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[Docs] vertex deepseek (#12345)

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* docs vertex

* Vertex AI - Anthropic (Claude)
This commit is contained in:
Ishaan Jaff
2025-07-05 11:14:41 -07:00
committed by GitHub
parent 9991a813e2
commit 6ce8f5692e
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docs/my-website/docs/providers/vertex.md
+1 -642
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@@ -2,7 +2,7 @@ import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# VertexAI [Anthropic, Gemini, Model Garden]
# VertexAI [Gemini]
## Overview
@@ -1208,534 +1208,6 @@ os.environ["VERTEXAI_LOCATION"] = "us-central1 # Your Location
# set directly on module
litellm.vertex_location = "us-central1 # Your Location
```
## Anthropic
| Model Name | Function Call |
|------------------|--------------------------------------|
| claude-3-opus@20240229 | `completion('vertex_ai/claude-3-opus@20240229', messages)` |
| claude-3-5-sonnet@20240620 | `completion('vertex_ai/claude-3-5-sonnet@20240620', messages)` |
| claude-3-sonnet@20240229 | `completion('vertex_ai/claude-3-sonnet@20240229', messages)` |
| claude-3-haiku@20240307 | `completion('vertex_ai/claude-3-haiku@20240307', messages)` |
| claude-3-7-sonnet@20250219 | `completion('vertex_ai/claude-3-7-sonnet@20250219', messages)` |
### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
model = "claude-3-sonnet@20240229"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = completion(
model="vertex_ai/" + model,
messages=[{"role": "user", "content": "hi"}],
temperature=0.7,
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: anthropic-vertex
litellm_params:
model: vertex_ai/claude-3-sonnet@20240229
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: anthropic-vertex
litellm_params:
model: vertex_ai/claude-3-sonnet@20240229
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "anthropic-vertex", # 👈 the 'model_name' in config
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
### Usage - `thinking` / `reasoning_content`
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
resp = completion(
model="vertex_ai/claude-3-7-sonnet-20250219",
messages=[{"role": "user", "content": "What is the capital of France?"}],
thinking={"type": "enabled", "budget_tokens": 1024},
)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
- model_name: claude-3-7-sonnet-20250219
litellm_params:
model: vertex_ai/claude-3-7-sonnet-20250219
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
2. Start proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
curl http://0.0.0.0:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
-d '{
"model": "claude-3-7-sonnet-20250219",
"messages": [{"role": "user", "content": "What is the capital of France?"}],
"thinking": {"type": "enabled", "budget_tokens": 1024}
}'
```
</TabItem>
</Tabs>
**Expected Response**
```python
ModelResponse(
id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
created=1740470510,
model='claude-3-7-sonnet-20250219',
object='chat.completion',
system_fingerprint=None,
choices=[
Choices(
finish_reason='stop',
index=0,
message=Message(
content="The capital of France is Paris.",
role='assistant',
tool_calls=None,
function_call=None,
provider_specific_fields={
'citations': None,
'thinking_blocks': [
{
'type': 'thinking',
'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
'signature': 'EuYBCkQYAiJAy6...'
}
]
}
),
thinking_blocks=[
{
'type': 'thinking',
'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
'signature': 'EuYBCkQYAiJAy6AGB...'
