Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization

1. 1Identify the most suitable calibration data using frequency-based methods
2. 2Apply pruning and quantization techniques to compress the model
3. 3Evaluate the compressed model's performance using the curated calibration data
4. 4Refine the compression configuration based on the evaluation results

ML researchers and engineers benefit from this approach as it enhances model portability while preserving performance, and can be applied by data scientists and ai-engineers to optimize model compression

💡 Frequency-based data curation is a critical step in preserving model capabilities during post-training compression

🚀 Frequency-based data curation boosts model compression for LLMs!