diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md
index 10ac13ecaf0..b8633adc5e4 100644
--- a/docs/my-website/docs/providers/vertex.md
+++ b/docs/my-website/docs/providers/vertex.md
@@ -1369,6 +1369,71 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
+## Gemini Pro
+| Model Name | Function Call |
+|------------------|--------------------------------------|
+| gemini-pro | `completion('gemini-pro', messages)`, `completion('vertex_ai/gemini-pro', messages)` |
+
+## Fine-tuned Models
+
+Call fine-tuned Vertex AI Gemini models through LiteLLM. If you want to use LiteLLM to call a model in the `/gemini` request/response format, you can do so by setting `model="vertex_ai/gemini/{MODEL_ID}"`. This tells litellm that the request/response format follows the `gemini` model family format.
+
+| Property | Details |
+|----------|---------|
+| Provider Route | `vertex_ai/gemini/{MODEL_ID}` |
+| Vertex Documentation | [Vertex AI - Fine-tuned Gemini Models](https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini-use-supervised-tuning#test_the_tuned_model_with_a_prompt)|
+| Supported Operations | `/chat/completions`, `/completions`, `/embeddings`, `/images` |
+
+
+
+
+```python showLineNumbers
+import litellm
+import os
+
+## set ENV variables
+os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811"
+os.environ["VERTEXAI_LOCATION"] = "us-central1"
+
+response = litellm.completion(
+ model="vertex_ai/gemini/", # e.g. vertex_ai/4965075652664360960
+ messages=[{ "content": "Hello, how are you?","role": "user"}],
+)
+```
+
+
+
+
+1. Add Vertex Credentials to your env
+
+```bash
+!gcloud auth application-default login
+```
+
+2. Setup config.yaml
+
+```yaml
+- model_name: finetuned-gemini
+ litellm_params:
+ model: vertex_ai/gemini/
+ vertex_project:
+ vertex_location:
+```
+
+3. Test it!
+
+```bash
+curl --location 'https://0.0.0.0:4000/v1/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: ' \
+--data '{"model": "finetuned-gemini" ,"messages":[{"role": "user", "content":[{"type": "text", "text": "hi"}]}]}'
+```
+
+
+
+
+
+
## Model Garden
:::tip
@@ -1479,67 +1544,6 @@ response = completion(
-## Gemini Pro
-| Model Name | Function Call |
-|------------------|--------------------------------------|
-| gemini-pro | `completion('gemini-pro', messages)`, `completion('vertex_ai/gemini-pro', messages)` |
-
-## Fine-tuned Models
-
-Fine tuned models on vertex have a numerical model/endpoint id.
-
-
-
-
-```python
-from litellm import completion
-import os
-
-## set ENV variables
-os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811"
-os.environ["VERTEXAI_LOCATION"] = "us-central1"
-
-response = completion(
- model="vertex_ai/", # e.g. vertex_ai/4965075652664360960
- messages=[{ "content": "Hello, how are you?","role": "user"}],
- base_model="vertex_ai/gemini-1.5-pro" # the base model - used for routing
-)
-```
-
-
-
-
-1. Add Vertex Credentials to your env
-
-```bash
-!gcloud auth application-default login
-```
-
-2. Setup config.yaml
-
-```yaml
-- model_name: finetuned-gemini
- litellm_params:
- model: vertex_ai/
- vertex_project:
- vertex_location:
- model_info:
- base_model: vertex_ai/gemini-1.5-pro # IMPORTANT
-```
-
-3. Test it!
-
-```bash
-curl --location 'https://0.0.0.0:4000/v1/chat/completions' \
---header 'Content-Type: application/json' \
---header 'Authorization: ' \
---data '{"model": "finetuned-gemini" ,"messages":[{"role": "user", "content":[{"type": "text", "text": "hi"}]}]}'
-```
-
-
-
-
-
## Gemini Pro Vision
| Model Name | Function Call |