}
],
reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
)
],
usage=Usage(
completion_tokens=68,
prompt_tokens=42,
total_tokens=110,
completion_tokens_details=None,
prompt_tokens_details=PromptTokensDetailsWrapper(
audio_tokens=None,
cached_tokens=0,
text_tokens=None,
image_tokens=None
),
cache_creation_input_tokens=0,
cache_read_input_tokens=0
)
)
```
## Meta/Llama API
| Model Name | Function Call |
|------------------|--------------------------------------|
| meta/llama-3.2-90b-vision-instruct-maas | `completion('vertex_ai/meta/llama-3.2-90b-vision-instruct-maas', messages)` |
| meta/llama3-8b-instruct-maas | `completion('vertex_ai/meta/llama3-8b-instruct-maas', messages)` |
| meta/llama3-70b-instruct-maas | `completion('vertex_ai/meta/llama3-70b-instruct-maas', messages)` |
| meta/llama3-405b-instruct-maas | `completion('vertex_ai/meta/llama3-405b-instruct-maas', messages)` |
| meta/llama-4-scout-17b-16e-instruct-maas | `completion('vertex_ai/meta/llama-4-scout-17b-16e-instruct-maas', messages)` |
| meta/llama-4-scout-17-128e-instruct-maas | `completion('vertex_ai/meta/llama-4-scout-128b-16e-instruct-maas', messages)` |
| meta/llama-4-maverick-17b-128e-instruct-maas | `completion('vertex_ai/meta/llama-4-maverick-17b-128e-instruct-maas',messages)` |
| meta/llama-4-maverick-17b-16e-instruct-maas | `completion('vertex_ai/meta/llama-4-maverick-17b-16e-instruct-maas',messages)` |
### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
model = "meta/llama3-405b-instruct-maas"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = completion(
model="vertex_ai/" + model,
messages=[{"role": "user", "content": "hi"}],
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: anthropic-llama
litellm_params:
model: vertex_ai/meta/llama3-405b-instruct-maas
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: anthropic-llama
litellm_params:
model: vertex_ai/meta/llama3-405b-instruct-maas
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "anthropic-llama", # 👈 the 'model_name' in config
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
## Mistral API
[**Supported OpenAI Params**](https://github.com/BerriAI/litellm/blob/e0f3cd580cb85066f7d36241a03c30aa50a8a31d/litellm/llms/openai.py#L137)
| Model Name | Function Call |
|------------------|--------------------------------------|
| mistral-large@latest | `completion('vertex_ai/mistral-large@latest', messages)` |
| mistral-large@2407 | `completion('vertex_ai/mistral-large@2407', messages)` |
| mistral-nemo@latest | `completion('vertex_ai/mistral-nemo@latest', messages)` |
| codestral@latest | `completion('vertex_ai/codestral@latest', messages)` |
| codestral@@2405 | `completion('vertex_ai/codestral@2405', messages)` |
### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
model = "mistral-large@2407"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = completion(
model="vertex_ai/" + model,
messages=[{"role": "user", "content": "hi"}],
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: vertex-mistral
litellm_params:
model: vertex_ai/mistral-large@2407
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: vertex-mistral
litellm_params:
model: vertex_ai/mistral-large@2407
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "vertex-mistral", # 👈 the 'model_name' in config
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
### Usage - Codestral FIM
Call Codestral on VertexAI via the OpenAI [`/v1/completion`](https://platform.openai.com/docs/api-reference/completions/create) endpoint for FIM tasks.
Note: You can also call Codestral via `/chat/completion`.
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
# os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
# OR run `!gcloud auth print-access-token` in your terminal
model = "codestral@2405"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = text_completion(
model="vertex_ai/" + model,
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
prompt="def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():",
suffix="return True", # optional
temperature=0, # optional
top_p=1, # optional
max_tokens=10, # optional
min_tokens=10, # optional
seed=10, # optional
stop=["return"], # optional
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: vertex-codestral
litellm_params:
model: vertex_ai/codestral@2405
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: vertex-codestral
litellm_params:
model: vertex_ai/codestral@2405
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl -X POST 'http://0.0.0.0:4000/completions' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"model": "vertex-codestral", # 👈 the 'model_name' in config
"prompt": "def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():",
"suffix":"return True", # optional
"temperature":0, # optional
"top_p":1, # optional
"max_tokens":10, # optional
"min_tokens":10, # optional
"seed":10, # optional
"stop":["return"], # optional
}'
```
</TabItem>
</Tabs>
## AI21 Models
| Model Name | Function Call |
|------------------|--------------------------------------|
| jamba-1.5-mini@001 | `completion(model='vertex_ai/jamba-1.5-mini@001', messages)` |
| jamba-1.5-large@001 | `completion(model='vertex_ai/jamba-1.5-large@001', messages)` |
### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
model = "meta/jamba-1.5-mini@001"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = completion(
model="vertex_ai/" + model,
messages=[{"role": "user", "content": "hi"}],
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: jamba-1.5-mini
litellm_params:
model: vertex_ai/jamba-1.5-mini@001
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: jamba-1.5-large
litellm_params:
model: vertex_ai/jamba-1.5-large@001
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "jamba-1.5-large",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
## Gemini Pro
| Model Name | Function Call |
@@ -1832,119 +1304,6 @@ curl --location 'https://0.0.0.0:4000/v1/chat/completions' \
</TabItem>
</Tabs>
## Model Garden
:::tip
All OpenAI compatible models from Vertex Model Garden are supported.
