How I Built an Adaptive Concept Explainer Using Hugging Face Models

Author(s): Kaushik Rajan

Originally published on Towards AI.

Demystifying Complex Ideas Through Multi-Level ExplanationsCredit: Generative AI (ChatGPT 4o)

Have you ever found yourself trying to wrap your head around a complex concept, or having to break down something technical for someone who doesn’t have the same background? I created a free tool to solve this problem. It provides progressively detailed explanations of any concept — from simple, five-year-old friendly descriptions to expert-level technical breakdowns.

In our information-rich world, understanding complex topics can be challenging, especially when explanations are pitched at the wrong level of expertise. Whether you’re a student struggling with quantum physics, a professional trying to grasp machine learning, or simply curious about game theory, the gap between beginner-friendly resources and expert-level content can be frustrating.

I built the concept explainer tool by using a pair of powerful language models from Hugging Face that run the explanation pipeline:

1. Mistral-7B-Instruct-v0.2: This is a cutting-edge 7B scale model that is very effective for complex instruction-following problems and which produces explanatory prose rich in detail. Its ability to maintain coherence throughout lengthy explanations makes it perfect when handling advanced and expert-level content.

2. Falcon-7B-Instruct: High-quality instruction-tuned model that gives very good results for slightly different kinds of explanations. I included it as an alternative so users have options on… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI