  Large Language Models (LLMs) have reshaped natural language processing with
their impressive capabilities. However, their ever-increasing size has raised
concerns about their effective deployment and the need for LLM compression.
This study introduces the Divergent Token Metrics (DTMs), a novel approach to
assessing compressed LLMs, addressing the limitations of traditional perplexity
or accuracy measures that fail to accurately reflect text generation quality.
DTMs measure token divergences that allow deeper insights into the subtleties
of model compression, in particular, when evaluating components' impacts
individually. Utilizing the First Divergent Token Metric (FDTM) in model
sparsification reveals that 25% of all attention components can be pruned
beyond 90% on the Llama-2 model family, still keeping SOTA performance. For
quantization, FDTM suggests that more than 80% of parameters can be naively
transformed to int8 without special outlier management. These evaluations
indicate the necessity of choosing appropriate compressions for parameters
individually -- and that FDTM can identify those -- while standard metrics
result in deteriorated outcomes.
