Power Each AI Agent With A Different LOCAL LLM (AutoGen + Ollama Tutorial)
TLDRThe video script demonstrates how to use Autogen, an open-source tool powered by Olama, to run multiple models locally on any modern machine without needing a supercomputer. It guides viewers through installing Olama, downloading models like Mistol and Code Llama, and setting up an environment with Conda to use Autogen and Light LLM. The tutorial showcases creating agents, such as a general assistant and a coding agent, and orchestrating their tasks through a group chat setup. The script highlights the flexibility and potential of Autogen for various applications.
Takeaways
- 🌐 Autogen is a tool that allows users to run open-source models locally without the need for a superpowered computer.
- 🔌 The tutorial demonstrates how to use Olama to power models locally and Light LLM to create an API endpoint for each model.
- 📈 Autogen has received numerous updates, and the video provides links to various tutorials for different levels of users.
- 🔄 The process involves downloading models like Mistol and Code Llama using Olama's command-line interface.
- 🚀 Multiple models can be run simultaneously, with each model powering a different agent tailored to specific tasks.
- 📝 The video shows how to create a configuration list for each model and how to set up agents with their respective configurations.
- 🔧 The setup includes creating a user proxy and a group chat manager to coordinate interactions between agents.
- 🎯 The demonstration includes testing the system by asking the agents to tell a joke and write a Python script.
- 🛠️ The video emphasizes the flexibility of using different models for various tasks, such as coding or creative writing.
- 📋 The script provided in the video can be copied and pasted for users to follow along and experiment with the setup.
- 💡 The video encourages users to provide feedback and share their use cases for Autogen, especially if they have code to contribute.
Q & A
What is the main purpose of the autogen tool discussed in the transcript?
-The main purpose of autogen, powered by olama, is to enable the use of any open-source model and run it locally on a modern machine without the need for a superpowered computer.
How does olama contribute to the process of running models locally?
-Olama powers the models locally, allowing users to download and run multiple models simultaneously without the need for a high-performance computer.
What is the role of light llm in the setup described?
-Light llm is used to wrap the model, providing an API endpoint that can be utilized by autogen to interact with the model.
What are some of the models mentioned in the transcript that can be used for specific tasks?
-Some of the models mentioned include mistol for orchestration, cod llama for coding, and other specialized models for tasks like creative writing, SQL writing, and more.
How does the user interface with the olama models?
-The user interfaces with the olama models through the command line, using specific commands to download, run, and manage the models.
What is the significance of the ability to run multiple models simultaneously?
-The ability to run multiple models simultaneously allows for the creation of agents that can be powered by fine-tuned, specialized models that excel at specific tasks, enhancing the overall functionality and efficiency of the system.
How does the user ensure that the correct Python environment is being used for the autogen setup?
-The user can ensure the correct Python environment is being used by checking the output of 'which python' command within the activated conda environment for autogen.
What is the role of the user proxy agent in the group chat setup?
-The user proxy agent in the group chat setup represents the human user, managing interactions with the other agents and executing tasks based on user input.
How can the user optimize the performance of the open-source models?
-The user can optimize the performance of the open-source models by adjusting termination messages, fine-tuning the models for specific tasks, and experimenting with different configurations to achieve the desired results.
What is the process for troubleshooting if the autogen library appears to be unavailable?
-If the autogen library appears to be unavailable, the user should ensure that the conda environment for autogen is activated. This can often resolve issues related to library recognition and availability.
What is the significance of the group chat manager in the autogen setup?
-The group chat manager in the autogen setup coordinates the interactions between different agents within the group chat. It ensures that tasks are assigned and managed effectively across the various agents.
Outlines
🌟 Introduction to Autogen and Olama
The video begins with an introduction to autogen, a tool powered by olama, which enables the use of open-source models locally on any modern machine without the need for a supercomputer. The speaker mentions that autogen has received numerous updates since their last video and provides a link to previous tutorials for beginners to experts. The process of using autogen involves three components: autogen itself, olama for local model powering, and light llm to create an API endpoint for the models. The dream of open-source agent usage is to have each agent powered by a specialized, fine-tuned model that excels in specific tasks.
🛠️ Setting Up the Environment and Models
The speaker proceeds to demonstrate the setup process for autogen, starting with the installation of olama. They show how easy it is to download and install olama, which runs from the command line without a graphical interface. The speaker then installs two models using olama: mistol as the orchestration model and cod llama for coding. They explain how to download the models using a simple command and discuss the impressive ability of olama to run multiple models simultaneously. The speaker also provides a brief demonstration of the mistol model's speed and responsiveness on their MacBook Pro M2 Max.
🔧 Configuring Autogen and Light LLM
In this section, the speaker guides the audience through the installation of autogen and light llm, which provides a wrapper around olama to expose an API for use with autogen. They show how to install both components using pip and verify the Python environment. The speaker then explains how to create a configuration list for the models, detailing the process of setting up local model URLs for both mistol and cod llama. They also demonstrate how to create two agents, one for general tasks using mistol and another for coding tasks using cod llama, and how to set up a user proxy to interact with these agents.
🎬 Testing the Setup and Agent Interaction
The speaker tests the setup by creating a group chat with the agents and a user proxy, and initiates a conversation with a simple task of telling a joke. They discuss the importance of optimizing termination messages for the models and the need for customization. The speaker then attempts a more complex task where the user proxy agent generates a random number and asks the coder agent to output numbers from 1 to that number. Although the initial attempt does not work as expected, with some adjustments and a cache clear, the speaker successfully demonstrates the interaction between the agents, showing the coder agent writing a script and the user proxy agent executing it.
🚀 Conclusion and Future Plans
The speaker concludes the video by summarizing the successful demonstration of individual models powering separate agents. They encourage viewers to provide feedback and share real-world use cases for autogen in the comments or on Discord. The speaker also mentions their plans for an expert video and expresses interest in collecting and showcasing the best practical applications of autogen from the community.
Mindmap
Keywords
💡Autogen
💡Olama
💡Light LLM
💡Model Fine-Tuning
💡Agent
💡API Endpoint
💡Conda
💡Group Chat
💡User Proxy Agent
💡Environment Setup
Highlights
Introduction to autogen, a tool that uses open-source models and runs them locally on any modern machine.
Use of olama to power models locally, eliminating the need for a superpowered computer.
Integration with light llm to wrap the model and provide an API endpoint for use.
Capability to run multiple models simultaneously, each powering individual agents.
Example of using a specialized model like code llama for coding tasks.
Demonstration of olama's ability to run multiple models at once and manage them efficiently.
Downloading and using the mistol model as the main orchestration model.
Installation and setup process of olama and light llm for local model usage.
Creating a Python environment with conda and installing autogen and light llm within it.
Configuration of local model URLs and API setup for autogen to utilize the models.
Creating agents with specific tasks, such as a general assistant and a coding agent.
Utilizing group chat functionality to manage interactions between multiple agents and the user proxy.
Execution of a task that involves the collaboration of different agents, each powered by a separate model.
Testing the system with a joke-telling task and a Python script writing task.
Observation of the models' ability to handle tasks and terminate correctly.
Discussion on optimizing autogen for better performance with open-source models.
Final demonstration of the system working with separate models for user proxy and coding tasks.