Large language models (LLMs) become far more useful when adapted to specific tasks. Fine-tuning allows you to start from a pretrained model and specialize it with domain data.
Why Fine-Tune?
- Improve performance on your unique dataset
- Reduce inference cost by training a smaller model on a focused corpus
Basic Process
- Select a base model (e.g., GPT, Llama)
- Prepare a dataset of prompt-response pairs
- Train using a library such as Hugging Face Transformers
- Evaluate and iterate
Fine-tuning requires careful dataset curation and hyperparameter tuning, but it often yields dramatic improvements over using an out-of-the-box model.