Update leaderboard for fair model evaluation
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Transfer GitHub repositories to Hugging Face Spaces
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Analyze data to generate a comprehensive profile report
Generate a detailed dataset report
Execute commands and visualize data
View and compare pass@k metrics for AI models
Mapping Nieman Lab's 2025 Journalism Predictions
This project is a GUI for the gpustack/gguf-parser-go
Launch Argilla for data labeling and annotation
https://huggingface.co/spaces/VIDraft/mouse-webgen
Generate detailed data profile reports
This is a data visualization tool designed to help users better understand and compare the performance of open-source large language models (LLMs). The tool aims to create a steeper leaderboard to encourage fair competition and innovation in the AI community. By providing a clear and interactive way to track model improvements, it helps researchers and developers identify areas for optimization and pushes the boundaries of LLM capabilities.
• Interactive Leaderboard: Visualize model performance metrics in a dynamic and easily comparable format.
• Real-Time Tracking: Stay updated with the latest advancements in LLM performance.
• Performance Comparisons: Highlight differences between models to identify strengths and weaknesses.
• Customizable Filters: Focus on specific metrics or models to tailor your analysis.
• Insight Generation: Gain actionable insights to improve model development and fine-tuning.
What is the purpose of this tool?
The tool aims to foster innovation by providing a clear and competitive leaderboard, helping researchers and developers improve LLM performance.
How does it help in model evaluation?
By visualizing performance metrics, it allows for fair and transparent comparisons, making it easier to spot areas for improvement.
Can I customize the metrics I track?
Yes, the tool offers customizable filters to focus on specific metrics or models, tailoring the analysis to your needs.