Researchers Develop a More Efficient Way to Fine-Tune Large Language Models for Long Text Sequences

Researchers from CUHK and MIT have introduced LongLoRA, an innovative method to efficiently fine-tune large language models (LLMs) for extended text contexts. Addressing the computational challenges of traditional techniques, LongLoRA combines the Shift Short Attention (S2-Attn) mechanism for effective data subgroup information sharing and an enhanced low-rank adaptation method, LoRA, to process longer sequences. Remarkably efficient, LongLoRA can fine-tune models with up to 100,000 tokens on a standard machine, integrates seamlessly with current AI technologies, and retains compatibility with techniques like FlashAttention-2.

Read the full story — https://news.superagi.com/2023/09/22/researchers-develop-a-more-efficient-way-to-fine-tune-large-language-models-for-long-text-sequences/