Fine-tuning is best used when you need to change the behavior, tone, or specific formatting of a model (e.g., generating JSON output perfectly every time). If you simply need the model to know new information, Retrieval-Augmented Generation (RAG) is usually faster and more reliable.
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Fine-Tuning (PEFT, LoRA)
Fine-tuning adjusts the internal weights of a model so it performs better on a targeted task, like writing SQL queries or understanding medical terminology. Parameter-Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) are modern techniques that allow developers to fine-tune massive models quickly and cheaply without retraining every parameter.
CategoryModels
Reading time4 min read
Last updatedFeb 19, 2025
Definition
The process of taking a pre-trained model and training it further on a smaller, specialized dataset to adapt it to specific tasks or domains.
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PEFT and LoRA
Hugging FaceInstead of updating all billions of parameters, PEFT/LoRA techniques freeze the original model and only train a small "adapter" module. This makes fine-tuning accessible to teams without massive GPU clusters.
FAQ
Is fine-tuning the same as RAG?
No. Fine-tuning bakes knowledge into the model’s weights, like teaching someone to speak Spanish. RAG gives the model an open textbook at query time without changing its weights.
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