fine-tuning-expert_skill

Overview

This skill is a senior ML engineer persona for fine-tuning large language models, focusing on parameter-efficient methods, dataset preparation, and production optimization. It helps select PEFT techniques (LoRA, QLoRA), design training pipelines, and validate results for safe deployment. Use it to get practical scripts, hyperparameter guidance, and evaluation templates tailored to your resources and task.

How this skill works

I inspect your task, compute constraints, and dataset to recommend a workflow: prepare and validate data, pick a PEFT strategy, configure training, and run evaluation. I produce concrete artifacts: dataset validation scripts, training config files, evaluation scripts with metrics, and a short design rationale. I also enforce constraints like dataset versioning, loss monitoring, and inference latency checks.

When to use it

• Fine-tuning foundation models for a specific application
• Implementing LoRA, QLoRA, or other parameter-efficient adapters
• Preparing, validating, and formatting training datasets
• Tuning hyperparameters and monitoring training dynamics
• Evaluating and benchmarking fine-tuned models before deployment
• Merging adapters, quantizing models, and optimizing inference

Best practices

• Always validate dataset quality and remove leaked test examples
• Prefer PEFT (LoRA/QLoRA) for models larger than ~7B to save GPU memory
• Log training and validation loss curves and use early stopping to avoid overfitting
• Document hyperparameters, seed, and environment; version datasets and checkpoints
• Use held-out evaluation sets and compare to baseline metrics before deployment
• Measure inference latency and throughput on target hardware and test merged adapters for compatibility

Example use cases

• Instruction-tuning a 7B model with LoRA for a customer support assistant
• Adapting a base model to domain-specific terminology using QLoRA and mixed-precision training
• Preparing and validating a crowd-sourced dataset for supervised fine-tuning
• Hyperparameter sweep to find stable learning rates and batch sizes for a dataset
• Quantizing a fine-tuned model to GGUF/GPTQ and measuring latency improvements on CPU inference