Automated Local LLM Testing with LM Studio

Overview

This workflow automates the testing of multiple local Language Learning Models (LLMs) using LM Studio. It efficiently manages the process from receiving chat messages to analyzing model responses.

Key Features

• Dynamic Input Handling: The workflow dynamically processes inputs for different models, ensuring flexibility and adaptability.
• Time Tracking: It captures start and end times to calculate the time difference for each model run, providing insights into performance.
• Response Analysis: Metrics from LLM responses are analyzed to evaluate model effectiveness.
• Data Management: Results are saved to Google Sheets for easy access and further analysis.

Benefits

Automating the LLM testing process saves significant time and reduces manual errors. By capturing and analyzing response metrics, businesses can make informed decisions about model performance and improvements.

Use Cases

Ideal for AI researchers and developers who need to test and compare multiple LLMs efficiently. This workflow supports iterative testing and data-driven decision-making.

Integrations

• Google Sheets: For storing and managing test results.
• LM Studio: As the platform for running and managing LLMs.

Automation Benefits

This workflow streamlines the testing process, allowing for rapid iteration and analysis, ultimately leading to faster development cycles and improved model performance.