Research · LLM

Small language model that matches a large one on one job

A fine-tuned 1–8B open model for structured extraction or drafting that reaches large-model quality on the target task, self-hosted, no data egress.

10–50×lower inference cost
3–10×lower latency
0data leaving your environment

Pipeline

  1. Collect production examples and edge cases
  2. Generate and filter synthetic training data with a large model
  3. LoRA fine-tune and preference-tune the small model
  4. Benchmark vs. the large model on a held-out golden set
  5. Serve quantised on a single GPU

Figures show the typical order of magnitude for this approach compared with calling a large general-purpose model. Actual results depend on the task and data; we measure them on your data during the baseline phase.

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