Build Multi-task Game-playing Policies From Mixed Sft Data Splits

NanoJev is a 0.6B Qwen3-based replica of Jev: a parallel decision model that returns probability distributions over supplied candidate actions with zero output-token decoding. Ships SFT training configs, an 18.7K-question dataset, an HTTP decision service and ViZDoom/Maze/Snake benchmarks.

AI Frameworks | Python · decision-model · parallel-decoding