Examples
Worked examples
- Is an instance
LoRA fine-tuning of Llama 3 on a corpus of cardiology guidelines to build a domain assistant
Counter-examples
Looks similar, but isn't
- Not an instance
Few-shot prompting (showing examples in the prompt) is not fine-tuning — model weights are unchanged
Editorial commentary
Fine-tuning is the process of further training a pre-trained foundation model on a smaller, task-specific or domain-specific dataset, updating some or all of its parameters, to specialise its behaviour while retaining the general capability learned during pre-training. Common methods include full-parameter supervised fine-tuning (SFT), parameter-efficient methods such as LoRA (which train a small set of additional weights rather than the whole model), and preference-optimisation methods (RLHF, DPO) that adjust a model’s outputs toward human-preferred responses rather than toward a fixed labelled dataset.
Why the method matters, not just the fact of fine-tuning
“This model was fine-tuned” is an incomplete disclosure. The method determines what changed: full-parameter SFT can alter behaviour broadly, LoRA constrains the change to a narrow, cheaply reversible subspace, and RLHF/DPO change output preferences rather than factual knowledge. A fine-tune lineage record is the artefact that should capture which method was used, on what data, with what hyperparameters.
Why it matters for research
A fine-tuned model built on licensed or restricted training data inherits use restrictions from that data even where the base model itself is unrestricted — a distinction that matters directly for whether the fine-tuned model, or research results derived from it, can be shared or published.
References
- See also: RLHF, Large language model (LLM), Model lineage.
Also known as
Model adaptation · Domain adaptation (LLM)
Machine-readable encodings
Use in your systems
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