Test your AI models with Giskard
Pergel: A Unified Benchmark for Evaluating Turkish LLMs
GIFT-Eval: A Benchmark for General Time Series Forecasting
Measure execution times of BERT models using WebGPU and WASM
Calculate memory usage for LLM models
Determine GPU requirements for large language models
Convert PyTorch models to waifu2x-ios format
Upload ML model to Hugging Face Hub
Evaluate reward models for math reasoning
View and submit machine learning model evaluations
Compare code model performance on benchmarks
Upload a machine learning model to Hugging Face Hub
Evaluate code generation with diverse feedback types
Giskard Hub is an advanced platform designed for testing and benchmarking AI models. It provides a comprehensive environment for machine learning engineers and researchers to evaluate and compare the performance of their AI models across various datasets and metrics. With Giskard Hub, users can ensure their models meet the highest standards of quality and reliability.
• Multiple Dataset Support: Test your models on a wide range of datasets to assess performance in diverse scenarios.
• Baseline Model Comparison: Compare your model's performance against industry-standard benchmarks.
• Customizable Metrics: Define and use specific evaluation metrics tailored to your needs.
• Comprehensive Analytics: Gain detailed insights into your model's strengths and weaknesses.
• Integration Capabilities: Easily integrate with popular machine learning frameworks and tools.
What types of models can I test on Giskard Hub?
Giskard Hub supports a wide range of AI models, including classification, regression, and generative models. It is compatible with most machine learning frameworks.
Can I use my own datasets for benchmarking?
Yes, Giskard Hub allows you to upload and use your own datasets for model evaluation.
How do I share my benchmarking results?
You can generate shareable reports or export results in various formats (e.g., CSV, JSON) for easy collaboration with your team.