quantbenchx added to PyPI

QuantBenchX's addition to PyPI with its pure-Python GGUF/safetensors parsing and layerwise analysis capabilities marks a significant development in the field of model optimization. This means that website owners who rely on machine learning models for their online applications can now leverage QuantBenchX to analyze and improve the performance of their models, potentially leading to faster load times and improved user experience. With QuantBenchX, website owners can optimize their models for better quantization quality, which can result in reduced latency and increased efficiency. To take advantage of QuantBenchX, website owners can start by installing the library via PyPI and exploring its capabilities for layerwise analysis and quality prediction. Additionally, they can use QuantBenchX to identify areas where their models can be optimized for better performance, and then update their llms.txt files to reflect these changes. By doing so, website owners can ensure that their AI-powered applications are running at optimal levels, leading to improved user engagement and overall website performance.