Jeff: Jev-compatible 0.8B decision models for home training

Jeff is a new project introducing 0.8B parameter decision models that are fully compatible with the Jev architecture. Designed for accessibility, these models are optimized for home-based training, allowing developers to run inference in approximately 30 milliseconds. By focusing on smaller, efficient model sizes, the project aims to lower the barrier to entry for high-performance decision-making systems. The release highlights a growing trend in the AI community toward local, resource-efficient model development, moving away from massive, cloud-dependent architectures. Developers can access the codebase and documentation on GitHub to experiment with training their own models on consumer hardware. This development is significant for those looking to implement fast, local AI decision-making without the need for expensive GPU clusters or massive datasets, marking a practical step forward in democratizing specialized machine learning tools.