aimo-progress-prize

AI Mathematics Olympic Solutions

CommonProductEducationMathematicsEducation

This GitHub repository contains training and inference code to replicate our winning solution in the AI Mathematics Olympic (AIMO) Progress Prize 1. Our solution consists of four main parts: a recipe for fine-tuning DeepSeekMath-Base 7B for use in solving math problems using Tool Integrated Reasoning (TIR); two high-quality datasets of about 10 million math questions and solutions; an algorithm for generating solution candidates with coding execution feedback (SC-TIR); and four carefully selected validation sets from AMC, AIME, and MATH to guide model selection and avoid overfitting the public leaderboard.

Fine-tune the DeepSeekMath-Base 7B model to solve math problems
Train the model using two high-quality datasets of math questions and solutions
Use the Self-Concordant Decoding algorithm to generate solution candidates
Use validation sets from AMC
AIME
and MATH to guide model selection
Train the model using open-source libraries TRL
PyTorch
vLLM
and DeepSpeed

\The product is suitable for researchers and students in the fields of mathematics and computer science
particularly those interested in the application of AI in solving complex mathematical problems. It can help them understand how to use deep learning models to improve their ability to solve mathematical problems.\

Researchers use the model to improve their ability to solve math competition problems.
Students use the model to learn and understand complex mathematical concepts.
Educators use the model as a teaching aid
helping students master problem-solving skills.

1. Create a Python virtual environment and activate it.
2. Install a specific version of PyTorch to ensure reproducibility.
3. Install other necessary package dependencies.
4. Install Flash Attention 2.
5. Log in to your Hugging Face account.

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