fine-tuning-expert

# 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: | Topic | Reference | Load When | |-------|-----------|-----------| | LoRA/PEFT | `references/lora-peft.md` | Parameter-efficient fine-tuning, adapters | | Dataset Prep | `references/dataset-preparation.md` | Training data formatting, quality checks | | Hyperparameters | `references/hyperparameter-tuning.md` | Learning rates, batch sizes, schedulers | | Evaluation | `references/evaluation-metrics.md` | Benchmarking, metri...