Lumina-Agent cover

Champion · Rank 1

Lumina-Agent is a data-centric, memory-aware end-to-end voice-command system designed for a strict 5 GB NPU memory limit and complex multi-turn interactions.

GitHub Presentation

My contribution

I worked on system deployment and memory optimization, including context compression, training/inference precision alignment, and reliable execution on the constrained NPU environment.

Highlights

  • Flat-Direct single-pass agent architecture to reduce latency and cascading errors.
  • Context compressed from roughly 20k to 5k tokens through semantic tool-description distillation.
  • 6k-context training on an RTX 3090 with gradient checkpointing, full-linear LoRA, and BF16.
  • A 20,000+ sample synthetic-data pipeline for multi-turn logic, concurrent commands, and anti-hallucination cases.

Final result: Rank 1, 100% on the local smoke test, and over 90% accuracy on complex logic cases.

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