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OPEN-MOE-LLM-LEADERBOARD is a comprehensive platform designed for benchmarking and comparing large language models (LLMs). It serves as a centralized hub where researchers, developers, and users can explore, evaluate, and submit their models for transparent and fair comparison. The platform is part of the OpenMoe initiative, which aims to promote openness and collaboration in the field of AI research.
• Comprehensive Model Database: Access a wide range of pre-trained LLMs, including state-of-the-art models from leading research organizations and companies.
• Standardized Evaluation Metrics: Models are evaluated using a consistent set of benchmarks and metrics to ensure fair and meaningful comparisons.
• Customizable Benchmarking: Users can define custom evaluation tasks and datasets to test models under specific conditions.
• Model Submission and Sharing: Developers can easily submit their models for inclusion in the leaderboard, fostering community-driven progress.
• Versioning and Tracking: Track model improvements and updates over time with versioned submissions.
• Detailed Documentation: Each model is accompanied by detailed documentation, including training parameters, architecture, and performance analysis.
• Community Interaction: Engage with a vibrant community of researchers and developers through discussions and forums.
What is the purpose of the OPEN-MOE-LLM-LEADERBOARD?
The platform aims to provide a transparent and standardized way to evaluate and compare large language models, enabling researchers and developers to identify top-performing models and share their work with the community.
How do I submit my model to the leaderboard?
To submit your model, prepare it according to the platform's submission guidelines, which include providing model weights, configuration files, and detailed documentation. Then, use the submission interface to upload your model for evaluation.
What evaluation metrics does the platform use?
The platform uses a variety of standardized metrics, including perplexity, BLEU score, ROUGE score, and task-specific benchmarks, to ensure comprehensive and fair model comparisons.
Can I customize the evaluation tasks for my specific use case?
Yes, the platform allows users to define custom evaluation tasks and datasets, enabling them to test models under specific conditions tailored to their needs.
How are models ranked on the leaderboard?
Models are ranked based on their performance across a suite of benchmarks and metrics, with the highest-performing models appearing at the top of the leaderboard.
Is the platform free to use?
Yes, the platform is open and free to use, with the goal of democratizing access to AI research tools and fostering collaboration across the research community.