LLMs Take on Decision-Making Roles: Jev and the Rise of Local AI Models

A new type of large language model (LLM) has emerged, one that’s specifically designed to make decisions rather than generate text. This shift in focus marks a significant departure from traditional LLM applications, where models are tasked with producing written responses or engaging in conversation. Instead, these decision-making models use input data to classify and categorize content quickly and efficiently.

The model in question is called Jev, which was released just weeks ago. According to its developers, Jev is an ultra-fast and extremely cheap solution for tasks that require classification and categorization. Unlike traditional LLMs, Jev can’t produce written text – not even a single sentence. Its output consists solely of floating-point numbers, which serve as the answers to user-specified yes/no questions or scoring-type requests.

Simon Willison provides a concise summary of what makes Jev so interesting: ‘It’s like having an LLM as a judge.’ This apt description highlights the model’s unique ability to make decisions based on input data. The idea has clearly caught on, with many developers experimenting with their own decision-making models and sharing their findings online.

Kev and Nimble are two notable examples of these local AI models. Nimble is particularly noteworthy, as it’s small enough to run locally without requiring significant computational resources. In fact, Nimble was recently added as a supported model in Ollama, making it even more accessible for developers who want to experiment with decision-making LLMs.

If you’ve successfully integrated one of these local AI models into your project and would like to share your experience, we’d love to hear about it. Please submit your story through our tips line.