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model-cascade

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taskdistill

Distil an expensive LLM API call on a narrow task into a small local model: capture traffic, curate, LoRA fine-tune with MLX on Apple Silicon, evaluate against the teacher, and serve an OpenAI-compatible cascade that escalates low-confidence requests.

  • Updated Sep 30, 2026
  • Python

LLM serving router that prices every request in dollars and milliseconds. Semantic cache plus a confidence-gated cheap-to-GPT-4 cascade: 96.9% lower cost than always calling the strong model, within 1.6 accuracy points, median latency 450ms to sub-ms on repeated traffic. FastAPI, SQLite cost ledger, reproducible benchmark.

  • Updated Jul 27, 2026
  • Python

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