Tailored LLaMA Optimizing Few Shot Learning in Pruned LLaMA Models with Task Specific Prompts

This research paper proposes a new technique called Tailored-LLaMA for optimizing the performance of pruned LLaMA models in few-shot learning. The authors address the challenge of fine-tuning large language models (LLMs) for specific tasks while minimizing computational resources. Tailored-LLaMA focuses on structural pruning to reduce model size, employs task-specific prompts to enhance accuracy, and leverages the LoRA method for efficient fine-tuning. Experimental results show that Tailored-LLaMA significantly outperforms other methods in terms of recovery rate and accuracy, highlighting the potential for creating efficient and task-specific LLM variants.

paper - http://arxiv.org/abs/2410.19185v1
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