Meet QLORA: 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

Large language models (LLMs) may be improved via finetuning, which also allows for adding or removing desired behaviors. However, finetuning big models is prohibitively costly; for example, a LLaMA 65B parameter model consumes more than 780 GB of GPU RAM when finetuning it in standard 16-bit mode. Although more current quantization approaches can lessen the […]

The post Meet QLORA: An Efficient Finetuning Approach That Reduces Memory Usage Enough To Finetune A 65B Parameter Model On A Single 48GB GPU …

16-bit ai shorts applications artificial intelligence big editors pick example finetuning gpu language language model language models large language model large language models llama llms machine learning memory performance staff tech news technology usage