# 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.

Source: https://aibyos.com/research/slm

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

**0**data 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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## Typical tooling

-   Llama / Qwen / Mistral
-   TRL / Unsloth
-   vLLM
-   Sovereign GPU

## Related

[All research](https://aibyos.com/research)[Sovereign GPU compute](https://aibyos.com/gpu)[Fine-tuning](https://aibyos.com/services/fine-tuning)