Peft Fine Tuning
원문: peft-fine-tuning
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integ
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PEFT (Parameter Efficient Fine Tuning) Fine tune LLMs by training <1% of parameters using LoRA, QLoRA, and 25+ adapter methods. When to use PEFT Use PEFT/LoRA when: Fine tuning 7B 70B models on consumer GPUs (RTX 4090, A100) Need to train <1% parameters (6MB adapters vs 14GB full model) Want fast iteration with multiple task specific adapters Deploying multiple fine tuned variants from one base model Use QLoRA (PEFT + quantization) when: Fine tuning 70B models on single 24GB GPU Memory is the primary constraint Can accept ~5% quality trade off vs full fine tuning Use full fine tuning instead when: Training small models (<1B parameters) Need maximum quality and have compute budget Significant…
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