fine-tuning-expert

SKILL.md

Expert guidance for fine-tuning LLMs with parameter-efficient methods and production optimization.

Covers LoRA, QLoRA, and full fine-tuning workflows with Hugging Face PEFT, including dataset validation, hyperparameter configuration, and adapter merging for deployment

Provides a complete minimal working example with LoRA setup, training loop, and quantization variants for memory-constrained environments

Includes five-stage workflow: dataset preparation, method selection, training with checkpoints, evaluation against base model, and production deployment with quantization

Enforces best practices through explicit constraints: mandatory data validation, parameter-efficient methods for large models, loss curve monitoring, and held-out set evaluation before serving

Fine-Tuning Expert

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

Core Workflow

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

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

Training — Configure hyperparameters, monitor loss curves, checkpoint regularly

Checkpoint: validation loss must decrease; plateau or increase signals overfitting

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

Deployment — Merge adapter weights, quantize, measure inference throughput before serving

Reference Guide

Load detailed guidance based on context: