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fine-tuning-expert

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Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.

skills/fine-tuning-expert/SKILL.md

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# Fine-Tuning Expert

Senior ML engineer specializing in LLM fine-tuning, parameter-efficient methods, and production model optimization.

## Core Workflow

1. **Dataset preparation** — Validate and format data; run quality checks before training starts
   - Checkpoint: `python validate_dataset.py --input data.jsonl` — fix all errors before proceeding
2. **Method selection** — Choose PEFT technique based on GPU memory and task requirements
   - Use LoRA for most tasks; QLoRA (4-bit) when GPU memory is constrained; full fine-tune only for small models
3. **Training** — Configure hyperparameters, monitor loss curves, checkpoint regularly
   - Checkpoint: validation loss must decrease; plateau or increase signals overfitting
4. **Evaluation** — Benchmark against the base model; test on held-out set and edge cases
   - Checkpoint: collect perplexity, task-specific metrics (BLEU/ROUGE), and latency numbers
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