Retrain models for new data at edge devices
Calculate GPU requirements for running LLMs
Calculate memory needed to train AI models
Run benchmarks on prediction models
Convert Stable Diffusion checkpoint to Diffusers and open a PR
Calculate memory usage for LLM models
Convert and upload model files for Stable Diffusion
Display and submit language model evaluations
Submit models for evaluation and view leaderboard
Quantize a model for faster inference
View NSQL Scores for Models
Display leaderboard of language model evaluations
Search for model performance across languages and benchmarks
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.