Display model benchmark results
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Launch web-based model application
Generate and view leaderboard for LLM evaluations
Find recent high-liked Hugging Face models
Rank machines based on LLaMA 7B v2 benchmark results
Evaluate open LLMs in the languages of LATAM and Spain.
Browse and submit model evaluations in LLM benchmarks
SolidityBench Leaderboard
Request model evaluation on COCO val 2017 dataset
Compare audio representation models using benchmark results
Text-To-Speech (TTS) Evaluation using objective metrics.
Retrain models for new data at edge devices
The Redteaming Resistance Leaderboard is a tool designed to benchmark and compare AI models based on their ability to resist adversarial attacks and maintain performance under challenging conditions. It provides a centralized platform to evaluate and rank models, offering insights into their robustness and reliability in real-world scenarios.
• Real-time Performance Tracking: continuously updates model performance metrics
• Head-to-Head Comparisons: ability to compare multiple models simultaneously
• Resistance Metrics: evaluates models based on their ability to withstand adversarial inputs
• Filtering System: allows users to filter models by specific criteria such as dataset, architecture, or performance thresholds
• Historical Data: provides access to past performance records for trend analysis
• Cross-Platform Compatibility: accessible on multiple devices and browsers
What is Redteaming in the context of AI models?
Redteaming refers to the process of systematically testing AI models to identify vulnerabilities and measure their resistance to adversarial attacks or unexpected inputs.
How are models ranked on the leaderboard?
Models are ranked based on their performance under stress tests, including their ability to maintain accuracy and reliability when exposed to challenging or adversarial conditions.
Can I customize the metrics used for comparison?
Yes, the platform allows users to filter and customize the metrics used for comparison, enabling tailored analysis based on specific needs or use cases.