Fine-tuning takes a foundation model trained on broad data and specializes it for particular use cases. Methods include: full fine-tuning (updating all parameters, expensive but thorough), LoRA/QLoRA (Low-Rank Adaptation - training small adapter layers, efficient and popular), prompt tuning (learning soft prompts), and RLHF (Reinforcement Learning from Human Feedback, used to align models with human preferences). Fine-tuning requires less data and compute than training from scratch while achieving task-specific performance. Common applications include domain adaptation (legal, medical), instruction following, and custom assistant behavior.
🧠 AI & LLMs intermediate
Fine-tuning
Adapting a pre-trained model to specific tasks or domains by training on specialized data.
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</> Related Terms
LLM (Large Language Model)
AI models trained on massive text datasets to understand and generate human-like text.
Prompt Engineering
The practice of designing and optimizing inputs to AI models to achieve desired outputs.
Transformer
Neural network architecture using self-attention mechanisms, the foundation of modern LLMs like GPT and Claude.