Generate and view leaderboard for LLM evaluations
Benchmark models using PyTorch and OpenVINO
Evaluate and submit AI model results for Frugal AI Challenge
Find and download models from Hugging Face
Launch web-based model application
Create demo spaces for models on Hugging Face
Convert PaddleOCR models to ONNX format
View LLM Performance Leaderboard
View and submit language model evaluations
Evaluate open LLMs in the languages of LATAM and Spain.
Retrain models for new data at edge devices
Optimize and train foundation models using IBM's FMS
Calculate memory needed to train AI models
Arabic MMMLU Leaderborad is a model benchmarking tool designed to evaluate and compare the performance of different large language models (LLMs) on Arabic language tasks. It provides a comprehensive leaderboard where researchers and developers can assess model capabilities across a variety of NLP tasks specific to Arabic. The platform allows for transparent and standardized evaluation, enabling the community to track progress in Arabic NLP.
What is the purpose of the Arabic MMMLU Leaderborad?
The purpose is to provide a standardized platform for evaluating and comparing LLMs on Arabic language tasks, fostering transparency and collaboration in NLP research.
How can I get started with the leaderboard?
Start by preparing your model, selecting tasks, and following the step-by-step instructions provided on the platform.
Can I customize the evaluation metrics?
Yes, the platform allows users to define and track specific evaluation metrics tailored to their needs.