diff --git a/docs/foundation/paligemma.md b/docs/foundation/paligemma.md
new file mode 100644
index 000000000..94b886405
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+++ b/docs/foundation/paligemma.md
@@ -0,0 +1,140 @@
+PaliGemma is a large multimodal model developed by Google Research.
+
+You can use PaliGemma to:
+
+1. Ask questions about images (Visual Question Answering)
+2. Identify the location of objects in an image (object detection)
+3. Identify the precise location of objects in an imageh (image segmentation)
+
+You can deploy PaliGemma object detection models with Inference, and use PaliGemma for object detection.
+
+### How to Use PaliGemma (VQA)
+
+Create a new Python file called `app.py` and add the following code:
+
+```python
+import inference
+
+from inference.models.paligemma.paligemma import PaliGemma
+
+pg = PaliGemma(api_key="YOUR ROBOFLOW API KEY")
+
+from PIL import Image
+
+image = Image.open("image.jpeg") # Change to your image
+
+prompt = "How many dogs are in this image?"
+
+result = pg.predict(image,prompt)
+```
+
+In this code, we load PaliGemma run PaliGemma on an image, and annotate the image with the predictions from the model.
+
+Above, replace:
+
+1. `prompt` with the prompt for the model.
+2. `image.jpeg` with the path to the image in which you want to detect objects.
+
+To use PaliGemma with Inference, you will need a Roboflow API key. If you don't already have a Roboflow account, sign up for a free Roboflow account.
+
+Then, run the Python script you have created:
+
+```
+python app.py
+```
+
+The result from your model will be printed to the console.
+
+### How to Use PaliGemma (Object Detection)
+
+Create a new Python file called `app.py` and add the following code:
+
+```python
+import os
+import transformers
+import re
+import numpy as np
+import supervision as sv
+from typing import Tuple, List, Optional
+from PIL import Image
+
+image = Image.open("/content/data/dog.jpeg")
+
+def from_pali_gemma(response: str, resolution_wh: Tuple[int, int], class_list: Optional[List[str]] = None) -> sv.Detections:
+ _SEGMENT_DETECT_RE = re.compile(
+ r'(.*?)' +
+ r'' * 4 + r'\s*' +
+ '(?:%s)?' % (r'' * 16) +
+ r'\s*([^;<>]+)? ?(?:; )?',
+ )
+
+ width, height = resolution_wh
+ xyxy_list = []
+ class_name_list = []
+
+ while response:
+ m = _SEGMENT_DETECT_RE.match(response)
+ if not m:
+ break
+
+ gs = list(m.groups())
+ before = gs.pop(0)
+ name = gs.pop()
+ y1, x1, y2, x2 = [int(x) / 1024 for x in gs[:4]]
+ y1, x1, y2, x2 = map(round, (y1*height, x1*width, y2*height, x2*width))
+
+ content = m.group()
+ if before:
+ response = response[len(before):]
+ content = content[len(before):]
+
+ xyxy_list.append([x1, y1, x2, y2])
+ class_name_list.append(name.strip())
+ response = response[len(content):]
+
+ xyxy = np.array(xyxy_list)
+ class_name = np.array(class_name_list)
+
+ if class_list is None:
+ class_id = None
+ else:
+ class_id = np.array([class_list.index(name) for name in class_name])
+
+ return sv.Detections(
+ xyxy=xyxy,
+ class_id=class_id,
+ data={'class_name': class_name}
+ )
+
+prompt = "detect person; car; backpack"
+response = pali_gemma.predict(image, prompt)[0]
+print(response)
+
+detections = from_pali_gemma(response=response, resolution_wh=image.size, class_list=['person', 'car', 'backpack'])
+
+bounding_box_annotator = sv.BoundingBoxAnnotator()
+label_annotator = sv.LabelAnnotator()
+
+annotatrd_image = bounding_box_annotator.annotate(image, detections)
+annotatrd_image = label_annotator.annotate(annotatrd_image, detections)
+sv.plot_image(annotatrd_image)
+```
+
+In this code, we load PaliGemma run PaliGemma on an image, and annotate the image with the predictions from the model.
