Pre-trained models are the backbone of modern NLP. Whether you're summarizing documents, classifying support tickets, or building semantic search, the Hugging Face transformers library gives you access to thousands of models, but wiring up tokenization, device management, and batching correctly takes careful attention to detail.
This post shows how Kiro handles that for you. We'll walk through a real example: building a text summarizer using a pre-trained BART model, then iterating with Kiro to add GPU support and batching, all through natural conversation.
Everything here was tested locally and verified. The output you see below is real — captured from actual script execution on a MacBook with no GPU.
The Task: Summarize Documents with a Pre-Trained Model
I wanted a script that takes paragraphs of text and produces concise summaries. The goal: use the Hugging Face transformers library directly (not the pipeline shortcut) so we have full control over tokenization, device placement, and generation parameters.
