Evaluate Multiple LLMs Simultaneously Using Ollama on Runpod

In the rapidly expanding field of open-source text-generation models on Huggingface, the challenge is choosing the right model for specific use cases, with more than 100,000 models available. Ollama, a lightweight command-line interface, stands out by allowing multiple models to be loaded simultaneously for inference, unlike alternatives that handle only one model at a time. This feature enables users to evaluate multiple models' responses to the same prompts, providing insights into their effectiveness. The article underscores the importance of selecting an appropriate Large Language Model (LLM) based on factors such as parameter size, GPU requirements, and user satisfaction, while suggesting that larger, more resource-intensive models may not always be suitable for production use. It details the installation and evaluation process using ollama, highlighting the need for a nuanced approach to model evaluation involving diverse queries to ensure consistent performance. The article also emphasizes the significance of custom-designed questions and benchmarks tailored to specific use cases, offering examples in creative writing, coding, and logical reasoning to aid in model assessment. Ultimately, it invites users to experiment with LLMs, suggesting deploying an Ollama Pod on Runpod as a practical step towards refining model evaluation strategies.