This package provides a python decorator to save on disk and reuse the results of functions that are long to execute. This can be referred to as persistent memoization. The result of a decorated ...

So far, running LLMs has required a large amount of computing resources, mainly GPUs. Running locally, a simple prompt with a typical LLM takes on an average Mac ...