:::
#### Using Model Garden
**Almost all Vertex Model Garden models are OpenAI compatible.**
<Tabs>
<TabItem value="openai" label="OpenAI Compatible Models">
| Property | Details |
|----------|---------|
| Provider Route | `vertex_ai/openai/{MODEL_ID}` |
| Vertex Documentation | [SDK for Deploy & OpenAI Chat Completions](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/open-models/get_started_with_model_garden_sdk.ipynb), [Vertex Model Garden](https://cloud.google.com/model-garden?hl=en) |
| Supported Operations | `/chat/completions`, `/embeddings` |
<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/openai/<your-endpoint-id>",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: llama3-1-8b-instruct
litellm_params:
model: vertex_ai/openai/5464397967697903616
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "llama3-1-8b-instruct", # 👈 the 'model_name' in config
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
</TabItem>
<TabItem value="non-openai" label="Non-OpenAI Compatible Models">
```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-endpoint-id>",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
</Tabs>
## Gemini Pro Vision
| Model Name | Function Call |
|------------------|--------------------------------------|
docs/my-website/docs/providers/vertex_partner.md
+670
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@@ -0,0 +1,670 @@
import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Vertex AI - Anthropic, DeepSeek, Model Garden
## Supported Partner Providers
| Provider | LiteLLM Route | Vertex Documentation |
|----------|---------------|---------------|
| Anthropic (Claude) | `vertex_ai/claude-*` | [Vertex AI - Anthropic Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude) |
| DeepSeek | `vertex_ai/deepseek-ai/{MODEL}` | [Vertex AI - DeepSeek Models](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/deepseek) |
| Meta/Llama | `vertex_ai/meta/{MODEL}` | [Vertex AI - Meta Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/llama) |
| Mistral | `vertex_ai/mistral-*` | [Vertex AI - Mistral Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/mistral) |
| AI21 (Jamba) | `vertex_ai/jamba-*` | [Vertex AI - AI21 Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/ai21) |
| Model Garden | `vertex_ai/openai/{MODEL_ID}` or `vertex_ai/{MODEL_ID}` | [Vertex Model Garden](https://cloud.google.com/model-garden?hl=en) |
## Vertex AI - Anthropic (Claude)
| Model Name | Function Call |
|------------------|--------------------------------------|
| claude-3-opus@20240229 | `completion('vertex_ai/claude-3-opus@20240229', messages)` |
| claude-3-5-sonnet@20240620 | `completion('vertex_ai/claude-3-5-sonnet@20240620', messages)` |
| claude-3-sonnet@20240229 | `completion('vertex_ai/claude-3-sonnet@20240229', messages)` |
| claude-3-haiku@20240307 | `completion('vertex_ai/claude-3-haiku@20240307', messages)` |
| claude-3-7-sonnet@20250219 | `completion('vertex_ai/claude-3-7-sonnet@20250219', messages)` |
#### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
model = "claude-3-sonnet@20240229"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = completion(
model="vertex_ai/" + model,
messages=[{"role": "user", "content": "hi"}],
temperature=0.7,
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: anthropic-vertex
litellm_params:
model: vertex_ai/claude-3-sonnet@20240229
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: anthropic-vertex
litellm_params:
model: vertex_ai/claude-3-sonnet@20240229
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "anthropic-vertex", # 👈 the 'model_name' in config
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
#### Usage - `thinking` / `reasoning_content`
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
resp = completion(
model="vertex_ai/claude-3-7-sonnet-20250219",
messages=[{"role": "user", "content": "What is the capital of France?"}],
thinking={"type": "enabled", "budget_tokens": 1024},
