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Power Each AI Agent With A Different LOCAL LLM (AutoGen + Ollama Tutorial)

Matthew Berman2023-11-29
ollama#autogen#pyautogen#litellm#agents#ai agents#ai#open-source ai#open-source#artificial intelligence#local llm#local agents
81K views|7 months ago
💫 Short Summary

The video showcases using autogen with olama to run multiple open-source models locally, enhancing performance and efficiency. It covers downloading various AI models, setting up environments, and activating autogen for multiple model usage. The segments demonstrate creating specialized agents for coding tasks, coordinating interactions through group chat, and successfully executing scripts with Mistol and Code Llama models. The importance of testing and refining AI projects is emphasized for optimal performance. The video concludes by highlighting the real-world applications of autogen, particularly for coding, and encourages viewer engagement for future content.

✨ Highlights
📊 Transcript
Using autogen powered by olama to run open-source models locally.
Users can connect individual agents to different models for specialized tasks like coding or creative writing.
The process involves installing olama, downloading models like mistol and Cod llama, and running them concurrently.
Running multiple models simultaneously allows for seamless swapping and improved performance.
The video highlights the efficiency and speed of utilizing multiple models for various tasks.
Testing various AI models through Olama.
Includes models such as Find Model, Deep Sea Coder, Orca 2, STAR Coder, Dolphin 2.2, and more.
Process involves downloading models, writing Python scripts, setting up environment with Conda, and installing Autogen and Light LLM.
Demonstrates activating environments, checking Python versions, and loading models for use.
Successfully runs Uvicorn at Local Host Port 8000 and loads multiple models to work simultaneously.
Activating autogen feature for running multiple servers and models simultaneously using Ollama.
Import autogen in a Python file to begin the process.
Create a config list for different models such as Mistal and Code Llama.
Set up the base URL and API key for the models.
Configure the LLM parameters for each model to create multiple assistants with specific configurations for different tasks.
Setting up multiple agents using different models and coordinating them through group chat.
Utilizing the mistal model for an assistant agent and the code llama model for a coding agent.
Creating a user proxy agent and optimizing settings for open source models.
Emphasizing the importance of correctly terminating tasks and experimenting with settings.
Concluding with initiating a chat task to test the setup, activating autogen, and addressing potential library availability issues.
Successful execution of scripts by models Mistol and Code Llama with user proxy and coder agents.
Models generate and run Python scripts to output numbers 1 to 100, demonstrating effective collaboration between models and agents.
Initial challenges like incorrect execution overcome by adjusting human input mode to 'never', resulting in successful script execution.
Importance of testing and refining AI projects for optimal performance emphasized in the segment.
Viewer feedback invited for future content development.
Real-world use cases of Autogen, particularly for code, are highlighted.
Viewers are invited to share their code in the video description or Discord channel.
The audience is encouraged to like, subscribe, and stay tuned for future content.