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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…
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:
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