evalsig added to PyPI

Evalsig's addition to PyPI with statistical inference for LLM evaluations, including paired tests, clustered SE, MDE, sequential testing, and release gating, marks a significant development in the field of Large Language Model (LLM) assessment. This means that website owners who utilize LLMs to generate content or interact with users can now leverage evalsig to more accurately evaluate the performance of these models, allowing for data-driven decisions on model selection, fine-tuning, and deployment. By using evalsig, website owners can better understand the strengths and weaknesses of their LLMs, ultimately leading to improved user experiences and more effective content generation. To take advantage of evalsig, website owners can follow these actionable tips: first, explore the evalsig library on PyPI to understand its capabilities and integration requirements; second, use evalsig to conduct paired tests and clustered SE analyses on their LLMs to identify areas for improvement; third, implement release gating using evalsig's sequential testing features to ensure that only high-performing models are deployed, and update their llms.txt files accordingly to reflect these changes.