Large Language Models (LLMs) are the driving force behind the many AI-powered things we see in today's AI-crazed tech space. They have enabled natural language processing at scales never seen before.
But, in order for those to be reliable, they have to be evaluated to ensure key metrics like performance, accuracy, safety, bias, and ethics.
Hugging Face, a popular name known for providing an accessible platform for people to share models, datasets, and other kinds of AI-related work, has recently announced that they are open-sourcing an internal evaluation suite for helping with just that.
LightEval: What To Expect?
As we're seeing more and more everyday, evaluation is one of the most important steps - if not the most important one - in AI. Not only do we need to improve general benchmarking but we should also give every organization to run their own evaluation, aligned with their specific… pic.twitter.com/KoTPfbt1bq
— clem 🤗 (@ClementDelangue) September 9, 2024
CEO of Hugging Face announcing LightEval.
Built by the same team behind Open LLM Leaderboard, LightEval is completely written in the Python programming language, with Hugging Face using it internally with Datatrove and Nanotron for evaluation over the past few months.
In this early avatar, LightEval can be used on both small and large-scale deployments on a wide range of devices that include CPUs, GPUs, and TPUs. The developers have also implemented integration with the Accelerate library, further bolstering LightEval's use across a diverse hardware environment.
As it is available under the permissive MIT License, LightEval can be used by various end users. Be it individuals seeking to see how their custom model performs or large organizations looking to gauge a model used in one of their applications before it enters production.
Want To Check It Out?
LightEval can be sourced from GitHub, where you will find all the necessary instructions on how to install it. If you like what it does, then you can also look into contributing to it.
Do keep in mind that LightEval is still under development and is not completely stable. If you face any problems, consider opening an issue on the GitHub repo.
Via: VentureBeat
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