Machine Learning & LLM Fine-Tuning

How it's built

(1) Data prep — collection, cleaning, train/eval splits. (2) Baseline — prompted baseline scores, eval set locked first. (3) Fine-tune — LoRA/QLoRA runs with hyperparameter sweeps. (4) Benchmark — against baseline and base model with regression checks. (5) Serve — quantize, deploy, monitor latency and drift.

Core fundamentals

• eval set locked before training (no cherry-picking)
• fine-tune only when prompting demonstrably fails
• regression-checked against the base model
• serving cost modeled before training starts

Build blueprint

Deliverables

• fine-tuned model
• eval report
• serving endpoint
• training pipeline

Stack

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