Overview of natively supported quantization schemes in 🤗 Transformers

The article outlines the newly supported quantization schemes in the Hugging Face Transformers library, including dynamic quantization, static quantization, and quantization-aware training (QAT). It details the implementation of these techniques across various model architectures, with specific examples like BERT and GPT-2, highlighting their impact on model size and inference speed. This enhancement allows practitioners to optimize their models for deployment on resource-constrained environments without significant loss in accuracy, thereby improving efficiency in real-world applications.