DeepSeek AI Researchers Propose Expert-Specialized Fine-Tuning, or ESFT to Reduce Memory by up to 90% and Time by up to 30%
What are large language models (LLMs) optimized for?
Large language models (LLMs) are optimized for natural language processing tasks, such as understanding and generating text, and can be tailored for specific applications using techniques like the Expert-Specialized Fine-Tuning (ESFT) method, which selectively fine-tunes relevant model components for improved efficiency and performance4.
Why is customization of LLMs challenging?
Customization of Large Language Models (LLMs) is challenging due to their large size, with billions of parameters, making them computationally expensive and time-consuming to fine-tune for specific tasks. Additionally, LLMs often require a significant amount of task-specific training data, and ensuring that the models maintain their general capabilities while being customized for specific tasks can be difficult.
What is parameter-efficient fine-tuning (PEFT)?
Parameter-efficient fine-tuning (PEFT) is a technique used in natural language processing to improve the performance of pre-trained language models on specific downstream tasks5. It involves freezing some of the layers of the pre-trained model and only fine-tuning the last few layers that are specific to the downstream task5. This approach reduces computational costs and overcomes issues of catastrophic forgetting, making it a more efficient alternative to full fine-tuning3.