Evaluate and submit AI model results for Frugal AI Challenge
Display benchmark results
View and submit machine learning model evaluations
View and submit LLM benchmark evaluations
Evaluate reward models for math reasoning
Optimize and train foundation models using IBM's FMS
Explain GPU usage for model training
Display model benchmark results
Browse and evaluate language models
Analyze model errors with interactive pages
Evaluate LLM over-refusal rates with OR-Bench
Download a TriplaneGaussian model checkpoint
Retrain models for new data at edge devices
Submission Portal is a web-based platform designed to evaluate and submit AI model results for the Frugal AI Challenge. It serves as a centralized hub for participants to upload their model outputs, benchmark performance, and compare results with others in a transparent and standardized manner. The portal streamlines the submission process and provides a seamless experience for participants to showcase their AI solutions.
• Secure Submission Environment: Upload your model results securely and efficiently.
• Benchmarking Tools: Compare your model's performance against industry standards and other submissions.
• Real-Time Feedback: Receive immediate feedback on your submission to identify areas for improvement.
• Comprehensive Analytics: Access detailed analytics and visualizations of your model's performance.
• Submission Tracking: Monitor the status of your submissions and view past results.
What is the purpose of Submission Portal?
Submission Portal is designed to facilitate the submission and evaluation of AI model results for the Frugal AI Challenge, enabling participants to benchmark their solutions effectively.
How do I format my model results for submission?
Formatting guidelines are provided on the portal’s homepage. Ensure your results comply with these specifications to avoid submission issues.
Can I submit multiple results for the same model?
Yes, you can submit multiple iterations of your model’s results. Each submission will be treated as a separate entry for benchmarking purposes.