Home-Trained 0.8B Decision Models Achieve 30ms Inference Speed

A new set of "Jeff" decision models, boasting 0.8 billion parameters, have been developed and trained entirely at home. These models are designed to be Jev-compatible, indicating a focus on efficient deployment and integration within existing frameworks.

The key achievement highlighted is the models' impressive inference speed, clocking in at approximately 30 milliseconds. This rapid processing capability suggests potential for real-time applications and efficient handling of complex decision-making tasks, even with a relatively modest parameter count.