diff --git a/docs/my-website/docs/fine_tuning.md b/docs/my-website/docs/fine_tuning.md
index fd3cbc792dc..657377aa815 100644
--- a/docs/my-website/docs/fine_tuning.md
+++ b/docs/my-website/docs/fine_tuning.md
@@ -110,58 +110,6 @@ curl http://localhost:4000/v1/fine_tuning/jobs \
-
-
-
-
-
-```python
-ft_job = await client.fine_tuning.jobs.create(
- model="gemini-1.0-pro-002", # Vertex model you want to fine-tune
- training_file="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl", # file_id from create file response
- extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm proxy which provider to use
-)
-```
-
-
-
-
-```shell
-curl http://localhost:4000/v1/fine_tuning/jobs \
- -H "Content-Type: application/json" \
- -H "Authorization: Bearer sk-1234" \
- -d '{
- "custom_llm_provider": "vertex_ai",
- "model": "gemini-1.0-pro-002",
- "training_file": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl"
- }'
-```
-
-
-
-
-:::info
-
-Use this to create Fine tuning Jobs in [the Vertex AI API Format](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#create-tuning)
-
-:::
-
-```shell
-curl http://localhost:4000/v1/projects/tuningJobs \
- -H "Content-Type: application/json" \
- -H "Authorization: Bearer sk-1234" \
- -d '{
- "baseModel": "gemini-1.0-pro-002",
- "supervisedTuningSpec" : {
- "training_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl"
- }
-}'
-```
-
-
-
-
-
### Request Body
diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md
index 10329b15a4b..aa551d711c3 100644
--- a/docs/my-website/docs/providers/vertex.md
+++ b/docs/my-website/docs/providers/vertex.md
@@ -4,6 +4,7 @@ import TabItem from '@theme/TabItem';
# VertexAI [Anthropic, Gemini, Model Garden]
+## Overview
| Property | Details |
|-------|-------|
@@ -11,6 +12,8 @@ import TabItem from '@theme/TabItem';
| Provider Route on LiteLLM | `vertex_ai/` |
| Link to Provider Doc | [Vertex AI ↗](https://cloud.google.com/vertex-ai) |
| Base URL | [https://{vertex_location}-aiplatform.googleapis.com/](https://{vertex_location}-aiplatform.googleapis.com/) |
+| Supported Operations | [`/chat/completions`](#sample-usage), `/completions`, [`/embeddings`](#embedding-models), [`/audio/speech`](#text-to-speech-apis), [`/fine_tuning`](#fine-tuning-apis), [`/batches`](#batch-apis), [`/files`](#batch-apis), [`/images`](#image-generation-models) |
+
@@ -2500,6 +2503,110 @@ create_batch_response = oai_client.batches.create(
}
```
+## **Fine Tuning APIs**
+
+
+| Property | Details |
+|----------|---------|
+| Description | Create Fine Tuning Jobs in Vertex AI (`/tuningJobs`) using OpenAI Python SDK |
+| Vertex Fine Tuning Documentation | [Vertex Fine Tuning](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#create-tuning) |
+
+### Usage
+
+#### 1. Add `finetune_settings` to your config.yaml
+```yaml
+model_list:
+ - model_name: gpt-4
+ litellm_params:
+ model: openai/fake
+ api_key: fake-key
+ api_base: https://exampleopenaiendpoint-production.up.railway.app/
+
+# 👇 Key change: For /fine_tuning/jobs endpoints
+finetune_settings:
+ - custom_llm_provider: "vertex_ai"
+ vertex_project: "adroit-crow-413218"
+ vertex_location: "us-central1"
+ vertex_credentials: "/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json"
+```
+
+#### 2. Create a Fine Tuning Job
+
+
+
+
+```python
+ft_job = await client.fine_tuning.jobs.create(
+ model="gemini-1.0-pro-002", # Vertex model you want to fine-tune
+ training_file="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl", # file_id from create file response
+ extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm proxy which provider to use
+)
+```
+
+
+
+
+```shell
+curl http://localhost:4000/v1/fine_tuning/jobs \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer sk-1234" \
+ -d '{
+ "custom_llm_provider": "vertex_ai",
+ "model": "gemini-1.0-pro-002",
+ "training_file": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl"
+ }'
+```
+
+
+
+
+
+**Advanced use case - Passing `adapter_size` to the Vertex AI API**
+
+Set hyper_parameters, such as `n_epochs`, `learning_rate_multiplier` and `adapter_size`. [See Vertex Advanced Hyperparameters](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#advanced_use_case)
+
+
+
+
+
+```python
+
+ft_job = client.fine_tuning.jobs.create(
+ model="gemini-1.0-pro-002", # Vertex model you want to fine-tune
+ training_file="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl", # file_id from create file response
+ hyperparameters={
+ "n_epochs": 3, # epoch_count on Vertex
+ "learning_rate_multiplier": 0.1, # learning_rate_multiplier on Vertex
+ "adapter_size": "ADAPTER_SIZE_ONE" # type: ignore, vertex specific hyperparameter
+ },
+ extra_body={
+ "custom_llm_provider": "vertex_ai",
+ },
+)
+```
+
+
+
+
+```shell
+curl http://localhost:4000/v1/fine_tuning/jobs \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer sk-1234" \
+ -d '{
+ "custom_llm_provider": "vertex_ai",
+ "model": "gemini-1.0-pro-002",
+ "training_file": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
+ "hyperparameters": {
+ "n_epochs": 3,
+ "learning_rate_multiplier": 0.1,
+ "adapter_size": "ADAPTER_SIZE_ONE"
+ }
+ }'
+```
+
+
+
+
## Extra