Accelerate SetFit inference on Intel Xeon with 🤗 Optimum Intel static quantization

AI Impact Summary

The post demonstrates accelerating SetFit inference on Intel Xeon CPUs by quantizing the SetFit model body with INCQuantizer from Optimum Intel using static post-training quantization. It relies on a 100-sample calibration set and Optimum Intel >= 1.14.0, leveraging bf16/int8 GEMMs and AMX/VNNI/AVX-512 optimizations to boost throughput. The workflow includes benchmarking against FP32 baselines with PyTorch/Transformers and IPEX, outlining a concrete path to production-grade deployments on Intel hardware. Expect potential small trade-offs in accuracy due to quantization and ensure careful model artifact management (optimum_model_path) during migration.