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docs/my-website/docs/providers/vertex.md
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@ -1369,6 +1369,71 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
</Tabs>
## 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` |
<Tabs>
<TabItem value="sdk" label="SDK">
```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/<your-finetuned-model>", # e.g. vertex_ai/4965075652664360960
messages=[{ "content": "Hello, how are you?","role": "user"}],
)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
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/<ENDPOINT_ID>
vertex_project: <PROJECT_ID>
vertex_location: <LOCATION>
```
3. Test it!
```bash
curl --location 'https://0.0.0.0:4000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: <LITELLM_KEY>' \
--data '{"model": "finetuned-gemini" ,"messages":[{"role": "user", "content":[{"type": "text", "text": "hi"}]}]}'
```
</TabItem>
</Tabs>
## Model Garden
:::tip
@ -1479,67 +1544,6 @@ response = completion(
</Tabs>
## 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.
<Tabs>
<TabItem value="sdk" label="SDK">
```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/<your-finetuned-model>", # 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
)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
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/<ENDPOINT_ID>
vertex_project: <PROJECT_ID>
vertex_location: <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: <LITELLM_KEY>' \
--data '{"model": "finetuned-gemini" ,"messages":[{"role": "user", "content":[{"type": "text", "text": "hi"}]}]}'
```
</TabItem>
</Tabs>
## Gemini Pro Vision
| Model Name | Function Call |