Create and evaluate a function approximation model
Track, rank and evaluate open LLMs and chatbots
Calculate memory usage for LLM models
Compare audio representation models using benchmark results
Browse and submit language model benchmarks
View and submit LLM benchmark evaluations
Convert PaddleOCR models to ONNX format
Benchmark LLMs in accuracy and translation across languages
Search for model performance across languages and benchmarks
Find and download models from Hugging Face
Measure BERT model performance using WASM and WebGPU
Text-To-Speech (TTS) Evaluation using objective metrics.
Generate and view leaderboard for LLM evaluations
Hdmr is a tool designed for model benchmarking, enabling users to create and evaluate function approximation models. It provides a structured approach to comparing different models and understanding their performance under various conditions.
What models are compatible with Hdmr?
Hdmr supports a wide range of models, including machine learning algorithms and custom mathematical functions.
Can I add custom evaluation metrics?
Yes, Hdmr allows users to define and integrate custom metrics for model evaluation.
How do I interpret the benchmarking results?
Results are presented in visual and numerical formats, enabling clear comparison of model performance based on defined metrics.