)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
- model_name: claude-3-7-sonnet-20250219
litellm_params:
model: vertex_ai/claude-3-7-sonnet-20250219
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
2. Start proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
curl http://0.0.0.0:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
-d '{
"model": "claude-3-7-sonnet-20250219",
"messages": [{"role": "user", "content": "What is the capital of France?"}],
"thinking": {"type": "enabled", "budget_tokens": 1024}
}'
```
</TabItem>
</Tabs>
**Expected Response**
```python
ModelResponse(
id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
created=1740470510,
model='claude-3-7-sonnet-20250219',
object='chat.completion',
system_fingerprint=None,
choices=[
Choices(
finish_reason='stop',
index=0,
message=Message(
content="The capital of France is Paris.",
role='assistant',
tool_calls=None,
function_call=None,
provider_specific_fields={
'citations': None,
'thinking_blocks': [
{
'type': 'thinking',
'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
'signature': 'EuYBCkQYAiJAy6...'
}
]
}
),
thinking_blocks=[
{
'type': 'thinking',
'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
'signature': 'EuYBCkQYAiJAy6AGB...'
}
],
reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
)
],
usage=Usage(
completion_tokens=68,
prompt_tokens=42,
total_tokens=110,
completion_tokens_details=None,
prompt_tokens_details=PromptTokensDetailsWrapper(
audio_tokens=None,
cached_tokens=0,
text_tokens=None,
image_tokens=None
),
cache_creation_input_tokens=0,
cache_read_input_tokens=0
)
)
```
## VertexAI DeepSeek
| Property | Details |
|----------|---------|
| Provider Route | `vertex_ai/deepseek-ai/{MODEL}` |
| Vertex Documentation | [Vertex AI - DeepSeek Models](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/deepseek) |
#### Usage
**LiteLLM Supports all Vertex AI DeepSeek Models.** Ensure you use the `vertex_ai/deepseek-ai/` prefix for all Vertex AI DeepSeek models.
| Model Name | Usage |
|------------------|------------------------------|
| vertex_ai/deepseek-ai/deepseek-r1-0528-maas | `completion('vertex_ai/deepseek-ai/deepseek-r1-0528-maas', messages)` |
## VertexAI Meta/Llama API
| Model Name | Function Call |
|------------------|--------------------------------------|
| meta/llama-3.2-90b-vision-instruct-maas | `completion('vertex_ai/meta/llama-3.2-90b-vision-instruct-maas', messages)` |
| meta/llama3-8b-instruct-maas | `completion('vertex_ai/meta/llama3-8b-instruct-maas', messages)` |
| meta/llama3-70b-instruct-maas | `completion('vertex_ai/meta/llama3-70b-instruct-maas', messages)` |
| meta/llama3-405b-instruct-maas | `completion('vertex_ai/meta/llama3-405b-instruct-maas', messages)` |
| meta/llama-4-scout-17b-16e-instruct-maas | `completion('vertex_ai/meta/llama-4-scout-17b-16e-instruct-maas', messages)` |
| meta/llama-4-scout-17-128e-instruct-maas | `completion('vertex_ai/meta/llama-4-scout-128b-16e-instruct-maas', messages)` |
| meta/llama-4-maverick-17b-128e-instruct-maas | `completion('vertex_ai/meta/llama-4-maverick-17b-128e-instruct-maas',messages)` |
| meta/llama-4-maverick-17b-16e-instruct-maas | `completion('vertex_ai/meta/llama-4-maverick-17b-16e-instruct-maas',messages)` |
#### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
model = "meta/llama3-405b-instruct-maas"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = completion(
model="vertex_ai/" + model,
messages=[{"role": "user", "content": "hi"}],
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: anthropic-llama
litellm_params:
model: vertex_ai/meta/llama3-405b-instruct-maas
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: anthropic-llama
litellm_params:
model: vertex_ai/meta/llama3-405b-instruct-maas
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "anthropic-llama", # 👈 the 'model_name' in config
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
