Fail-case gallery (step 6/6) · fine-tune a small specialist

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Some production jobs still wobble after both of those levers: the same CI log gets labeled `flake` on Tuesday and `needs-human` on Thursday, even though the acceptance test never changed. Fine-tuning a **small specialist** is the lever for that wobble. A specialist is a narrow behavior — voice, format, or a three-way label — trained into a LoRA adapter on top of a small instruct model. The base weights stay shared. The adapter is the job. This chapter ships one living artifact: `specialist-adapter-receipt/` for a **CI-triage labeler** that marks a failing job as flake, product bug, or needs-human. That is the same triage surface you met in agent studio, now treated as a consistency problem instead of a tool-contract problem. The framing is adapted from Sai Rahul ([@sairahul1](https://x.com/sairahul1)) — [How To Fine-Tune a Small LLM on Your Own Data (Full Guide)](https://x.com/sairahul1/status/2100882424343265527) — into PromptDojo's judgment-and-receipt bar. Read his piece if you want the wider how-to. The unlock here is the gate, the splits, and the eval table, not a transcription of that writeup. ## Use this chapter when - One repeated job stays inconsistent under a strong prompt. - Retrieval already won or lost on the facts. The leftover problem is **how** the model writes or labels, not **what** it knows. - You can review a few hundred examples and spare a T4-class GPU hour. Skip the adapter when the brief is still weak, when the missing piece is a document that changes, when nobody will read the training rows, or when you want a generally smarter assistant. Fine-tune will not deliver that last one. ## What the lab actually grades The in-browser drills run in ordinary Python. They grade the **config and the receipt**, not a live GPU train. The Colab notebook under `specialist-adapter-receipt/` is the T4 path: Qwen2.5-1.5B-Instruct (or a peer in the 0.5–3B class), QLoRA via PEFT + bitsandbytes, TRL `SFTTrainer`, adapter export. 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