Create and evaluate a function approximation model
Explore and benchmark visual document retrieval models
Convert a Stable Diffusion XL checkpoint to Diffusers and open a PR
Display leaderboard for earthquake intent classification models
Create demo spaces for models on Hugging Face
View and submit LLM benchmark evaluations
Evaluate model predictions with TruLens
Submit models for evaluation and view leaderboard
Display LLM benchmark leaderboard and info
Export Hugging Face models to ONNX
Evaluate open LLMs in the languages of LATAM and Spain.
Benchmark models using PyTorch and OpenVINO
Explore and submit models using the LLM Leaderboard
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.