Filter and view AI model leaderboard data
Browse and compare Indic language LLMs on a leaderboard
Generate a detailed dataset report
Explore and compare LLM models through interactive leaderboards and submissions
Evaluate LLMs using Kazakh MC tasks
Explore tradeoffs between privacy and fairness in machine learning models
Generate detailed data reports
Display a welcome message on a webpage
Visualize amino acid changes in protein sequences interactively
Browse and filter AI model evaluation results
Migrate datasets from GitHub or Kaggle to Hugging Face Hub
View and compare pass@k metrics for AI models
Display CLIP benchmark results for inference performance
LLM Leaderboard for CRM is a data visualization tool designed to help businesses analyze and compare AI model performance within their CRM systems. It provides a centralized platform to track, filter, and visualize leaderboard data, enabling users to make data-driven decisions and optimize their AI strategies.
• Real-time Performance Tracking: Monitor AI model performance metrics in real-time. • Customizable Filters: Filter data by specific criteria such as model type, accuracy, or usage. • Side-by-Side Comparisons: Compare multiple AI models to evaluate strengths and weaknesses. • Historical Data Analysis: Access and analyze performance trends over time. • Interactive Visualizations: Generate charts, graphs, and heatmaps for clear data representation. • Multi-Model Support: Compatible with various AI models and frameworks. • Integration Ready: Easily integrates with existing CRM systems.
What is the purpose of LLM Leaderboard for CRM?
The purpose of LLM Leaderboard for CRM is to provide a user-friendly interface for tracking and comparing AI model performance within your CRM system, helping businesses optimize their AI strategies.
How does the app ensure data accuracy?
The app pulls real-time data from connected AI models and CRM systems, ensuring that the leaderboard reflects the most up-to-date performance metrics.
Can I customize the visualizations?
Yes, users can customize the visualizations by applying filters, selecting specific models, and choosing from various chart types to suit their needs.