A library for automatic data selection in active fine-tuning of large neural networks.
Please cite our work if you use this library in your research (bibtex below):
- Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
- Transductive Active Learning: Theory and Applications (Section 4)
pip install activeft from activeft.sift import Retriever # Load embeddings embeddings = np.random.rand(1000, 512) query_embeddings = np.random.rand(1, 512) index = faiss.IndexFlatIP(d) index.add(embeddings) retriever = Retriever(index) indices = retriever.search(query_embeddings, N=10)- The code is auto-formatted using
black .. - Static type checks can be run using
pyright. - Tests can be run using
pytest test.
To start a local server hosting the documentation run pdoc ./activeft --math.
- update version number in
pyproject.tomlandactiveft/__init__.py - build:
poetry build - publish:
poetry publish - push version update to GitHub
- create new release on GitHub
@article{hubotter2024efficiently, title = {Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs}, author = {H{\"u}botter, Jonas and Bongni, Sascha and Hakimi, Ido and Krause, Andreas}, year = 2024, journal = {arXiv preprint arXiv:2410.08020} } @inproceedings{hubotter2024transductive, title = {Transductive Active Learning: Theory and Applications}, author = {H{\"u}botter, Jonas and Sukhija, Bhavya and Treven, Lenart and As, Yarden and Krause, Andreas}, year = 2024, booktitle = {Advances in Neural Information Processing Systems} }
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