View LLM Performance Leaderboard
View NSQL Scores for Models
Convert Hugging Face model repo to Safetensors
Display LLM benchmark leaderboard and info
Evaluate AI-generated results for accuracy
Display and filter leaderboard models
Rank machines based on LLaMA 7B v2 benchmark results
Evaluate code generation with diverse feedback types
Track, rank and evaluate open LLMs and chatbots
Evaluate open LLMs in the languages of LATAM and Spain.
Calculate memory needed to train AI models
Download a TriplaneGaussian model checkpoint
View and submit machine learning model evaluations
The LLM Performance Leaderboard is a tool designed to evaluate and compare the performance of large language models (LLMs) across various tasks and datasets. It provides a comprehensive overview of model capabilities, helping users identify top-performing models for specific use cases. By benchmarking models, the leaderboard enables researchers and developers to make informed decisions about model selection and optimization.
• Performance Metrics: Detailed performance metrics across multiple benchmarks and datasets.
• Model Comparisons: Side-by-side comparisons of different LLMs, highlighting strengths and weaknesses.
• Customizable Benchmarks: Ability to filter results by specific tasks or datasets.
• Interactive Visualizations: Graphs and charts to simplify data interpretation.
• Real-Time Updates: Regular updates with the latest models and benchmark results.
• Community Insights: Access to expert analyses and community discussions on model performance.
What types of models are included in the leaderboard?
The leaderboard includes a wide range of LLMs, from open-source models to proprietary ones, covering various architectures and sizes.
How often are the results updated?
Results are updated regularly, typically when new models are released or when significant updates to existing benchmarks occur.
Can I contribute to the leaderboard?
Yes, contributions are welcome. Users can submit feedback, suggest new benchmarks, or participate in community discussions to enhance the platform.