Start a web application for model inference
View LLM Performance Leaderboard
Convert Hugging Face models to OpenVINO format
Display LLM benchmark leaderboard and info
Optimize and train foundation models using IBM's FMS
Browse and submit evaluations for CaselawQA benchmarks
Browse and submit model evaluations in LLM benchmarks
Calculate VRAM requirements for LLM models
Evaluate RAG systems with visual analytics
Determine GPU requirements for large language models
Measure over-refusal in LLMs using OR-Bench
View and submit LLM benchmark evaluations
Display leaderboard of language model evaluations
Mapcoordinates is a web-based application designed for model inference and benchmarking. It provides a platform to evaluate and compare the performance of various AI models, enabling users to make informed decisions based on accurate metrics. The tool simplifies the process of testing and validating models, making it an essential resource for developers and researchers.
• Multiple Model Support: Evaluate and benchmark different AI models across frameworks.
• Versioning: Track performance changes across model versions.
• Performance Metrics: Access detailed metrics such as accuracy, inference time, and resource usage.
• Cross-Framework Benchmarking: Compare models from TensorFlow, PyTorch, and other popular frameworks.
• Visualized Results: Generate graphs and charts to visualize benchmarking outcomes.
• Optimization Recommendations: Receive suggestions to improve model performance based on benchmarks.
• Extensible API: Integrate custom models or frameworks into the platform.
What does Mapcoordinates do?
Mapcoordinates is a web application that allows users to benchmark and compare AI models, providing detailed insights into their performance and efficiency.
What types of models can I benchmark with Mapcoordinates?
You can benchmark models from various frameworks, including TensorFlow, PyTorch, and others, depending on your specific needs.
Can I use Mapcoordinates for tasks other than benchmarking?
Mapcoordinates is primarily designed for model inference and benchmarking. While it supports visualization and optimization, it is not intended for general-purpose AI tasks.