizes the importance of specifying the Llama 3 model to avoid defaulting to Chat GPT, which could incur costs.
⚙️ Setting Up and Running Crew with Gro
The paragraph demonstrates the process of setting up and running a Crew with Gro, which simplifies the substitution of different language models. It covers obtaining a Gro API key, updating the agent constructor to use `chat_grock`, and adjusting the Crew's rate limit to avoid being rate-limited by Gro's token per minute restrictions.
🎉 Conclusion and Final Outputs
The final paragraph showcases the results generated by the Crew using the 70 billion parameter model of Llama 3, which includes copywriting and mid-journey descriptions for an Instagram post about a smart, temperature-controlled coffee mug. The video concludes with the presenter's satisfaction with the results and an invitation for the audience to explore more AI content on the channel and join the community for further support.
Mindmap
Keywords
💡Llama 3
💡Crew AI
💡Ollama
💡Groq
💡Mid-Journey Descriptions
💡Parameter Version
💡Context Window
💡Token
💡Rate Limit
💡API Key
💡Local Language Model
Highlights
Introduction to Llama 3, a third-generation large language model developed by Meta, and its capabilities.
Live demonstration of Llama 3 showcasing its advanced AI capabilities.
Comparison of Llama 3 to other language models like Mistral and Jimma, highlighting its superior performance.
Explanation of the two versions of Llama 3: the 8 billion parameter model suitable for local use and the 70 billion parameter model for more complex tasks.
How to run a Crew using Llama 3 locally on your computer using Ollama, a tool for running large language models locally.
The process of generating Instagram posts for a smart thermos product using a Crew, including writing text and creating mid-journey descriptions.
The importance of mentioning that the Crew was built by the creator of Crew AI, Xiao, and has been formatted for the tutorial.
Transition to using Gro to run Crews faster and access the larger 7 billion parameter version of Llama 3.
Performance comparison of Llama 3 on Gro, showing its speed and efficiency compared to other models like Chat GPT 4.
Downloading and setting up Ollama on a local computer for private and free use of large language models.
Creating a custom large language model specialized for working with Crew AI using a model file.
Building and running a Crew in Visual Studio Code connected to Ollama for local execution.
Description of the two Crews set up for creating content and images for Instagram advertising.
How to set up environment and build Crews using Python and Crew AI's tools.
Using Llama 3 for smaller, quick tasks where it excels, and the recommendation to use it for tasks like file conversion.
Connecting the Crew to Gro for faster execution and accessing the larger Llama 3 model.
Addressing the rate limit issue when using Gro and the workaround by setting the maximum number of requests per minute.
Final results of the Crew using the 70 billion parameter model of Llama 3, generating high-quality Instagram post copies and mid-journey images.