Generate and view leaderboard for LLM evaluations
Calculate memory usage for LLM models
Display LLM benchmark leaderboard and info
Browse and evaluate ML tasks in MLIP Arena
Track, rank and evaluate open LLMs and chatbots
View LLM Performance Leaderboard
Explore and visualize diverse models
Browse and submit LLM evaluations
Push a ML model to Hugging Face Hub
Determine GPU requirements for large language models
Explore and submit models using the LLM Leaderboard
Benchmark models using PyTorch and OpenVINO
Evaluate AI-generated results for accuracy
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.