+
+Above, replace:
+
+1. `prompt` with the prompt for the model.
+2. `image.jpeg` with the path to the image in which you want to detect objects.
+
+To use PaliGemma with Inference, you will need a Roboflow API key. If you don't already have a Roboflow account, sign up for a free Roboflow account.
+
+Then, run the Python script you have created:
+
+```
+python app.py
+```
+
+The result from the model will be displayed:
+
+
diff --git a/docs/quickstart/licensing.md b/docs/quickstart/licensing.md
index 7578f4723..accf9d4f6 100644
--- a/docs/quickstart/licensing.md
+++ b/docs/quickstart/licensing.md
@@ -1,24 +1,3 @@
## Inference Source Code License
-The Roboflow Inference code is distributed under an Apache 2.0 license.
-
-## Using Models Hosted on Roboflow
-
-To use a model hosted on Roboflow for commercial purposes, you need a Roboflow Enterprise license.
-
-Contact the Roboflow sales team to inquire about an enterprise license.
-
-## Model Code Licenses
-
-The models supported by Roboflow Inference have their own licenses. View the licenses for supported models below.
-
-| model | license |
-| :------------------------ | :-----------------------------------------------------------------------------------: |
-| `inference/models/clip` | MIT |
-|`inference/models/gaze` | MIT, Apache 2.0 |
-| `inference/models/sam` | Apache 2.0 |
-| `inference/models/vit` | Apache 2.0 |
-| `inference/models/yolact` | MIT |
-| `inference/models/yolov5` | AGPL-3.0 |
-| `inference/models/yolov7` | GPL-3.0 |
-| `inference/models/yolov8` | AGPL-3.0 |
\ No newline at end of file
+See [Roboflow Licensing](https://inference.roboflow.com/quickstart/licensing/) for information on how Inference and models supported in Inference are licensed.
diff --git a/mkdocs.yml b/mkdocs.yml
index 1442529db..f1508f483 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -43,27 +43,27 @@ nav:
- YOLOv7: fine-tuned/yolov7.md
- YOLOv5: fine-tuned/yolov5.md
- YOLO-NAS: fine-tuned/yolonas.md
+ - Foundation Model:
+ - What is a Foundation Model?: foundation/about.md
+ - CLIP (Classification, Embeddings): foundation/clip.md
+ - CogVLM (Multimodal Language Model): foundation/cogvlm.md
+ - DocTR (OCR): foundation/doctr.md
+ - Grounding DINO (Object Detection): foundation/grounding_dino.md
+ - L2CS-Net (Gaze Detection): foundation/gaze.md
+ - PaliGemma: foundation/paligemma.md
+ - Segment Anything (Segmentation): foundation/sam.md
+ - YOLO-World (Object Detection): foundation/yolo_world.md
- Run a Model:
- Predict on an Image Over HTTP: quickstart/run_model_on_image.md
- Predict on a Video, Webcam or RTSP Stream: quickstart/run_model_on_rtsp_webcam.md
- Predict Over UDP: quickstart/run_model_over_udp.md
- Keypoint Detection: quickstart/run_keypoint_detection.md
-
- Deploy a Model:
- Configure Your Deployment: https://roboflow.github.io/deploy-setup-widget/results.html
- How Do I Run Inference?: quickstart/inference_101.md
- What Devices Can I Use?: quickstart/devices.md
- Retrieve Your API Key: quickstart/configure_api_key.md
- - Model Licenses: quickstart/licensing.md
- - Foundation Model:
- - What is a Foundation Model?: foundation/about.md
- - CLIP (Classification, Embeddings): foundation/clip.md
- - CogVLM (Multimodal Language Model): foundation/cogvlm.md
- - DocTR (OCR): foundation/doctr.md
- - Grounding DINO (Object Detection): foundation/grounding_dino.md
- - L2CS-Net (Gaze Detection): foundation/gaze.md
- - Segment Anything (Segmentation): foundation/sam.md
- - YOLO-World (Object Detection): foundation/yolo_world.md
+ - Model Licenses: https://roboflow.com/licensing
- Workflows:
- What is a Workflow?: workflows/about.md
- Understanding Workflows: workflows/understanding.md