Frequently Asked Questions

Model compression refers to techniques that reduce the size and computational requirements of machine learning models while maintaining their performance. This includes methods like pruning (removing unnecessary weights), quantization (reducing numerical precision), and knowledge distillation (training smaller models to mimic larger ones).

Compressed models offer several benefits: faster inference speeds, lower memory requirements, reduced energy consumption, and the ability to deploy on resource-constrained devices like mobile phones or edge devices. This translates to lower operational costs and a smaller carbon footprint.

Modern compression techniques are designed to minimize accuracy loss. With proper calibration, most models can be compressed by 50-90% with less than 1% accuracy drop. Our tools help you find the optimal balance between compression and performance for your specific use case.

FasterAI supports PyTorch models natively. We also provide tools for exporting to ONNX format for broader deployment options. Our libraries integrate seamlessly with popular training frameworks and can be used with minimal code changes.

We offer a comprehensive suite of techniques including: structured and unstructured pruning, post-training quantization (INT8, INT4), quantization-aware training, knowledge distillation, and hybrid approaches that combine multiple methods for optimal results.

Getting started is simple: install our library via pip (`pip install fasterai`), import it into your training script, and apply compression with just a few lines of code. Check our quickstart guide for step-by-step instructions and examples.

Yes! FasterAI is designed to integrate with minimal changes to your existing code. For PyTorch users, you can add compression callbacks to your training loop. We also provide high-level APIs that handle the integration automatically.

Yes, our core libraries (FasterAI and FasterBench) are open-source and free to use under the Apache 2.0 license. We also offer premium services for enterprises that need custom solutions, dedicated support, or on-premise deployment.

Yes, we offer consulting services ranging from one-time model optimization projects to ongoing partnerships. Our team can help you identify optimization opportunities, implement compression strategies, and deploy optimized models in production.

We offer multiple support channels: our GitHub repository for bug reports and feature requests, community Discord for discussions, comprehensive documentation with tutorials and API references, and direct email support for enterprise customers.

Yes, we offer training sessions and workshops for teams looking to build expertise in model optimization. These can be customized to your team's skill level and specific use cases. Contact us to discuss your training needs.