The process of compressing large language models (LLMs) like LLaMA-2-7B involves more than just reducing the number of parameters. The choice of calibration data—used during the compression process—plays a critical role in determining how well the compressed model will perform on downstream tasks. In this post, we’ll delve into the impact of calibration data on two advanced compression techniques: SparseGPT and Wanda. By comparing models calibrated with different datasets, we’ll uncover the significant influence that calibration data has on the effectiveness of these techniques.
Importance of Calibration Data
We evaluate how calibration data used during the compression process impacts the model performance on specific tasks even after significant parameter reduction. The choice of calibration data can make a substantial difference in the outcomes of compression. In our study, we used two distinct instruction-following datasets, Alpaca and Unnatural, to evaluate the performance of these models. While Alpaca was used for both calibration and evaluation, providing a consistent basis for task-specific evaluation, the Unnatural dataset served as an independent test set. This dual-dataset approach allowed us to assess how well the sparsified models performed on different instruction-following tasks, offering a robust evaluation of their capabilities within the instruction-following domain.
Experimental Findings
We evaluated the performance of five different model variants: the original LLaMA-2-7B model, SparseGPT and Wanda models calibrated with C4, and SparseGPT and Wanda models calibrated with Alpaca. These models were assessed on their ability to perform instruction-following tasks using metrics such as Exact Match, F1 Score, and ROUGE-1. The results of our experiments are summarized in the following Table 1, highlighting the performance of LLaMA-2-7B and its sparsified variants evaluated on the Alpaca and Unnatural datasets.
Table 1: Performance of LLaMA-2-7B and sparsified variants on Alpaca and Unnatural datasets.
These results show that models calibrated with the Alpaca dataset consistently outperformed those calibrated with the C4 dataset across all metrics. SparseGPT, in particular, showed higher sensitivity to the choice of calibration data. In some cases, Alpaca-calibrated SparseGPT models even exceeded the performance of the base model on certain tasks.
Discussion of Calibration Data Selection
Our experiments reveal that calibration data significantly influences the effectiveness of model compression, with models calibrated using the Alpaca dataset generally outperforming those calibrated with C4 across various metrics. SparseGPT shows higher sensitivity to the choice of calibration data than Wanda, as SparseGPT-Alpaca models retain or slightly improve performance in some metrics, while C4-calibrated models exhibit significant performance declines. Wanda models also degrade with C4 calibration, but less severely.
SparseGPT-Alpaca achieved the most impressive results, particularly in the Unnatural dataset evaluation, indicating that task-specific calibration not only preserves in-domain capabilities but also enhances the model’s ability to generalize to novel instruction-following scenarios. The consistently strong performance of Alpaca-calibrated models across both evaluation datasets underscores the value of using instruction-tuned datasets for calibration.
Conclusion
The impact of calibration data on LLM compression cannot be overstated. As demonstrated in our study, the right calibration data can significantly enhance the effectiveness of compression techniques like SparseGPT and Wanda, leading to better performance on downstream tasks. These findings have important implications for AI practitioners seeking to deploy LLMs in environments where computational resources are limited. Moving forward, research should continue to explore the relationship between calibration data and compression techniques, with the goal of optimizing LLM performance across a wide range of applications.
.svg){kind=logo}
.png)
