Transformers backend integration in SGLang
Hugging Face transformers library is the standard for working with state-of-the-art models — from experimenting with cutting-edge research to fine-tuning on custom data. Its simplicity, flexibility, and expansive model zoo make it a powerful tool for rapid development.
But once you’re ready to move from notebooks to production, inference performance becomes mission-critical. That’s where SGLang comes in.
Designed for high-throughput, low-latency inference, SGLang now offers seamless integration with transformers as a backend. This means you can pair the flexibility of transformers with the raw performance of SGLang.
Let’s dive into what this integration enables and how you can