Generate and view leaderboard for LLM evaluations
Calculate GPU requirements for running LLMs
Benchmark models using PyTorch and OpenVINO
Compare code model performance on benchmarks
Display and filter leaderboard models
View and compare language model evaluations
View and submit language model evaluations
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Multilingual Text Embedding Model Pruner
Analyze model errors with interactive pages
Determine GPU requirements for large language models
Browse and submit model evaluations in LLM benchmarks
Calculate memory usage for LLM 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.