AutoGen Bench - The Ultimate Guide to AI Agent Model Selection (Ollama, Groq)
AI Summary
Summary: Autogen Bench for AI Model Evaluation
- Introduction to Autogen Bench
- Autogen Bench is a tool for evaluating AI models.
- It helps determine the best model for running AI agents like Autogen or Crew AI.
- The tool simplifies the process of testing different models.
- Testing Process
- Models tested include GP4, Mistal, Code Llama, Mixt Llama 270b, and JMA 7B.
- Tests are run using OpenAI API for GP4, O Llama for Mistal and Code Llama, and Gro for Mixt Llama 270b and JMA 7B.
- The tool uses a human eval dataset with prompts to feed to agents.
- Results show the number of successes and failures for each model.
- Key Aspects of Autogen Bench
- Repetition: Running the same test multiple times.
- Isolation: Running agents in a dedicated container environment.
- Instrumentation: Logging the behavior of each agent step by step.
- Tutorial Overview
- The video creator offers a step-by-step guide on using Autogen Bench.
- They encourage viewers to subscribe to their YouTube channel for more AI-related content.
- Steps for Using Autogen Bench
- Install Autogen Bench and clone the human eval dataset.
- Configure the model to be tested in the oi_config_list file.
- Run tests against the tasks in the human eval dataset.
- Repeat tests multiple times in a Docker container, logging results.
- Review results in the human eval folder to determine model performance.
- Results and Conclusion
- GP4 Turbo performed the best, followed by Mixt Llama 270b and others.
- Detailed results and configurations are available on the creator’s website and GitHub repo.
- The creator plans to make more videos on similar topics.
- Final Notes
- The video includes instructions for integrating other models like Grock and Olama.
- Security precautions are advised when testing with Olama.
- The creator expresses excitement about the tool and its capabilities.