Parameter-Efficient

Introduction Large language models (LLMs) have transitioned from research curiosities to production‑grade components that power chatbots, code assistants, search engines, and countless downstream applications. While the raw, pre‑trained weights are impressive, real‑world deployments rarely use a model “out‑of‑the‑box.” Companies and developers need to adapt these models to domain‑specific vocabularies, compliance constraints, or performance targets—a process commonly referred to as fine‑tuning. Fine‑tuning, however, is resource‑intensive. Traditional full‑parameter updates demand multiple GPUs, large batch sizes, and hours (or days) of compute. Parameter‑efficient fine‑tuning (PEFT) techniques such as LoRA, adapters, and prefix‑tuning dramatically reduce memory footprints and training time by freezing the majority of the model and learning only a small set of auxiliary parameters. ...

Introduction Large language models (LLMs) such as GPT‑4, LLaMA, and PaLM have demonstrated remarkable capabilities across a wide range of natural‑language tasks. Their raw performance, however, is often a starting point rather than a finished product. Real‑world applications typically require fine‑tuning—adapting a pre‑trained model to a specific domain, style, or task. Traditional fine‑tuning updates every parameter in the model, which can be prohibitively expensive in terms of compute, memory, and storage, especially when dealing with models that contain billions of weights. ...