*LLM decompression isn’t a silver bullet, but it’s a practical technique for systematically extracting value from trained models. The key insight is treating inference as a knowledge extraction tool rather than just a generation mechanism.
With efficient inference infrastructure, we can reverse-engineer the compressed knowledge in any model and convert it into structured, reusable datasets. This has immediate applications in model analysis, knowledge transfer, and training data creation.*
LLM-Deflate: Extracting LLMs Into Datasets 
ScalarLM
LLM-Deflate: Extracting LLMs Into Datasets
Large Language Models compress massive amounts of training data into their parameters. This compression is lossy but highly effective—billions of...



