🧠 Mistral Fine-Tune for Math Solver using Unsloth, Gradio & OpenAI API

This project showcases how to fine-tune the powerful Mistral language model using the Unsloth library to build a robust and interactive math problem solver. It combines multiple components to enhance the user experience and ensure high accuracy:

• 🔧 Mistral Fine-Tuning with Unsloth for step-by-step mathematical reasoning.
• 🌐 Gradio Interface for easy, browser-based user interaction.
• 🧠 OpenAI API Integration to:
  • 🖼️ Extract math problems from uploaded images (OCR + interpretation).
  • ✅ Validate and correct Mistral's output if one or more of the generated answers are incorrect.

🚀 Project Overview

Large Language Models (LLMs) are increasingly being used in educational tools, especially for solving math problems. However, base models often struggle with step-by-step mathematical reasoning. In this project, we: 🧠 Technologies Used

• Mistral — Lightweight, high-performance LLM.
• Unsloth — Memory-optimized library for fast fine-tuning with LoRA.
• Gradio — Web-based UI for testing and deployment.
• OpenAI API — Used for image-to-text (problem extraction) and output validation.

Training Flow

You can see my training flow below

Inference Flow

You can see my inference flow below