Explore Large Language Models with Azure Databricks

Why it matters

A small task-specific model can replace a general model call for a narrow step, but only if you know what its output looks like and where it runs.

What to practise

In an Azure Databricks notebook on serverless compute, load four pipeline objects from the Transformers library, three of them pinned to a model revision: summarization, sentiment analysis, English to French translation, and zero-shot classification. Bound the summary with min_length, max_length and do_sample, and pass candidate_labels to the classifier. Look at the shape of each result, such as a label with a score, because that is what an agent step would consume.

How it fits

2.4.14 is the decision this informs. The exercise does not measure latency or choose hardware, so time each pipeline yourself before you decide where it would run in an agent workflow. Databricks setup is background.