Foundation Model Fine-Tuning — Adapt Models to Your Domain

I will fine-tune a foundation model on your domain data — using LoRA for parameter-efficient adaptation of an open-weight model (Llama 3, Mistral, or Qwen) or the OpenAI fine-tuning API for GPT-3.5 — to adapt the model's behaviour, vocabulary familiarity, and output style to your specific domain without the cost of training from scratch. Fine-tuning a foundation model is appropriate when prompting alone cannot reliably elicit your desired output style or domain accuracy — the right decision point is when you've optimised prompts and few-shot examples but still see systematic failures on your domain-specific test cases that additional prompt engineering doesn't fix.

The fine-tuning covers dataset preparation, LoRA configuration for your model and task, training job execution with validation monitoring, evaluation against a held-out domain test set, comparison against the prompted baseline, and model export for inference.