Using Scikit-LLM with Open-Source LLMs

A new tutorial explains how to utilize locally hosted language models via Ollama for text classification tasks without any cost related to API calls. This guide focuses on integrating manageable size large language models, such as Mistral, Gemma, and Llama 3, using the Scikit-LLM Python library.

The article begins by recommending the installation of Ollama and suggests using an integrated development environment (IDE) to facilitate the tutorial. It provides instructions for downloading a local LLM after setting up Ollama. Users can interact with the model in the terminal, maintaining its operation in the background for API calls.

The tutorial emphasizes the need to install specific libraries and handle potential errors that may arise during Python code execution. It guides users through filling in their Python code, starting with necessary imports including the ZeroShotGPTClassifier class to implement zero-shot classification using Ollama's models.

Additionally, users prepare a small dataset of user reviews and their corresponding categories for classification. The tutorial outlines steps for initializing and running the classifier, showcasing how Scikit-LLM can be linked with an Ollama model for effective task performance.

Finally, it highlights printing output results from model predictions on test examples, demonstrating the ease of using free, locally run models in a traditional machine learning setup.