Create and evaluate a function approximation model
Display LLM benchmark leaderboard and info
Convert Stable Diffusion checkpoint to Diffusers and open a PR
Browse and filter machine learning models by category and modality
Run benchmarks on prediction models
Benchmark models using PyTorch and OpenVINO
Download a TriplaneGaussian model checkpoint
Convert Hugging Face models to OpenVINO format
Explore and submit models using the LLM Leaderboard
Merge machine learning models using a YAML configuration file
Evaluate LLM over-refusal rates with OR-Bench
Submit deepfake detection models for evaluation
View and submit 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.