Self-calibration for Language Model Quantization and Pruning

• URL: http://arxiv.org/abs/2410.17170v1
• Date: Tue, 22 Oct 2024 16:50:00 GMT
• Title: Self-calibration for Language Model Quantization and Pruning
• Authors: Miles Williams, George Chrysostomou, Nikolaos Aletras,
• Abstract summary: Quantization and pruning are fundamental approaches for model compression. In a post-training setting, state-of-the-art quantization and pruning methods require calibration data. We propose self-calibration as a solution.
• Score: 38.00221764773372
• License:
• Abstract: Quantization and pruning are fundamental approaches for model compression, enabling efficient inference for language models. In a post-training setting, state-of-the-art quantization and pruning methods require calibration data, a small set of unlabeled examples. Conventionally, randomly sampled web text is used, aiming to reflect the model training data. However, this poses two key problems: (1) unrepresentative calibration examples can harm model performance, and (2) organizations increasingly avoid releasing model training data. In this paper, we propose self-calibration as a solution. Our approach requires no external data, instead leveraging the model itself to generate synthetic calibration data as a better approximation of the pre-training data distribution. We extensively compare the performance of self-calibration with several baselines, across a variety of models, compression methods, and tasks. Our approach proves consistently competitive in maximizing downstream task performance, frequently outperforming even using real data.

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