Retrain models for new data at edge devices
View and submit LLM benchmark evaluations
View and submit language model evaluations
Calculate memory usage for LLM models
Browse and submit model evaluations in LLM benchmarks
Teach, test, evaluate language models with MTEB Arena
Launch web-based model application
Benchmark LLMs in accuracy and translation across languages
Browse and filter ML model leaderboard data
Leaderboard of information retrieval models in French
Convert Hugging Face models to OpenVINO format
View and compare language model evaluations
Evaluate code generation with diverse feedback types
EdgeTA is a model benchmarking tool designed to retrain models for new data at edge devices. It enables efficient adaptation of AI models to work effectively in resource-constrained environments like smartphones, IoT devices, or other edge computing platforms. With EdgeTA, users can optimize their models for real-time performance while maintaining accuracy.
What is EdgeTA used for?
EdgeTA is used to retrain AI models on edge devices, enabling them to adapt to new data while optimizing for resource efficiency.
Can EdgeTA work with any type of model?
Yes, EdgeTA is designed to be model-agnostic, supporting various deep learning frameworks and architectures.
How does EdgeTA ensure data privacy?
EdgeTA processes data locally on edge devices, minimizing the need for cloud-based data transmission and enhancing privacy.