Visualize model performance on function calling tasks
Track, rank and evaluate open LLMs and chatbots
Benchmark LLMs in accuracy and translation across languages
Convert PyTorch models to waifu2x-ios format
Compare LLM performance across benchmarks
Launch web-based model application
Browse and evaluate ML tasks in MLIP Arena
Request model evaluation on COCO val 2017 dataset
Calculate memory needed to train AI models
Persian Text Embedding Benchmark
Load AI models and prepare your space
Explore and submit models using the LLM Leaderboard
Retrain models for new data at edge devices
The Nexus Function Calling Leaderboard is a tool designed to visualize and benchmark model performance on function calling tasks. It provides a comprehensive platform to compare and analyze the effectiveness of different models in executing specific functions, helping users make informed decisions based on performance metrics.
• Real-time performance metrics: Track model accuracy, execution speed, and success rates in real-time. • Customizable benchmarks: Define specific function calling tasks to test models in scenarios relevant to your use case. • Comparison tools: Easily compare the performance of multiple models on the same task. • Visual analytics: Detailed graphs and charts to help interpret performance data. • Community-driven insights: Access a community-sourced repository of benchmarked models and tasks. • User-friendly interface: Intuitive dashboard design for seamless navigation and analysis.
What models are supported by Nexus Function Calling Leaderboard?
The platform supports a wide range of models, including popular AI frameworks and custom models. Check the documentation for a full list of supported models.
How often are the benchmarks updated?
Benchmarks are updated in real-time as new models are added or existing ones are retested. You can also request specific models to be benchmarked.
Can I use Nexus Function Calling Leaderboard for private benchmarks?
Yes, the platform allows you to run private benchmarks for internal use. Contact support for details on setting up a private instance.