Key Takeaway
By the end of this blueprint you will have a repeatable fine-tuning pipeline that curates and validates training data, orchestrates LoRA/QLoRA training jobs, evaluates checkpoints against standardized benchmarks, registers passing models in a versioned registry, and deploys them with canary rollouts and automatic rollback.
Prerequisites
- Python 3.11+ with PyTorch 2.x and the Hugging Face transformers library
- Access to GPU compute (cloud instances with A100/H100 or RunPod/Modal credits)
- A base model to fine-tune (Llama 3, Mistral, or Phi-3)
- Domain-specific data: at least 500 high-quality examples for LoRA fine-tuning
- Familiarity with training concepts: learning rate, epochs, loss curves
- W&B (Weights & Biases) or MLflow for experiment tracking
When to Fine-Tune vs. Prompt Engineer
Fine-tuning is not the default answer. It is expensive, requires curated data, and creates a model you must maintain. Reach for fine-tuning when prompt engineering has reached its ceiling: the model cannot follow your format consistently despite detailed instructions, the model lacks domain-specific vocabulary or reasoning patterns, you need to reduce inference costs by using a smaller model that matches a larger model's quality on your specific task, or you need to reduce latency by fitting the task into a model that runs on smaller hardware.
Try prompting and few-shot examples first. If you can get 80% of your target quality with prompting, you likely do not need fine-tuning. If you are stuck at 60% despite extensive prompt engineering, fine-tuning can often close the gap. The evaluation framework in this blueprint helps you measure exactly where you stand.
Data Curation Pipeline
Training data quality is the single largest determinant of fine-tuning success. The pipeline follows a four-stage process: collection (gathering raw examples from production logs, expert annotations, or synthetic generation), cleaning (deduplication, PII removal, format normalization), validation (schema checks, quality scoring, label verification), and splitting (train/validation/test with stratification on key attributes). Every dataset gets a version hash so you can reproduce any training run.
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