## VertexAI Mistral API
[**Supported OpenAI Params**](https://github.com/BerriAI/litellm/blob/e0f3cd580cb85066f7d36241a03c30aa50a8a31d/litellm/llms/openai.py#L137)
| Model Name | Function Call |
|------------------|--------------------------------------|
| mistral-large@latest | `completion('vertex_ai/mistral-large@latest', messages)` |
| mistral-large@2407 | `completion('vertex_ai/mistral-large@2407', messages)` |
| mistral-nemo@latest | `completion('vertex_ai/mistral-nemo@latest', messages)` |
| codestral@latest | `completion('vertex_ai/codestral@latest', messages)` |
| codestral@@2405 | `completion('vertex_ai/codestral@2405', messages)` |
#### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
model = "mistral-large@2407"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = completion(
model="vertex_ai/" + model,
messages=[{"role": "user", "content": "hi"}],
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: vertex-mistral
litellm_params:
model: vertex_ai/mistral-large@2407
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: vertex-mistral
litellm_params:
model: vertex_ai/mistral-large@2407
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "vertex-mistral", # 👈 the 'model_name' in config
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
#### Usage - Codestral FIM
Call Codestral on VertexAI via the OpenAI [`/v1/completion`](https://platform.openai.com/docs/api-reference/completions/create) endpoint for FIM tasks.
Note: You can also call Codestral via `/chat/completion`.
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
# os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
# OR run `!gcloud auth print-access-token` in your terminal
model = "codestral@2405"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = text_completion(
model="vertex_ai/" + model,
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
prompt="def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():",
suffix="return True", # optional
temperature=0, # optional
top_p=1, # optional
max_tokens=10, # optional
min_tokens=10, # optional
seed=10, # optional
stop=["return"], # optional
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: vertex-codestral
litellm_params:
model: vertex_ai/codestral@2405
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: vertex-codestral
litellm_params:
model: vertex_ai/codestral@2405
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl -X POST 'http://0.0.0.0:4000/completions' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"model": "vertex-codestral", # 👈 the 'model_name' in config
"prompt": "def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():",
"suffix":"return True", # optional
"temperature":0, # optional
"top_p":1, # optional
"max_tokens":10, # optional
"min_tokens":10, # optional
"seed":10, # optional
"stop":["return"], # optional
}'
```
</TabItem>
</Tabs>
## VertexAI AI21 Models
| Model Name | Function Call |
|------------------|--------------------------------------|
| jamba-1.5-mini@001 | `completion(model='vertex_ai/jamba-1.5-mini@001', messages)` |
| jamba-1.5-large@001 | `completion(model='vertex_ai/jamba-1.5-large@001', messages)` |
#### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
model = "meta/jamba-1.5-mini@001"
vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]
response = completion(
model="vertex_ai/" + model,
messages=[{"role": "user", "content": "hi"}],
vertex_ai_project=vertex_ai_project,
vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: jamba-1.5-mini
litellm_params:
model: vertex_ai/jamba-1.5-mini@001
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
- model_name: jamba-1.5-large
litellm_params:
model: vertex_ai/jamba-1.5-large@001
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-west-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "jamba-1.5-large",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
## Model Garden
:::tip
All OpenAI compatible models from Vertex Model Garden are supported.
