Generate and view leaderboard for LLM evaluations
Request model evaluation on COCO val 2017 dataset
Upload ML model to Hugging Face Hub
Compare code model performance on benchmarks
Convert and upload model files for Stable Diffusion
Rank machines based on LLaMA 7B v2 benchmark results
Convert Stable Diffusion checkpoint to Diffusers and open a PR
Browse and filter ML model leaderboard data
Explain GPU usage for model training
Browse and filter machine learning models by category and modality
Browse and evaluate ML tasks in MLIP Arena
View LLM Performance Leaderboard
Explore and visualize diverse 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.