I built Artifex, a Python library to create task-specific LLMs for NLP and Text Classification, without training data. At the moment, only Intent Classification and Guardrail models are supported, but I'll be adding more soon based on users' feedback. The reason I made this is that general-purpose LLMs are overkill for simple Text Classification tasks, and LLM APIs can get expensive quickly. On the other hand, fine-tuning an LLM for a specific task requires labelled data, which not everybody has.

That's why I created a library to train small, task-specific LLMs WITHOUT training data. You simply describe how the model should behave, and it will be trained on synthetic data generated for that purpose.

The models can run locally (without a GPU) or on small servers, offloading simple tasks and reducing reliance on third-party LLM APIs.

I am looking for any kind of feedback or suggestions for new model/tasks. Here is the GitHub repo: https://github.com/tanaos/artifex