:::
#### Using Model Garden
**Almost all Vertex Model Garden models are OpenAI compatible.**
<Tabs>
<TabItem value="openai" label="OpenAI Compatible Models">
| Property | Details |
|----------|---------|
| Provider Route | `vertex_ai/openai/{MODEL_ID}` |
| Vertex Documentation | [SDK for Deploy & OpenAI Chat Completions](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/open-models/get_started_with_model_garden_sdk.ipynb), [Vertex Model Garden](https://cloud.google.com/model-garden?hl=en) |
| Supported Operations | `/chat/completions`, `/embeddings` |
<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/openai/<your-endpoint-id>",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: llama3-1-8b-instruct
litellm_params:
model: vertex_ai/openai/5464397967697903616
vertex_ai_project: "my-test-project"
vertex_ai_location: "us-east-1"
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "llama3-1-8b-instruct", # 👈 the 'model_name' in config
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
</TabItem>
<TabItem value="non-openai" label="Non-OpenAI Compatible Models">
```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-endpoint-id>",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
</Tabs>
docs/my-website/release_notes/v1.74.0-stable/index.md
+10 -14
View File
@@ -58,16 +58,21 @@ pip install litellm==1.74.0.post1
This release brings significant performance improvements to the Python SDK with 2.3 seconds faster import times. We've refactored the initialization process to reduce startup overhead, making LiteLLM more efficient for applications that need quick initialization. This is a major improvement for applications that need to initialize LiteLLM quickly.
---
## New Models / Updated Models
### New Providers
### Updated Models
#### Features
- **[🆕 GitHub Copilot](../../docs/providers/github_copilot)** - Use GitHub Copilot API with LiteLLM - [PR](https://github.com/BerriAI/litellm/pull/12325), [Get Started](../../docs/providers/github_copilot)
- **[🆕 VertexAI DeepSeek](../../docs/providers/vertex)** - Add support for VertexAI DeepSeek models - [PR](https://github.com/BerriAI/litellm/pull/12312), [Get Started](../../docs/providers/vertex)
- **[Azure AI](../../docs/providers/azure_ai)**
- Add azure_ai cohere rerank v3.5 - [PR](https://github.com/BerriAI/litellm/pull/12283)
- **[Vertex AI](../../docs/providers/vertex)**
- Add size parameter support for image generation - [PR](https://github.com/BerriAI/litellm/pull/12292)
- **[Custom LLM](../../docs/providers/custom_llm_server)**
- Pass through extra_ properties on "custom" llm provider - [PR](https://github.com/BerriAI/litellm/pull/12185)
#### Bugs
- **[Mistral](../../docs/providers/mistral)**
- Fix transform_response handling for empty string content - [PR](https://github.com/BerriAI/litellm/pull/12202)
@@ -87,15 +92,6 @@ This release brings significant performance improvements to the Python SDK with
- **[Hugging Face](../../docs/providers/huggingface)**
- Fix Hugging Face tests - [PR](https://github.com/BerriAI/litellm/pull/12286)
#### Features
- **[🆕 GitHub Copilot](../../docs/providers/github_copilot)** - Use GitHub Copilot API with LiteLLM
- **[🆕 VertexAI DeepSeek](../../docs/providers/vertex)** - Add support for VertexAI DeepSeek models - [PR](https://github.com/BerriAI/litellm/pull/12312)
- **[Azure AI](../../docs/providers/azure_ai)**
- Add azure_ai cohere rerank v3.5 - [PR](https://github.com/BerriAI/litellm/pull/12283)
- **[Vertex AI](../../docs/providers/vertex)**
- Add size parameter support for image generation - [PR](https://github.com/BerriAI/litellm/pull/12292)
- **[Custom LLM](../../docs/providers/custom_llm_server)**
- Pass through extra_ properties on "custom" llm provider - [PR](https://github.com/BerriAI/litellm/pull/12185)
---
docs/my-website/sidebars.js
+1
View File
@@ -360,6 +360,7 @@ const sidebars = {
label: "Vertex AI",
items: [
"providers/vertex",
"providers/vertex_partner",
"providers/vertex_image",
]
},