This repository contains the source code and resources for an LLM-based mathematical problem solver. It combines the generative capabilities of large language models with the discriminative power of classical machine learning techniques.

Project Structure

• inference.py: Contains the main inference pipeline for solving mathematical problems using the two-stage approach.
• qlora_training.py: Script for fine-tuning the base language model using QLoRA (Quantized Low-Rank Adaptation).
• statistical_features_classifier.py: Trains the second-stage logistic regression classifier using aggregated statistics from the QLoRA model's outputs.
• model/: Directory containing the base language model and fine-tuned QLoRA adapters.
• data/: Contains training, validation, and test datasets.

Setup

1. Clone the Repository:

   git clone https:/yourusername/AIMO.git
   cd AIMO
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Download the Base Model:

   Download the base language model (e.g., DeepSeek-Math-7B-RL) and place it in the model/ directory.
   

Running the Project

1. Fine-tune the QLoRA Model:

   python qlora_training.py

2. Train the statistical features classifier:

   python aggregated_statistics_classifier.py

3. Run inference:

   python inference.py