What are the common methods of fine-tuning?
Fine-tuning involves adapting pre-trained language models (LLMs) to specific tasks or datasets using additional training. Common methods include full fine-tuning and parameter-efficient fine-tuning (PEFT) techniques.
Full fine-tuning updates all the model's parameters on the target task data but requires substantial computational resources. PEFT methods offer efficient alternatives: LoRA introduces low-rank matrices alongside original weights, Adapter modules insert small trainable layers between existing ones, and Prompt Tuning learns only task-specific soft prompt embeddings while freezing the core model. Key considerations are balancing performance gains against training cost, data availability, and hardware limitations.
These methods enable customization for enhanced performance in domains like finance (reports), medicine (notes), or customer support (chatbots). This improves accuracy, reduces prompt engineering effort, and facilitates deploying smaller, task-optimized models, accelerating the adoption of LLMs for specialized applications.
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