Fine-Tuning

Fine-tuning is a transfer learning technique where a pre-trained model (already trained on a large general dataset) is further trained on a smaller, domain-specific dataset. This adapts the model’s knowledge to perform better on specialized tasks.

Types of fine-tuning:

• Full Fine-Tuning: All model parameters are updated on the new dataset
• Parameter-Efficient Fine-Tuning (PEFT): Only a small subset of parameters are updated (e.g., LoRA, adapters)
• Instruction Fine-Tuning: Training the model to follow instructions using example pairs

Fine-tuning is widely used to customize LLMs for specific use cases like customer support chatbots, code assistants, medical diagnosis, and legal document analysis. It requires significantly less data and compute than training from scratch.