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Update documentation and some miscellaneous things (#96)
* Fix documentation and some miscellaneous things (#13)
* Re-formatting + more badges
* Update and fix docs
* Make error msg accurate
* handle lack of flash-attn flag better
* Document more hydra features
* update example exp configs to match latest supported metrics
* Change HF logo
* Simplify eval exp cfg dump
* testing push workflows
* Add workflow test branch
* update workflow path again
* Reformat badges to fix blue line issue
* Fix div
* revert change to tests build path
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Co-authored-by: Dornavineeth <[email protected]>
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### 📢 Updates
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#### [Apr 6, 2025]
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⚠️⚠️**IMPORTANT:** Be sure to run `python setup_data.py` immediately after merging the latest version. This is required to refresh the downloaded eval log files and ensure they're compatible with the latest evaluation metrics.
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🚨🚨**IMPORTANT:** 🚨🚨 Be sure to run `python setup_data.py` immediately after merging the latest version. This is required to refresh the downloaded eval log files and ensure they're compatible with the latest evaluation metrics.
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-**More Metrics!** Added 6 Membership Inference Attacks (MIA) (LOSS, ZLib, Reference, GradNorm, MinK, and MinK++), along with Extraction Strength (ES) and Exact Memorization (EM) as additional evaluation metrics.
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-**More TOFU Evaluations!** Now includes a holdout set and supports MIA attack-based evaluation. You can now compute MUSE's privleak on TOFU.
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-**More Documentation!**[`docs/links.md`](docs/links.md) contains resources for each of the implemented features and other useful LLM unlearning resources.
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python setup_data.py # saves/eval now contains evaluation results of the uploaded models
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# Downloads log files with metric eval results (incl retain model logs) from the models
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# used in the supported benchmarks.
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|[`docs/contributing.md`](docs/contributing.md)| Instructions on how to add new methods, benchmarks, components such as trainers, benchmarks, metrics, models, datasets, etc. |
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|[`docs/evaluation.md`](docs/evaluation.md)| Detailed instructions on creating and running evaluation metrics and benchmarks. |
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|[`docs/experiments.md`](docs/experiments.md)| Guide on running experiments in various configurations and settings, including distributed training, fine-tuning, and overriding arguments. |
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|[`docs/hydra.md`](docs/hydra.md)|Explanation of the Hydra features used in configuration management for experiments. |
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|[`docs/hydra.md`](docs/hydra.md)|A short tutorial on Hydra features, Hydra is the configuration management package we use extensively. |
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|[`community/leaderboard.md`](community/leaderboard.md)| Reference results from various unlearning methods run using this framework on TOFU and MUSE benchmarks. |
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|[`docs/links.md`](docs/links.md)| List of all links to the research papers or other sources the implemented features are sourced from. |
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|[`docs/repro.md`](docs/repro.md)| Results are provided solely for reproducibility purposes, without any parameter tuning. |
@@ -193,26 +190,25 @@ If you use OpenUnlearning in your research, please cite OpenUnlearning and the b
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```bibtex
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@misc{openunlearning2025,
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title={OpenUnlearning: A Unified Framework for LLM Unlearning Benchmarks},
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title={{OpenUnlearning}: A Unified Framework for LLM Unlearning Benchmarks},
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author={Dorna, Vineeth and Mekala, Anmol and Zhao, Wenlong and McCallum, Andrew and Kolter, J Zico and Maini, Pratyush},
title={TOFU: A Task of Fictitious Unlearning for LLMs},
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title={{TOFU}: A Task of Fictitious Unlearning for LLMs},
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author={Maini, Pratyush and Feng, Zhili and Schwarzschild, Avi and Lipton, Zachary Chase and Kolter, J Zico},
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booktitle={First Conference on Language Modeling},
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year={2024}
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}
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@article{shi2024muse,
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title={MUSE: Machine Unlearning Six-Way Evaluation for Language Models},
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@inproceedings{
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shi2025muse,
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title={{MUSE}: Machine Unlearning Six-Way Evaluation for Language Models},
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author={Weijia Shi and Jaechan Lee and Yangsibo Huang and Sadhika Malladi and Jieyu Zhao and Ari Holtzman and Daogao Liu and Luke Zettlemoyer and Noah A. Smith and Chiyuan Zhang},
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year={2024},
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eprint={2407.06460},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2407.06460},
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booktitle={The Thirteenth International Conference on Learning Representations},
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year={2025},
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url={https://openreview.net/forum?id=TArmA033BU}
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}
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```
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</details>
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---
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### Star History
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[](https://www.star-history.com/#locuslab/open-unlearning&Date)
Copy file name to clipboardExpand all lines: community/benchmarks/template/README.md
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-[ ]**Hyperparameters & Search Space:** Specify key hyperparameters, their search ranges, number of trials etc.
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-[ ]**Computational Setup:** Mention the type and number of GPUs used.
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-[ ]**DeepSpeed Configuration:** If any modifications were made to the default DeepSpeed config, specify them here. (You may include the config as a code block.)
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-[ ]**DeepSpeed Configuration** (if used): If any modifications were made to the default DeepSpeed config, specify them here. (You may include the config as a code block.)
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-[ ]**Other Details:** Any additional setup details crucial for reproducing your method.
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To replicate your results, provide a `run.sh` script that contains all necessary commands to reproduce the final results. Ensure the script is well-documented.
Copy file name to clipboardExpand all lines: community/methods/template/README.md
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-[ ]**Hyperparameters & Search Space:** Specify key hyperparameters, their search ranges, number of trials etc.
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-[ ]**Computational Setup:** Mention the type and number of GPUs used.
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-[ ]**DeepSpeed Configuration:** If any modifications were made to the default DeepSpeed config, specify them here. (You may include the config as a code block.)
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-[ ]**DeepSpeed Configuration** (if used): If any modifications were made to the default DeepSpeed config, specify them here. (You may include the config as a code block.)
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-[ ]**Other Details:** Any additional setup details crucial for reproducing your method.
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