Browse and evaluate ML tasks in MLIP Arena
Convert PyTorch models to waifu2x-ios format
Persian Text Embedding Benchmark
Convert PaddleOCR models to ONNX format
Determine GPU requirements for large language models
Visualize model performance on function calling tasks
View LLM Performance Leaderboard
Calculate memory usage for LLM models
Evaluate LLM over-refusal rates with OR-Bench
Display leaderboard of language model evaluations
Browse and filter machine learning models by category and modality
Explore and submit models using the LLM Leaderboard
Display LLM benchmark leaderboard and info
MLIP Arena is a platform designed for model benchmarking, enabling users to browse and evaluate machine learning models across various tasks. It serves as a centralized hub for exploring and comparing the performance of different models, providing valuable insights for both researchers and practitioners.
• Model Library: Access a comprehensive library of pre-trained machine learning models. • Performance Comparison: Compare models across multiple metrics and benchmarks. • Task-Specific Analysis: Evaluate models based on specific tasks such as classification, regression, etc. • Customizable Benchmarks: Define custom evaluation criteria tailored to your needs. • Visualizations: Interactive charts and graphs to simplify performance analysis. • Cross-Model Insights: Identify strengths and weaknesses of different models. • Integration Support: Connect with popular machine learning frameworks and platforms.
What is MLIP Arena used for?
MLIP Arena is used for benchmarking and evaluating machine learning models across various tasks and datasets. It helps users compare model performance and identify the best-suited models for their use cases.
Do I need to register to use MLIP Arena?
No, while some features may require an account, basic browsing and evaluation of models are typically available without registration.
Can I evaluate custom models in MLIP Arena?
Yes, MLIP Arena supports the evaluation of custom models. You can upload your models and benchmark them against existing ones in the library.