Parameter-Efficient Fine-Tuning (PEFT) Methods for Large Language Models: LoRA, QLoRA, AdaLoRA, SoRA, and Training Acceleration with Unsloth

This article systematically analyzes popular parameter‑efficient fine‑tuning (PEFT) techniques for large language models—including Adapter Tuning, Prefix Tuning, LoRA, QLoRA, AdaLoRA, and SoRA—detailing their principles, implementation code, experimental results on NLU tasks, and practical acceleration using the Unsloth library.

AdaLoRALarge Language ModelsLoRA

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Parameter-Efficient Fine-Tuning (PEFT) Methods for Large Language Models: LoRA, QLoRA, AdaLoRA, SoRA, and Training Acceleration with Unsloth

UCloud Tech

Oct 13, 2023 · Artificial Intelligence

How PEFT Transforms Large Model Fine‑Tuning: Additive, Prompt & LoRA Methods Explained

This article introduces parameter‑efficient fine‑tuning (PEFT) techniques—including additive adapters, soft‑prompt methods, selection‑based BitFit, and re‑parameterization approaches like LoRA and AdaLoRA—explains their architectures, experimental results, and provides end‑to‑end code for fine‑tuning ChatGLM2‑6B on a Chinese medical QA dataset.

AdaLoRAAdapterLoRA

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How PEFT Transforms Large Model Fine‑Tuning: Additive, Prompt & LoRA Methods Explained