QLoRA
Efficient Finetuning of Quantized LLMs
April 20th, 2024
About QLoRA
QLoRA is an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. It backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters (LoRA).
Key Features
- Efficient finetuning of quantized language models.
- Reduces memory usage for finetuning large models.
- Preserves task performance during finetuning.
- Uses 4-bit quantized pretrained language models.
Use Cases
- Finetuning large language models with limited GPU memory.
- Improving task performance during finetuning.
- Efficient training of language models.
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