| --- |
| license: mit |
| tags: |
| - leak-curber |
| - kcat |
| - leaderboard |
| --- |
| # Leak CURBER kcat model starter |
|
|
| Use this starter to try the submission process or build your own model. |
| You train locally and upload a public Hugging Face (HF) model repository. |
| We build its Docker image and run CPU inference. A free HF account is enough. |
|
|
| The included baseline uses ridge regression with sequence composition and SMILES |
| character counts. Its weights use only the fixed training split. |
|
|
| Read the [step-by-step guide](https://huggingface.co/starkAhmed43/leak-curber-kcat-starter/blob/main/GUIDE.md) for the full workflow and troubleshooting. |
|
|
| ## Try the baseline |
|
|
| Download this repository and the public data: |
|
|
| ```bash |
| python -m pip install huggingface-hub==2.2.0 |
| hf download starkAhmed43/leak-curber-kcat-starter --local-dir model |
| hf download starkAhmed43/leak-curber-kcat train.parquet val_inputs.parquet --repo-type dataset --local-dir data |
| cd model |
| python -m pip install -r requirements.txt |
| python predict.py --input example_inputs.parquet --output predictions.csv |
| ``` |
|
|
| The output contains one prediction for each input ID. Compare its format with |
| `example_predictions.csv`. Those example values are model predictions, not labels. |
|
|
| To retrain the baseline, run: |
|
|
| ```bash |
| python train.py --train ../data/train.parquet --output weights/baseline.json |
| ``` |
|
|
| ## Test your Docker image |
|
|
| Install Docker first. From the `model` directory, run: |
|
|
| ```bash |
| mkdir -p output |
| docker build --platform linux/amd64 -t my-kcat-model . |
| docker run --rm --platform linux/amd64 --network none \ |
| -v "$PWD/../data:/input:ro" -v "$PWD/output:/output" \ |
| my-kcat-model python /app/predict.py \ |
| --input /input/val_inputs.parquet --output /output/predictions.csv |
| ``` |
|
|
| Check that `output/predictions.csv` contains every input ID exactly once. |
|
|
| ## Replace the baseline |
|
|
| Use any CPU-compatible model or library. Update `Dockerfile`, `requirements.txt`, |
| and `predict.py` as needed. Include your trained weights and all other assets. |
|
|
| Your image must meet these requirements: |
|
|
| | Item | Requirement | |
| |---|---| |
| | Platform | Linux x86-64 | |
| | Python | `/usr/local/bin/python` | |
| | Prediction script | `/app/predict.py` | |
| | Working directory | `/app` | |
| | Command | `python /app/predict.py --input INPUT --output OUTPUT` | |
| | Input | Parquet columns: `id`, `smiles`, `sequence` | |
| | Output | CSV columns: `id`, `prediction` | |
| | Target | **log10 kcat**, matching training column `log10_value` | |
| | Resources | 2 vCPU, 16 GB RAM, 10 minutes | |
| | Repository size | At most 4 GiB | |
|
|
| Return one finite number per input ID. Row order does not matter. Predictions |
| must have an absolute value no greater than 1,000,000. |
|
|
| Inference runs without internet, credentials, or host files. The evaluator starts |
| Python directly. Put required setup in your build or prediction code. Docker |
| entrypoints and shell startup hooks do not run. Use `/tmp` for temporary files. |
| Write predictions to the supplied output path. |
|
|
| ## Upload and submit |
|
|
| Use a public model repository under your registered HF account: |
|
|
| ```bash |
| hf auth login |
| hf upload YOUR_USERNAME/YOUR_MODEL . --repo-type model --no-private --exclude ".cache/**" "output/**" "predictions.csv" |
| ``` |
|
|
| If the repository already exists, check its visibility on HF. The upload flag |
| does not make an existing private repository public. |
|
|
| Open the [leaderboard](https://huggingface.co/spaces/starkAhmed43/leak-curber-leaderboard). |
| Sign in with your registered account. Enter your repository ID, revision, and model |
| name. The default revision is `main`. Each submission records a fixed commit, so |
| later edits do not change it. |
|
|
| Validation scores appear after evaluation. Use **My submissions** to check progress |
| and nominate a model for test evaluation. Best validation models and nominations |
| stay protected. Other successful archives expire after 7 days. You cannot nominate |
| an expired archive; submit the model again if needed. |
|
|
| For the course, submit and nominate before **November 6, 2026, at 23:59 IST**. |
| Each team has 2 submissions per day and 20 total per task. Only one evaluation per team can |
| be active. If you do not nominate a model, we select your best validation model. |
|
|
| Validation labels and all test data stay private. TAs publish test scores after |
| the deadline. |
|
|
