.. image:: ../resources/vLLM-WM-Logo.png :width: 120px :align: center vLLM-Watermark ============================ Tiny. Hackable. Lightning-fast watermarking for researchers built on vLLM .. toctree:: :maxdepth: 1 :caption: Getting started installation .. toctree:: :maxdepth: 1 :caption: Performance evaluation benchmark .. toctree:: :maxdepth: 1 :caption: Watermarking algorithms algorithms/openai algorithms/openai_dr algorithms/maryland algorithms/pf Supported Algorithms -------------------- The following table summarizes the three main watermarking algorithms implemented in this package: .. list-table:: :header-rows: 1 :class: algorithm-comparison * - Algorithm - Description - Paper * - **Gumbel/OpenAI** - Uses Gumbel-Max trick for deterministic sampling - `Aaronson (2023) `_ * - **KGW/Maryland** - Green-red token partitioning with logit bias - `Kirchenbauer et al. (2023) `_ * - **PF (Permute-and-Flip)** - Prefix-free coding with token permutations - `Lean et al. (2024) `_ .. note:: Each algorithm has different trade-offs between detectability, robustness, and text quality. See individual algorithm pages for detailed theory and examples. Quick start ----------- 1. Install the package (see :doc:`installation`) 2. Choose an algorithm from :doc:`algorithms/index` 3. Run the example code to try it locally For detailed API information, refer to the docstrings in the repository code. Citation -------- If you use vLLM-Watermark in your research, please cite: .. raw:: html
.. code-block:: bibtex @software{vllm_watermark, title = {vLLM-Watermark: A tiny, hackable research framework for LLM watermarking experiments}, author = {Apurv Verma}, year = {2025}, url = {https://github.com/dapurv5/vLLM-Watermark} } .. raw:: html