Generate and view leaderboard for LLM evaluations
Create demo spaces for models on Hugging Face
Quantize a model for faster inference
Optimize and train foundation models using IBM's FMS
Track, rank and evaluate open LLMs and chatbots
View and submit LLM benchmark evaluations
View RL Benchmark Reports
Open Persian LLM Leaderboard
Export Hugging Face models to ONNX
Compare and rank LLMs using benchmark scores
Create and upload a Hugging Face model card
Display model benchmark results
Submit models for evaluation and view leaderboard
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.