Follow one model from the Hugging Face Hub through fine-tuning, evaluation, optimization, and inference. This practical session uses a runnable Colab exercise with DistilBERT and AG News to connect the pieces of a measurable ML workflow: Transformers, PEFT LoRA, Accelerate, and Optimum.

We pin model and dataset revisions, separate training, validation, and test data, then export and quantize to ONNX INT8. We evaluate the exported model and compare quality, per-class recall, latency, and model size. An executable release policy shows both a promoted result and a deliberately blocked regression, making acceptance criteria part of the pipeline.

Attendees leave with slides, source code, a Colab notebook, and a repeatable method for evaluating their own deployment tradeoffs. Familiarity with Python and basic machine learning is helpful.

Delivered to a standing-room-only audience at DeepStation AI Demo Night in Miami on September 17, 2026.

Slides, notebook, source, and recorded results: https://github.com/jemsbhai/bringing-the-heat

Run the Colab: https://colab.research.google.com/github/jemsbhai/bringing-the-heat/blob/main/demo/Bringing_the_Heat_Colab.ipynb

Organizer recap: https://www.linkedin.com/posts/deepstationai_deepstation-is-excited-to-share-the-post-event-activity-7507809905546821632-UT_f