This paper is about making language models smaller and faster while still being able to do specific tasks well. Large language models (LLMs) like LLaMA are good at understanding and generating language but they are very large and take a lot of computer power to run. The authors of this paper present a method called Tailored-LLaMA that shrinks the size of LLaMA and fine-tunes it to perform well on specific tasks. First, they "prune" the model by removing parts that don't affect performance much. Then, they carefully choose prompts (instructions given to the model) that are specific to the task they want the model to perform. Finally, they use a technique called LoRA to quickly re-train the pruned model with the chosen prompts. The results show that even after shrinking the model by 50%, it can still perform well on tasks like answering questions and classifying text. This means Tailored-LLaMA could be a good way to make LLMs more accessible and affordable for people who don't have access to powerful computers.

https://arxiv.org/pdf/2410.19185