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

Large language models demonstrate impressive proficiency in language understanding and generation. Nonetheless, training these models from scratch, even the least complex billion-parameter variant demands significant computational resources rendering it economically impractical for many organizations. With large language models functioning as general-purpose task solvers, this paper investigates their task-specific fine-tuning. We employ task-specific datasets and prompts to fine-tune two pruned LLaMA models having 5 billion and 4 billion parameters. This process utilizes the pre-trained weights and focuses on a subset of weights using the LoRA method. One challenge in fine-tuning the LLaMA model is crafting a precise prompt tailored to the specific task. To address this, we propose a novel approach to fine-tune the LLaMA model under two primary constraints: task specificity and prompt effectiveness. Our approach, Tailored LLaMA initially employs structural pruning to reduce the model sizes from 7B to 5B and 4B parameters. Subsequently, it applies a carefully designed prompt specific to the task and utilizes the LoRA method to accelerate the fine-tuning process. Moreover, fine-tuning a model pruned by 50% for less than one hour restores the mean accuracy of classification tasks to 95.68% at a 20% compression ratio and to 86.54% at a 50% compression ratio through few-shot learning with 50 shots. Our validation of Tailored LLaMA on these two pruned variants demonstrates that even when compressed to 50%, the models maintain over 65% of the baseline model accuracy in few-shot classification and generation tasks. These findings highlight the efficacy of our tailored approach in maintaining high performance with significantly reduced model sizes.