Donald Papp writes about Jev, a new class of model that takes text input but outputs only floating-point numbers, making it fast and cheap for classification tasks. Rather than generating sentences, it returns direct answers to yes/no questions, multiple-choice lists, and scoring requests. The concept has quickly gained traction, with developers already building their own decision-type models like Kev and Nimble.
- Jev outputs a confidence score for every answer, derived from the relative token probabilities
- Nimble is small enough to run locally and was recently added as a supported model in Ollama
- Simon Willison provided a concise summary of what Jev does
- The comment section sparked debate over whether this is truly novel, with some noting it's essentially an LLM with constrained outputs
- Jev outputs a confidence score for every answer, derived from the relative token probabilities
- Nimble is small enough to run locally and was recently added as a supported model in Ollama
- Simon Willison provided a concise summary of what Jev does
- The comment section sparked debate over whether this is truly novel, with some noting it's essentially an LLM with constrained outputs
