Scikit-Ollama for Scikit-LLM/Ollama Integration Scikit-ollama, a new library based on scikit-llm, bridges the scikit-learn interface with locally running Ollama models to perform zero-shot text classification without cloud APIs. The library enables users to build a zero-shot sentiment classifier for movie reviews using a local Llama 3 model, addressing data privacy and cost concerns associated with commercial cloud APIs. In this article, you will learn how scikit-ollama bridges the scikit-learn interface with locally running Ollama models to perform zero-shot text classification; no cloud API required. Topics we will cover include: - What scikit-ollama is and how it relates to scikit-llm and the scikit-learn ecosystem. - How to load a movie review sentiment dataset and instantiate a zero-shot classifier backed by a local Llama 3 model. - How the fit/predict pattern works in the context of zero-shot LLM-driven classification, and what it actually does under the hood. Let’s not waste any more time. Introduction Large language model LLM integration into traditional machine learning workflows is not only possible nowadays, but also transforming the way we work with these models, in terms of both cost and security. Relying solely on commercial cloud APIs with quota and traffic bottlenecks — as well as data privacy concerns — is no longer the only go-to approach, and scikit-ollama has a lot to say on this. This library, largely based on scikit-llm, bridges the gap between the friendly scikit-learn syntax used to train and use classical machine learning models, and the power of LLMs — specifically free, locally installed models running on Ollama. This article explores how to set up this integration to build a highly practical zero-shot classifier for sentiment prediction on movie reviews, using a local Llama 3 model running on your machine. Step-by-Step Walkthrough First, since scikit-ollama is only compatible with Python 3.9 or higher, check the Python version currently installed in your local or virtual development environment; mine is a virtual environment set up inside Visual Studio Code: python --version 1 python --version If you have Python 3.8 or lower, make sure you install or switch to a newer Python version before proceeding. Then install scikit-ollama: pip install scikit-ollama 1 pip install scikit-ollama Once installed, we can begin coding. Scikit-LLM provides its own dataset catalog in its datasets module. We will use one of those text-based datasets, specifically one for sentiment classification of movie reviews. This is the code needed to load the data and display an example review alongside its associated sentiment label: python from skllm.datasets import get classification dataset Loading a demo sentiment analysis dataset containing movie reviews The expected labels are: "positive", "negative", "neutral" X, y = get classification dataset print f"Sample text: {X 0 } \nLabel: {y 0 }" 1234567 from skllm.datasets import get classification dataset Loading a demo sentiment analysis dataset containing movie reviews The expected labels are: "positive", "negative", "neutral"X, y = get classification dataset print f"Sample text: {X 0 } \nLabel: {y 0 }" Output: Sample text: I was absolutely blown away by the performances in 'Summer's End'. The acting was top-notch, and the plot had me gripped from start to finish. A truly captivating cinematic experience that I would highly recommend. Label: positive 12 Sample text: I was absolutely blown away by the performances in 'Summer's End'. The acting was top-notch, and the plot had me gripped from start to finish. A truly captivating cinematic experience that I would highly recommend. Label: positive Now for scikit-ollama itself. You will need to have Ollama locally installed on your machine. Follow the instructions in this article to do so, and make sure you install the model you want to use for this guide. To pull a model, run the following command in your terminal: ollama pull