I trained a model to be wrong 98% of the time and 96% sure about it. It took three tries.

A developer trained a model named Bev to be wrong 98% of the time, with 96% certainty, after three attempts. Bev works on Ollama's new decision endpoint (/v1/systemone) and is available with three GGUF quants under an Apache-2.0 license. The training took 3 hours and 38 minutes on a single 4090 GPU. The developer notes that Q4_K_M quantization changes 20 of Bev's 324 answers, making Q8_0 the default tag. Bev is intended as a joke and a test fixture, and users are warned not to rely on her for decisions.

This report details a unique model training experiment, focusing on intentionally creating a highly confident yet incorrect AI, unlike typical efforts to maximize accuracy.