Create and evaluate a function approximation model
Generate and view leaderboard for LLM evaluations
Upload ML model to Hugging Face Hub
Benchmark AI models by comparison
Display leaderboard for earthquake intent classification models
Convert Stable Diffusion checkpoint to Diffusers and open a PR
Merge machine learning models using a YAML configuration file
Convert and upload model files for Stable Diffusion
Search for model performance across languages and benchmarks
Calculate memory needed to train AI models
Load AI models and prepare your space
Explore GenAI model efficiency on ML.ENERGY leaderboard
View and compare language model evaluations
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.