View and submit machine learning model evaluations
Compare LLM performance across benchmarks
Display and filter leaderboard models
Browse and submit model evaluations in LLM benchmarks
Evaluate adversarial robustness using generative models
Export Hugging Face models to ONNX
Submit models for evaluation and view leaderboard
Convert and upload model files for Stable Diffusion
Visualize model performance on function calling tasks
Optimize and train foundation models using IBM's FMS
Predict customer churn based on input details
Rank machines based on LLaMA 7B v2 benchmark results
Benchmark models using PyTorch and OpenVINO
The LLM Safety Leaderboard is a platform designed to evaluate and compare the safety performance of large language models (LLMs). It provides a community-driven space where users can submit evaluations of machine learning models, focusing on their adherence to safety guidelines and ethical standards. The leaderboard serves as a transparent tool for developers, researchers, and users to assess and improve the safety of AI models.
1. What makes the LLM Safety Leaderboard unique?
The leaderboard's focus on safety metrics and its community-driven submissions set it apart from other model benchmarking tools. It prioritizes ethical AI development and user participation.
2. Can anyone submit a model evaluation?
Yes, any user can submit evaluations, provided they meet the platform's guidelines and quality standards. This ensures diverse and reliable data.
3. How are models ranked on the leaderboard?
Models are ranked based on aggregated safety metrics, including user submissions and automated evaluations. Rankings are updated in real-time as new data is added.