aimo-3-yemao-solution

AIMO 3 Local Submission

This repository contains yemaoye_submission.ipynb, a local-runtime version of my Kaggle AIMO 3 competition notebook.

The core inference logic is kept consistent with my final Kaggle submission. Kaggle-specific input paths have been replaced with local paths relative to this repository.

Folder layout

Model path

The notebook uses the following generic model path:

MODEL_PATH = BASE_DIR / "model"

This repository does not duplicate the full GPT-OSS-120B weights because of their large size.

Before running, point model/ to the GPT-OSS-120B weights, for example:

• Symlink / junction: model → local GPT-OSS-120B directory
• Copy or rename: put the model files under model/
• Or modify MODEL_PATH inside the notebook

Running locally

1. Set your Jupyter working directory to this repository.
2. Make sure wheels.tar.gz is in the repository root.
3. Link or copy GPT-OSS-120B weights to model/.
4. Optionally add test.csv for local gateway testing.
5. Run all cells in yemaoye_submission.ipynb.

The notebook is intended for a Linux GPU environment similar to Kaggle, with CUDA, vLLM, tar, and the required offline wheels.

Notes

• The full GPT-OSS-120B model weights are not included here.
• setup/ and setup/tiktoken_encodings/ are generated during environment setup.

Acknowledgements

My solution is largely built upon the excellent public baseline notebook shared by Andreas Bisiadis. I sincerely thank Andreas Bisiadis and other open-source contributors for their generous sharing and strong engineering work.

I also thank Kaggle and the competition host team for organizing the AI Mathematical Olympiad - Progress Prize 3 competition.