Add SynthID (watermerking by Google DeepMind)

docs/source/en/internal/generation_utils.md

@@ -185,6 +185,9 @@ generation.
 [[autodoc]] SuppressTokensLogitsProcessor
     - __call__
 
+[[autodoc]] SynthIDTextWatermarkLogitsProcessor
+    - __call__
+
 [[autodoc]] TemperatureLogitsWarper
     - __call__
 
@@ -418,5 +421,20 @@ A [`Constraint`] can be used to force the generation to include specific tokens
 
 ## Watermark Utils
 
+[[autodoc]] WatermarkingConfig
+    - __call__
+
 [[autodoc]] WatermarkDetector
     - __call__
+
+[[autodoc]] BayesianDetectorConfig
+    - __call__
+
+[[autodoc]] BayesianDetectorModel
+    - __call__
+
+[[autodoc]] SynthIDTextWatermarkingConfig
+    - __call__
+
+[[autodoc]] SynthIDTextWatermarkDetector
+    - __call__

docs/source/en/main_classes/text_generation.md

@@ -41,8 +41,6 @@ like token streaming.
 	- validate
 	- get_generation_mode
 
-[[autodoc]] generation.WatermarkingConfig
-
 ## GenerationMixin
 
 [[autodoc]] GenerationMixin

examples/research_projects/synthid_text/README.md

@@ -0,0 +1,34 @@
+# SynthID Text
+
+This project showcases the use of SynthIDText for watermarking LLMs. The code shown in this repo also
+demostrates the training of the detector for detecting such watermarked text. This detector can be uploaded onto
+a private HF hub repo (private for security reasons) and can be initialized again through pretrained model loading also shown in this script.
+
+See our blog post: https://huggingface.co/blog/synthid-text
+
+
+## Python version
+
+User would need python 3.9 to run this example.
+
+## Installation and running
+
+Once you install transformers you would need to install requirements for this project through requirements.txt provided in this folder.
+
+```
+pip install -r requirements.txt
+```
+
+## To run the detector training
+
+```
+python detector_training.py --model_name=google/gemma-7b-it
+```
+
+Check the script for more parameters are are tunable and check out paper at link
+https://www.nature.com/articles/s41586-024-08025-4 for more information on these parameters.
+
+## Caveat
+
+Make sure to run the training of the detector and the detection on the same hardware
+CPU, GPU or TPU to get consistent results (we use detecterministic randomness which is hardware dependent).