Start a web application for model inference
Explore and visualize diverse models
Visualize model performance on function calling tasks
View and submit LLM benchmark evaluations
Display LLM benchmark leaderboard and info
Quantize a model for faster inference
Calculate memory usage for LLM models
Benchmark AI models by comparison
Text-To-Speech (TTS) Evaluation using objective metrics.
Merge machine learning models using a YAML configuration file
Launch web-based model application
Measure BERT model performance using WASM and WebGPU
Compare and rank LLMs using benchmark scores
Mapcoordinates is a web application designed for model benchmarking. It provides a platform to evaluate and compare the performance of various machine learning models by analyzing their inference capabilities across different datasets and scenarios. This tool is particularly useful for researchers and developers looking to optimize model performance and make data-driven decisions.
• Model Evaluation: Comprehensive analysis of model inference capabilities
• Cross-Model Comparison: Direct comparison of different models' performance
• Customizable Metrics: Define and track specific evaluation criteria
• Data Visualization: Intuitive graphs and charts to represent benchmarking results
• Cross-Framework Support: Compatibility with multiple machine learning frameworks
• Scenario Simulation: Test models under various real-world scenarios
What frameworks does Mapcoordinates support?
Mapcoordinates supports a wide range of machine learning frameworks, including TensorFlow, PyTorch, and Scikit-learn.
Can I customize the evaluation metrics?
Yes, Mapcoordinates allows you to define custom evaluation metrics to align with your specific benchmarking goals.
How long does the benchmarking process typically take?
The duration depends on the model complexity and dataset size. .Simple models may complete in minutes, while larger models can take hours.