Run benchmarks on prediction models
View and submit language model evaluations
Track, rank and evaluate open LLMs and chatbots
Evaluate RAG systems with visual analytics
Download a TriplaneGaussian model checkpoint
Measure over-refusal in LLMs using OR-Bench
Calculate VRAM requirements for LLM models
Create demo spaces for models on Hugging Face
Browse and filter ML model leaderboard data
View and submit LLM evaluations
Quantize a model for faster inference
Compare and rank LLMs using benchmark scores
Compare model weights and visualize differences
The LLM Forecasting Leaderboard is a comprehensive tool designed for evaluating and comparing the performance of large language models (LLMs) in forecasting tasks. It provides a platform to benchmark prediction models, enabling users to assess their accuracy, reliability, and effectiveness in generating future predictions based on historical data. This leaderboard is particularly useful for researchers, data scientists, and professionals seeking to identify top-performing models for forecasting applications.
• Model Benchmarking: Compare the performance of different LLMs on various forecasting tasks. • Detailed Performance Metrics: Access metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared to evaluate model accuracy. • Customizable Forecasts: Define specific forecasting parameters to suit different use cases. • Historical Data Support: Leverage historical data to train and test models for future predictions. • Real-Time Updates: Stay informed with the latest model performance data and leaderboard rankings. • User-Friendly Interface: Easily navigate and interpret forecasting results through an intuitive dashboard. • Collaboration Tools: Share insights and compare results with colleagues or peers.
What types of forecasting tasks does the LLM Forecasting Leaderboard support?
The leaderboard supports a variety of forecasting tasks, including time series prediction, demand forecasting, and financial prediction, among others.
How often is the leaderboard updated?
The leaderboard is updated regularly to reflect the latest model performance data and new model releases.
Can I submit my own model for benchmarking?
Yes, you can submit your own model for evaluation. Please refer to the platform's documentation for submission guidelines and requirements.