Create and evaluate a function approximation model
Track, rank and evaluate open LLMs and chatbots
Convert Hugging Face models to OpenVINO format
Create and upload a Hugging Face model card
Submit deepfake detection models for evaluation
Generate and view leaderboard for LLM evaluations
Convert PaddleOCR models to ONNX format
Display LLM benchmark leaderboard and info
Benchmark models using PyTorch and OpenVINO
Create and manage ML pipelines with ZenML Dashboard
Export Hugging Face models to ONNX
Search for model performance across languages and benchmarks
Evaluate open LLMs in the languages of LATAM and Spain.
Hdmr is a tool designed for model benchmarking, specifically focused on creating and evaluating function approximation models. It enables users to develop, test, and compare different models to identify the most accurate and efficient solutions for their specific tasks.
What does Hdmr stand for?
Hdmr stands for Hierarchical Dynamic Model Representation, a framework for evaluating function approximation models.
Can Hdmr be used with any machine learning framework?
Hdmr is designed to support popular machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
How do I interpret the benchmarking results from Hdmr?
Hdmr provides detailed metrics and visualizations to help users interpret results. Lower error rates and higher convergence speeds typically indicate better model performance.