Display benchmark results
Calculate memory needed to train AI models
Convert a Stable Diffusion XL checkpoint to Diffusers and open a PR
Evaluate AI-generated results for accuracy
Display and filter leaderboard models
Compare and rank LLMs using benchmark scores
Benchmark models using PyTorch and OpenVINO
Multilingual Text Embedding Model Pruner
Open Persian LLM Leaderboard
Launch web-based model application
Convert and upload model files for Stable Diffusion
Create and upload a Hugging Face model card
View and submit language model evaluations
Redteaming Resistance Leaderboard is a benchmarking tool designed to evaluate the performance of AI models under adversarial attacks. It provides a platform to test and compare the resistance of different models to red teaming strategies, helping researchers and developers identify strengths and weaknesses in their systems.
• Leaderboard System: Displays rankings of models based on their resistance to adversarial attacks.
• Benchmarking Metrics: Provides detailed metrics on model performance under various red teaming scenarios.
• Customizable Attacks: Allows users to define and test specific types of adversarial inputs.
• Result Visualization: Offers graphical representations of benchmark results for easier analysis.
• Performance Tracking: Enables tracking of model improvements over time.
• Scenario Customization: Supports testing against real-world and hypothetical adversarial scenarios.
1. What does "red teaming" mean in this context?
Red teaming refers to the process of attacking a system (in this case, an AI model) to test its resistance and identify vulnerabilities.
2. How do I interpret the benchmark results?
Benchmark results show how well your model performs under adversarial conditions. Lower scores indicate weaker resistance, while higher scores suggest better robustness.
3. Can I test custom adversarial scenarios?
Yes, the leaderboard allows users to define and test custom adversarial scenarios, providing flexibility for specific use cases.