A small language model specialist is a model that earns a narrow job through measured behavior. It does not need to outperform a frontier model everywhere. It needs to beat the chosen baseline on a defined target, stay inside regression and runtime limits, and route away when the task is outside its competence.
Pick the job
Good specialist targets have repeatable inputs, objective outcomes, enough representative data, and a runtime reason to stay local. Examples include tool selection, structured extraction, file operations, SQL generation, formatting, or a bounded intent router.
Establish the baseline
Run the unmodified base model on the frozen evaluation set before training. Record accuracy by slice, invalid outputs, latency, memory, and abstention or routing behavior when available. A specialist without a baseline has no demonstrated gain.
Choose the smallest intervention
Start with prompting or constrained decoding when it solves the problem. Use LoRA or QLoRA when behavior must be learned. Consider distillation when a stronger teacher can generate reviewed target behavior. Do not stack techniques until the failure requires them.
Protect breadth with routing
A specialist can regress on general tasks even while its target score improves. Add an explicit router or confidence boundary and evaluate it. The runtime should choose the specialist only for tasks it earned. Report a routed ship as routed, not as a new general default.
Package the whole result
A useful specialist package includes the base identity, adapter or fused artifact, tokenizer and prompt contract, routing rule, runtime requirements, license information, evaluation report, known regressions, and reproducible configuration. Missing latency or memory remains missing.
Make a decision
Use one of four honest outcomes: ship within the declared route, retry training, retry data or evaluation, or reject. Preserve failed attempts because they explain the next experiment and prevent the same recipe from being repeated without evidence.
PostTrainLLM is organized around this target-to-report loop. Its public file-operations specialist is a concrete example of a strong target gain paired with a disclosed breadth regression and a routing constraint.
CTA: Inspect the specialists and report cards, then follow the Mac fine-tuning workflow for your own target.