How we sped up transformer inference 100x for 🤗 API customers

What changed

While experimenting with models in 🤗 Transformers is easy, deploying these large models into production with maximum performance, and managing them into an architecture that scales with usage is a hard engineering challenge for any Machine Learning Engineer. This 100x performance gain and built-in scalability is why subscribers of our hosted Accelerated Inference API chose to build their NLP features on top of it. As new releases of Transformers and Tokenizers typically ship every month, our API customers do not need to constantly adapt to new optimization opportunities, their models just keep running faster.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• Train your first Decision Transformer
• Differential Transformer V2
• Patch Time Series Transformer in Hugging Face

• Train your first Decision Transformer similarity 0.19
• Differential Transformer V2 similarity 0.17
• Patch Time Series Transformer in Hugging Face similarity 0.17