1.0Lightning AIhttps://lightning.ai/pagesJP Hennessyhttps://lightning.ai/pages/author/jplightning-ai/Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adaptersrich600338<blockquote class="wp-embedded-content" data-secret="tjH73ewkOL"><a href="https://lightning.ai/pages/community/article/understanding-llama-adapters/">Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://lightning.ai/pages/community/article/understanding-llama-adapters/embed/#?secret=tjH73ewkOL" width="600" height="338" title="“Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters” — Lightning AI" data-secret="tjH73ewkOL" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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https://lightningaidev.wpengine.com/wp-content/uploads/2023/04/llama-adapter-code.png2094924In the rapidly evolving field of artificial intelligence, utilizing large language models in an efficient and effective manner has become increasingly important. Parameter-efficient finetuning stands at the forefront of this pursuit, allowing researchers and practitioners to reuse pretrained models while minimizing their computational and resource footprints. It also allows us to train AI models on... Read more »