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

Reference Guide

Load detailed guidance based on context: